The global cybersecurity landscape continues to evolve at an alarming pace. On 27th April 2026, a newly identified ransomware strain began targeting organizations by encrypting business-critical data and appending a suspicious new file extension: .t9tyig_p2k.

This threat has created immediate concern because, at present, no publicly available decryption tool exists. That means organizations hit by this malware may face severe operational downtime, data inaccessibility, reputational damage, and potential financial losses.

This incident is another reminder that ransomware operators are becoming faster, smarter, and more aggressive. Industries must now move beyond traditional antivirus approaches and adopt proactive cybersecurity strategies.

Understanding the New Ransomware Threat

 Ransomware is malicious software designed to lock or encrypt data, making files unusable until a ransom is paid. The new .t9tyig_p2k variant appears to be using modern encryption methods, making recovery highly difficult without secure backups or specialized incident response.

Why This Attack Is Dangerous

 How Industries Are Being Attacked

✦ Most ransomware campaigns succeed through preventable gaps in security. Common entry points include:

Phishing Emails

Weak Remote Access

Unpatched Systems

Third-Party Vendor Access

Insider Negligence

Warning Signs of Infection

How to Tackle This Attack – ETSPL Strategies

ETSPL recommends a prevention-first model supported by rapid incident response. The following strategies are critical:

1. Immediate Isolation

If ransomware is suspected:

2. Preserve Evidence

Do not format systems or delete files. Preserve logs, ransom notes, memory data, and encrypted samples for forensic analysis.

3. Activate Secure Backups

Maintain:

Regular backup testing is essential.

4. Endpoint Detection & Response (EDR)

Deploy advanced monitoring tools that detect:

5. Multi-Factor Authentication (MFA)

Enable MFA across:

6. Employee Awareness

Train staff to identify:

7. Patch Management

Maintain a disciplined update cycle for:

ETSPL Incident Response Framework

ETSPL supports organizations through a structured ransomware response model:

Detect

Identify patient-zero system, attack timeline, and affected assets.

Contain

Stop spread across endpoints, servers, and cloud systems.

Investigate

Perform digital forensics to determine entry point and attacker behavior.

Recover

Restore operations using clean backups and hardened systems.

Strengthen

Close vulnerabilities and improve defenses post-incident.

Industries Most at Risk

• Manufacturing
• Healthcare
• Banking & Finance
• Retail & E-commerce
• Logistics
• Education
• Government
• IT / BPO / Shared Services

Any organization dependent on digital operations is a target.

Should You Pay the Ransom?

In most cases, paying ransom does not guarantee recovery. Attackers may:

• Provide broken decryptors
• Demand more money later
• Leak stolen data anyway
• Re-attack the same company

Professional legal, technical, and compliance review is advised before any decision.

Final Message to Industry Leaders

The .t9tyig_p2k ransomware campaign is a warning that cybercriminals continue innovating faster than unprepared businesses can respond. Security is no longer optional – it is a business continuity requirement.

The best defense is not panic after encryption. The best defense is readiness before attack.

ETSPL urges Indian industries and global enterprises to strengthen cyber resilience, test response plans, secure backups, and invest in active threat monitoring now.

About the Author :

Dr. Vinod Gokakakar
MD & CEO,
EBC TECH SERV PVT LTD (ETSPL)

Dr. Vinod Gokakakar is a cybersecurity strategist and technology leader focused on ransomware defense, digital trust, enterprise resilience, and advanced threat protection.

Dr. Vinod Gokakakar is a seasoned IT professional with over 21 years of diverse experience in the technology industry. Throughout his career, Dr. Vinod Gokakakar has worked with a wide range of sectors, from small businesses to multinational corporations, gaining invaluable insights into various technological verticals.

In 2017, leveraging his extensive expertise and recognizing the growing importance of cybersecurity for businesses, Dr. Vinod Gokakakartook a bold step and established his own company. Focused on serving small and medium-sized enterprises (SMEs), his company aimed to provide comprehensive cybersecurity solutions to protect these businesses from emerging
threats in the digital landscape. Dr. Vinod Gokakakar‘s commitment to safeguarding the digital assets of SMEs earned him a reputation as a trusted advisor in the cybersecurity domain.

Building on his success and driven by a passion for addressing complex challenges in the digital realm, Dr. Vinod Gokakakar expanded his entrepreneurial ventures further. In 2017, he founded ETSPL dedicated to providing a comprehensive suite of services encompassing cybersecurity, legal advisory, copyrights, intellectual property rights (IPR), online dispute resolutions, and more. This innovative venture positioned Dr. Vinod Gokakakar as a pioneer in offering integrated solutions that bridge the gap between technology and legal compliance.

Dr. Vinod Gokakakar‘s visionary leadership and multidisciplinary approach have positioned his companies as leaders in the cybersecurity and legal services sectors in India. His ability to anticipate emerging trends, coupled with a deep understanding of both technology and legal frameworks, has enabled him to offer holistic solutions tailored to the evolving needs of his clients.

With a track record of success and a commitment to excellence, Dr. Vinod Gokakakar continues to make significant contributions to the advancement of cybersecurity and legal services, empowering businesses to navigate the complexities of the digital age with confidence and resilience.

Dr. Vinod Gokakakar is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/vinodgokakakar/details/certifications/

Dr. Vinod Gokakakar is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/vinodgokakakar/details/honors/

Dr. Vinod Gokakakar can be contacted at :

LinkedIn | Twitter | E-mail

Are you an IT Professional? Visit FreePikTool website.

About EBC TECH SERV PVT LTD (ETSPL):

EBC TECH SERV PVT LTD (ETSPL) is a Managed IT Services Provider offering IT Advisory and Leadership Services to organizations in India.

EBC TECH SERV PVT LTD (ETSPL) offer value-driven solutions above and beyond managing and operating hosting, security and a full range of IT support services.

EBC TECH SERV PVT LTD (ETSPL) IT Advisory approach enables us to provide leadership to our clients in a complex rapidly changing IT environment.

EBC TECH SERV PVT LTD (ETSPL) focus is to provide maximum “business value” to clients enabling them to grow their businesses, manage their risk/compliance, and increase their competitive position by delivering improved business results.

EBC TECH SERV PVT LTD (ETSPL) client-first approach means our solutions are based on what technology solutions are needed to “fuel” their business, not a particular hardware or hosting solution.

Mission of EBC TECH SERV PVT LTD (ETSPL) :

The mission at EBC TECH SERV PVT LTD (ETSPL) is to add value and to contribute to clients’ long term success and competitive strength. This is accomplished by helping clients to identify solutions that improve performance, by assisting in implementing those solutions and by aiding in managing the subsequent change.

EBC TECH SERV PVT LTD (ETSPL) can be contacted at:

FaceBook | LinkedIn | YouTube | Website | E-mail

About CONSULT INNSERVICES :

We are external analysts who evaluate a company’s IT systems to help them meet their business objectives. Their primary duties include analyzing and diagnosing a company’s IT infrastructure, understanding a client’s business needs, and designing and implementing a technology solution.

We conduct training sessions for employees, reporting, resolving client issues, software testing, troubleshooting issues and developing innovative solutions that will drive growth.

CONSULT INNSERVICES can be contact at:

LinkedIn

Also read Dr. Vinod Gokakakar‘s earlier article :

The post New Ransomware Threat appeared first on Industry4o.com.

Key Takeaway: The story of Beulah Louise Henry, an inventive mind who saw possibility in broken toys, offers a powerful lens for reimagining how we approach data analytics today. Her legacy, echoed in the work of analytics pioneer John Tukey, reminds us that true innovation comes from seeing what others miss, daring to invent anew, and staying true to real human needs.

The Inventor Who Refused to Accept “Good Enough” as an Answer

In a modest North Carolina home in the 1890s, while her peers practiced embroidery and etiquette, young Beulah Louise Henry sat cross-legged on the floor, surrounded by a scatter of springs, gears, and doll parts. To her family, it looked like chaos. To Beulah, it was a classroom; each broken toy a lesson in how things worked, and more importantly, how they could work better.

Born in 1887, Beulah had no formal training in science or engineering. What she did have was a relentless curiosity and a stubborn refusal to accept inconvenience as inevitable. At nineteen, she patented her first invention: a handbag with snap-on covers, offering women variety without the burden of multiple purses. It was practical, elegant, and born from her own lived experience.

That was just the beginning. In 1912, she transformed ice cream making with a vacuum freezer that was cleaner and more efficient. She tackled the frustrations of sewing machines, inventing a lockstitch model that needed no bobbin. Her ideas came in bursts (like “popcorn popping,” she had once said), filling notebooks and, eventually, American homes and offices. By the 1930s, her inventions ranged from compact umbrellas to typewriters that made four copies at once, all designed to solve problems others had simply accepted.

Reporters dubbed her “Lady Edison,” a label that, while well-intentioned, missed the point. Edison had labs and teams; the only things Beulah had were imagination and grit. Rather than chasing fame, she was chasing function; always asking, “Why should things stay this way?” By the end of her career, she held 49 patents and had created over 100 inventions, many of which became everyday essentials.

Beyond historical curiosity, Beulah Henry’s story is a blueprint for how we, as data professionals, can approach our own work in the age of Industry 4.0. The lessons she embodied can be distilled into three rhyming keywords: View, New, and True.

VIEW: Learning to See What Others Miss

Beulah’s genius began with her ability to see problems that others overlooked. Where most saw a handbag, she saw an opportunity for modularity. Where others accepted the mess of ice cream making, she envisioned a cleaner, simpler process.

This principle, View, is foundational in data analytics. It’s about cultivating curiosity, questioning assumptions, finding connections, and exploring data with fresh eyes. The story of John Tukey, a pioneer in statistics, offers a striking parallel. In the mid-20th century, Tukey noticed that traditional statistics forced analysts to fit data into rigid models, often missing the real story. He responded by inventing Exploratory Data Analysis (EDA), a practice that encourages analysts to “listen to the data” before jumping to conclusions.

Tukey’s approach, using box plots, stem-and-leaf diagrams, and visual exploration, was revolutionary. It empowered analysts to discover patterns, outliers, and relationships that would otherwise remain hidden. Like Beulah, Tukey refused to accept the status quo, insisting that the first step in problem-solving is to truly see the problem; to VIEW it.

In today’s analytics landscape, this means resisting the urge to immediately apply familiar algorithms or dashboards. Instead, we must spend time understanding what our data is actually telling us. Are we exploring outliers or just discarding them? Are we asking new questions, or simply automating old answers? The View principle challenges us to approach data with the same wonder and skepticism that Beulah brought to her broken toys.

NEW: Innovation Through Practical Imagination

Rather than being content to see problems, Beulah reimagined solutions. Her vacuum ice cream freezer and bobbinless sewing machine were better than incremental improvements, for they were seen as leaps born from practical imagination. She asked, “What if we did it differently?” and then set out to build prototypes that proved it could be done.

In data analytics, the NEW principle may start off being about adopting the latest tools. But it soon progresses to rethinking the very problems we’re trying to solve. Too often, we focus on optimizing existing reports or models, rather than questioning whether those outputs are even necessary. Are we providing real-time insights when stakeholders need them, or just faster versions of yesterday’s reports? Are we designing data architectures around user workflows, or around technical convenience?

The most transformative analytics projects I’ve seen would seek to go beyond using new technology; they would redefine the problem. They asked “what if” questions that seemed impossible until someone, like Beulah, built a working prototype. For example, when business users struggled with data literacy, one team I worked with created interactive storyboards instead of static dashboards, making insights accessible to everyone, not just analysts.

The New principle invites us to be inventors, not just implementers. It’s about daring first to imagine, and then to build, solutions that eliminate friction entirely.

TRUE: Staying Authentic to Real Human Needs

Every one of Beulah’s inventions addressed a genuine, often unspoken, need. Her compact umbrellas fit into handbags because she understood how women navigated their days. Her typewriter innovations made office work more efficient because she had seen the bottlenecks firsthand.

The True principle is about staying grounded in authentic user needs. In analytics, technical brilliance is meaningless if it doesn’t improve real human experiences. I’ve seen machine learning models that dazzled data scientists but left business users confused. I’ve seen dashboards that delivered instant access to irrelevant information. The best analytics solutions are those that solve problems users actually feel; even if they can’t articulate them.

This means spending time with stakeholders, observing their workflows, listening to their stories, and identifying pain points that have become invisible through familiarity. It means designing tools that align with how people actually think and work, not how we wish they would.

The True principle reminds us that the ultimate goal of analytics is not just insight, but impact. Like Beulah, we must pay attention to the real lives behind the data, ensuring our solutions make a tangible difference.

Bringing It All Together: The Popcorn Legacy

Beulah Louise Henry’s ideas came like popcorn: sudden, rapid, unpredictable, unstoppable. She filled notebooks with sketches and calculations, always chasing the next solution to a problem which others had accepted as unsolvable. By the 1930s, her inventions were everywhere, yet her name faded into obscurity even as her creations became commonplace.

Her legacy is a call to action for all of us in the data world. We, too, are surrounded by scattered puzzle pieces (such as customer data, operational metrics, market signals, and the like) waiting for someone with Beulah’s curiosity to see the patterns, imagine new solutions, and stay true to the needs of real people.

Key Finding: Innovation in data analytics doesn’t require the latest technology or the fanciest credentials. It requires the courage to question, the imagination to invent, and the empathy to serve.

So, the next time you’re faced with a messy dataset or a stubborn business problem, remember Beulah Louise Henry. Sit with the broken pieces. Ask better questions. Build something new. And above all, stay true to the people you’re trying to help. Because sometimes, the most powerful ideas really do come like popcorn: sudden, surprising, and impossible to ignore.

The world called Beulah “Lady Edison” because it couldn’t imagine calling her what she simply was: an inventor. Let’s honor her legacy by being inventors in our own right: curious, creative, deliberate, and always striving to make things work just a little bit better.

Banner Image : AI generated 

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is a truly exceptional professional who has left an indelible mark on the IT, health and human services, and higher education sectors. Dr. Joe Perez journey began in the field of education, where he laid the foundation for his career. With advanced degrees in education and a doctorate that included a double minor in computers and theology, Dr. Joe Perez embarked on a path that ultimately led him to the dynamic world of data-driven Information Technology.

In the early 1990s, Dr. Joe Perez transitioned into IT, starting as a Computer Consultant at NC State University. Over the years, Dr. Joe Perez’s dedication and expertise led to a series of well- deserved promotions, culminating in his role as Business Intelligence Specialist that capped his 25 successful years at NC State. Not one to rest on his laurels, Dr. Joe Perez embarked on a new challenge in the fall of 2017, when he was recruited to take on the role of Senior Business Analyst at the NC Department of Health & Human Services (NCDHHS). His impressive journey continued with promotions to Senior Systems Specialist and Team Leader, showcasing his versatility and leadership capabilities.

In addition to his full-time responsibilities at NCDHHS, Dr. Joe Perez assumed the role of
fractional Chief Technology Officer at a North Carolina firm in October 2020. An Amazon best-selling author with over 21,000 followers on LinkedIn and numerous professional certifications, he is a highly sought-after international keynote speaker and recognized expert in data analytics and visualization.

Dr. Joe Perez’s contributions have not gone unnoticed. Dr. Joe Perez is a recipient of the IOT Industry Insights 2021 Thought Leader of the Year award, the 2023 Gartner Peer Community Ambassador of the Year award, and Top Ten Global Thought Leader for several categories at Thinkers360 in four consecutive years (2023-2026). Dr. Joe Perez holds memberships in several prestigious Thought Leader communities, and was recently inducted into the United Nations Global Network of Data Officers and Statisticians. Dr. Joe Perez’s reach extends to more than twenty countries worldwide, where he impacts thousands through his speaking engagements, articles, and status as a Distinguished Fellow in the Public Sector Network’s Future Government Institute.

Beyond his professional achievements, Dr. Joe Perez’s passion for teaching remains
undiminished. Whether as a speaker, workshop facilitator, podcast guest, conference emcee, or team leader, he continually inspires individuals to strive for excellence. He treasures his time with his family and is a gifted musician, singer, pianist, and composer. Joe also dedicates his skills through involvement in his church’s Hispanic ministry. Dr. Joe Perez manages the publication of a widely recognized monthly military newsletter, The Patriot News, and is deeply committed to his community.

To maintain a balanced life, Dr. Joe Perez is a regular at the gym, and he finds relaxation in watching Star Trek reruns. He lives by the philosophy that innovation is the key to progress, and he approaches each day with boundless energy and an unwavering commitment to excellence. Dr. Joe Perez’s journey is a testament to the remarkable achievements of a truly exceptional individual, and more importantly, to the amazing grace of God.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

The post Innovation through Data Analytics appeared first on Industry4o.com.

Executive Summary

Manufacturing is at an inflection point. Persistent supply chain volatility, increasing product complexity, workforce challenges, and rising sustainability expectations are exposing the limits of fragmented digital initiatives. Incremental automation and isolated digital tools are no longer sufficient. What is required is a more holistic transformation of how products are designed, produced, operated, and sustained.

Industry 5.0 reframes the manufacturing agenda around human centricity, resilience, and sustainability. Achieving this vision depends on Smart Manufacturing and the Factory of the Future, enabled by tightly integrated digital technologies, advanced analytics, and adaptive systems that connect engineering, supply chain, manufacturing & operations, quality & regulatory, and service.

This article presents a pragmatic roadmap for manufacturers seeking to move from ambition to execution. It highlights the role of ET-IT-OT-AI integration, Digital Threads & Digital Twins as foundational capabilities, and the practical application of emerging technologies such as Artificial Intelligence including Generative AI and Industrial AI, robotics, and the industrial metaverse.

Industry Context and Key Trends

The transition from Industry 4.0 to Industry 5.0 builds on connectivity and automation but extends the focus to collaboration between humans and intelligent systems, long term sustainability, and operational resilience.

Smart Manufacturing and the Factory of the Future

Smart Manufacturing and the Factory of the Future are not abstract concepts. Together, they represent an integrated, intelligent, and adaptive manufacturing ecosystem that seamlessly connects physical operations with digital intelligence.

Smart Manufacturing and the Factory of the Future

Smart Manufacturing and the Factory of the Future are not abstract concepts. Together, they represent an integrated, intelligent, and adaptive manufacturing ecosystem that seamlessly connects physical operations with digital intelligence.

 Core Principles and Characteristics

 A Pragmatic Roadmap to Transformation

Transformation must be phased, outcome driven, and scalable. Successful manufacturers approach Smart Manufacturing as a journey rather than a single program.

 Illustrative Examples

 Outcomes and Value Realization

Organizations that embrace Smart Manufacturing and the Factory of the Future capabilities consistently report measurable business outcomes. These include cycle time reductions of up to 45%, defect reductions approaching 40%, operational cost savings between 15% and 30%, meaningful reductions in energy use and emissions, and improved workforce safety alongside faster skill development.

 Conclusion

Smart Manufacturing and the Factory of the Future are no longer optional initiatives for manufacturers across discrete and process industries. They are strategic imperatives. By integrating engineering, information, and operational technologies, applying Industrial AI, robotics, and immersive technologies, and embedding sustainability and human centric design, organizations can achieve the agility, resilience, and competitiveness required in an Industry 5.0 world.

The path forward is pragmatic. Establish strong digital foundations. Scale intelligence and immersive capabilities where they deliver value. Sustain progress through continuous innovation and learning. Industry 5.0 is not about replacing people with machines. It is about building collaborative ecosystems in which technology amplifies human capability and judgment.

To explore practical research, frameworks, and real-world approaches for advancing Smart Manufacturing and the Factory of the Future, visit HitachiDS.com.

About the Author

Mr. Sankar Natarajan
Senior Director & Global Head of Digital PLM, Smart Manufacturing & Ops and Industrial Metaverse Practices
Hitachi Digital Services

Mr. Sankar Natarajan is the Senior Director and Global Head of Digital  PLM, Smart Manufacturing & Ops, and Industrial Metaverse practices at Hitachi Digital Services.

Mr. Sankar Natarajan leads the global strategy, solutions, go-to-market initiatives, and delivery of integrated digital transformations across products, factories, operations, and sustainability.

Under Mr. Sankar Natarajan leadership, teams drive large-scale innovation across the Hitachi Group of Companies and a rapidly expanding global client base.

With 20+ years of experience, Mr. Sankar Natarajan is a recognized industry thought leader in engineering, product development, supply chain, manufacturing, and operations.

Mr. Sankar Natarajan partners with leading organizations to develop outcome-driven digital transformation strategies, ensuring excellence in product innovation, supply chain and manufacturing operations, quality, regulatory compliance, and environmental sustainability.

By leveraging design thinking, digital & AI capabilities, and cutting-edge technologies, Mr. Sankar Natarajan helps businesses achieve measurable impact and long-term success.

Mr. Sankar Natarajan can be contacted at:

LinkedIn

About Hitachi Digital Services 

Hitachi Digital Services, a wholly owned subsidiary of Hitachi, Ltd., is a global systems integrator powering mission-critical platforms with people and technology.

Hitachi Digital Services help enterprises build, integrate, and run physical and digital systems with tailored solutions in cloud, data, IoT, and ERP modernization, underpinned by advanced AI.

By combining Information Technology and Operational Technology (ITxOT), Hitachi Digital Services drive efficiency, innovation, and growth across industries.

With over 110 years of Hitachi Group’s engineering and technology leadership, Hitachi Digital Services is powering smarter platforms for a safer, more sustainable future.

To learn more, visit hitachids.com

The post Smart Manufacturing & Future Factory appeared first on Industry4o.com.

As the world grapples with geopolitical volatility and uncertain economic tides, India stands tall, emerging not just as a resilient player but as a beacon of hope for the future. With a projected GDP growth of 8.2%, India is carving its path to become a global economic powerhouse. Yet, the country’s success story is far from linear—there are multifaceted challenges and opportunities ahead, particularly in the realms of innovation, sustainability, and inclusive growth.

Key Trends Shaping India’s Future :

Resilient Growth Amid Global Challenges :

India’s economy is projected to grow robustly, with a per capita consumption reaching approximately $3,000 by 2050. While the per capita consumption for consumer goods is currently at $54, this offers immense room for growth, particularly in rural areas where women’s participation in the workforce has risen to 35-40%. This demographic shift holds the promise of increased economic activity and greater social equity.

Innovation and Intellectual Property (IP) :

India’s R&D and IP patent landscape must see dramatic growth if it is to emerge as a global leader in technology. Fostering a culture of innovation will be crucial as India aspires to lead in fields like AI, biotech, and semiconductors.

Inflation and Consumer Sentiment :

Controlling food inflation is essential. The subdued consumer demand in recent years signals the need for more nuanced fiscal and policy interventions to ensure that growth benefits reach all sections of society.

Private Equity and Mutual Funds :

Private equity (PE) investments have been one of the key growth engines for businesses, while the mutual fund sector is growing at 15%—outpacing traditional bank deposits and fixed deposits (FDs). This shift signals a growing investor confidence in the Indian market.

Financial Reserves and Currency Volatility :

With $650 billion in reserves, India is well-positioned to handle financial shocks. However, the looming depreciation of the rupee and the looming risk of the exchange rate crossing the 100/USD mark could be a silent tax on exporters and citizens alike.

Strategic Challenges and Solutions :

Proactive Policy Management :

India must move beyond reactive policymaking and adopt a more strategic, anticipatory approach. Whether it’s negotiating tariffs or addressing geopolitical challenges, foresight and long-term planning are crucial to avoid the “firefighting” approach that hinders sustainable growth.

Global Trade and Negotiations :

India must strengthen its global negotiating position. While countries like Mexico and the U.S. have demonstrated an aggressive stance in renegotiating trade terms, India has often been seen as an adjustable, not indispensable, partner. To elevate its global status, India needs to leverage its market size and geopolitical importance to secure better trade deals.

The Role of Youth and Education :

India’s demographic advantage is undeniable—by 2050, the country is expected to have over 100 million high-net-worth individuals (HNWIs). However, the country must invest heavily in skilling, re-skilling, and up-skilling its workforce, particularly in emerging fields like AI, semiconductors, and renewable energy. This requires an overhaul of education systems, aligning them with future job market needs.

The Vision for India’s Economy in 2050 :

Digital Payments and Currency :

By 2030, digital payments in India are projected to exceed 80%, and the Digital Rupee could account for 25-30% of retail payments by 2040. India’s digital-first economy will serve as the backbone of its future growth, contributing significantly to GDP expansion from $4T to $30T.

AI and Wealth Management :

AI-driven models are expected to reduce non-performing assets (NPAs) by 40%, while the wealth management industry is set to evolve to cater to an increasing number of HNWIs. CXOs must balance technology adoption with ethical considerations, particularly in the areas of finance, healthcare, and retirement planning.

Sustainability and Green Growth :

India’s future economic growth will also be defined by sustainability. The country must aim for 50% of its energy needs to be met from renewable sources by 2050, while simultaneously advancing green financing initiatives to drive low-carbon growth.

A Comprehensive Action Plan for India’s Economy :

A – Access :

Ensure universal access to digital infrastructure, healthcare, education, and global markets. Bridging the urban-rural divide is crucial to fostering inclusive growth.

B – Backlogs :

Fast-track judicial, infrastructure, policy, and project backlogs to increase efficiency and development pace.

C – Costs & Corruption :

Reduce transaction costs and curb corruption through digitization, transparent procurement, and real-time audits. Transparency must be enforced at every level

D – Regulatory Delays :

Establish time-bound approvals, single-window clearances, and predictable policies to boost ease of doing business and attract global investment.

E – Employment :

Job creation must be a priority, particularly in manufacturing, MSMEs, green energy, and the digital economy.

F – Foreign Policy :

Strengthen strategic multi-alignment, leveraging India’s role as a bridge between the Global North and South to navigate geopolitical tensions.

G – Geopolitics :

India must use its demographic, economic, and diplomatic weight to counter supply chain risks and enhance regional stability.

H – Human Capital :

Skilling and up-skilling should be aligned with emerging technologies such as AI, semiconductors, and climate tech.

I – Infrastructure :

Build world-class infrastructure—roads, ports, logistics, and energy grids—to support India’s $5T+ economy aspirations.

J – Judicial Reforms :

Speed up justice delivery with tech-enabled courts and alternative dispute resolution systems.

K – Knowledge Economy :

Transition from a service-based economy to an IP-led knowledge economy, focusing on AI, deep tech, and biotech.

L – Logistics :

Improve logistics efficiency and bring down logistics costs to global benchmarks through multimodal integration and effective supply chain management.

M – Manufacturing :

Position India as the global manufacturing hub, leveraging the “Make in India” and “PLI” schemes to replace China as the world’s factory.

N – National Security :

Strengthen national security—cyber, space, maritime, and border security—amid rising global tensions.

O – Open Markets :

Pursue balanced trade policies, with strategic FTAs that protect domestic industries while expanding export opportunities.

P – Policy Stability :

Consistent, long-term government policies to build investor confidence and foster trust in the Indian market.

Q – Quality & Standards :

Establish global quality standards across manufacturing, education, healthcare, and governance.

R – Research & Innovation :

Increase R&D expenditure and foster academia-industry collaborations to boost innovation and IP certifications.

S – Sustainability :

Balance economic growth with sustainability by advancing green energy, climate action, and renewable financing.

T – Technology Adoption :

Rapid adoption of technologies such as AI, block chain, machine learning, and Industry 4.0 to maintain competitive advantage.

U – Urbanization :

Develop smart, inclusive cities with efficient transport, housing, and digital governance systems.

V – Vigilant Governance :

Data-driven, accountable, and citizen-centric governance should be prioritized in all levels of government.

W – Workforce Mobility :

Facilitate the movement of the Indian workforce, both domestically and globally, by recognizing acquired skills and promoting global employment opportunities.

X – Exports :

Expand high-value exports—particularly in electronics, defense, pharmaceuticals, and professional services—to diversify markets and reduce dependence on a few.

Y – Youth Dividend :

Invest in the education, entrepreneurship, and innovation capacities of India’s young population to harness its demographic dividend.

Z – Zero Tolerance for Inefficiency :

Implement zero tolerance for inefficiency, delays, and systemic leakages to fast-track India’s rise as a global economic leader.

Conclusion :

India stands at a transformative juncture in its journey to becoming a global economic leader. With a strategic focus on digital infrastructure, innovation, sustainability, and inclusive growth, the country has the potential to redefine its role in the global economy by 2050. As we navigate these challenges and opportunities, India must be agile, proactive, and committed to long-term vision.

About the Author :

Mr. Jawahar Lalla
Technology & IT Consultant / Blogger/ Podcaster/ Digital Influencer

Mr. Jawahar Lalla is a seasoned Motivational Speaker, Industry Expert, and Professional Trainer.

Mr. Jawahar Lalla with over 40 years of extensive experience in the IT industry, brings a wealth of knowledge as a senior management executive, writer, blogger, and speaker.

Mr. Jawahar Lalla is also digital influencer having more than 2 Lakhs connects across social media and digital platforms.

Mr. Jawahar Lalla has contributed insightful articles to both Indian and international publications on industry-related topics.

Mr. Jawahar Lalla is frequently invited as a keynote speaker and panel judge at conferences and seminars, where his deep industry insights and leadership experience are highly valued.

Mr. Jawahar Lalla is a passionate reviewer, he has reviewed numerous books in the fields of technology and management.

Mr. Jawahar Lalla is also active as a blogger, podcaster, public speaker, and even a model.

Mr. Jawahar Lalla is a prolific co-author, he has contributed to more than 450 anthology books.

Mr. Jawahar Lalla interviews and podcasts are available on his YouTube channel: jawaharlalla3573.

Mr. Jawahar Lalla can be contacted at :

LinkedIn | YouTube | E-mail | E-mail2 | Mobile : +91 – 9819428307

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India today stands on the brink of a transformative shift in mobility. As global markets move rapidly toward cleaner and more sustainable transportation, India has positioned itself among the world’s fastest-growing electric vehicle (EV) destinations. Backed by strong policy initiatives, forward-looking OEMs, and rising consumer interest, the EV revolution is generating not just cleaner mobility—but a powerful wave of new-age career opportunities for the country’s youth.

Over the past year, the pace of EV adoption has been nothing short of remarkable. In just the first six months of FY 2025–26, India recorded 91,726 EV sales—already 86 percent of the previous year’s total of 1.1 lakh. The share of EVs in the passenger vehicle market has nearly doubled, rising from 2.6 percent in FY25 to almost 5 percent this year. Data from the Federation of Automobile Dealers Associations (FADA) shows a 108 percent year-on-year surge, underscoring a fundamental shift in how Indians view mobility. These numbers are not mere indicators of sales momentum; they reflect a deeper structural transition within the automotive landscape, one that demands new technology, new talent, and new ways of thinking.

Global projections reinforce this rise. The International Energy Agency (IEA) estimates that India could become the world’s second-largest EV market by 2030. The government’s own targets aim for 30 percent vehicle electrification by the end of this decade. Meanwhile, NITI Aayog values India’s potential EV market at nearly USD 300 billion by 2030. It is increasingly clear that the EV transition is no longer a futuristic idea—it is the country’s next major economic engine. Yet, as India accelerates into this electric future, a significant challenge has emerged: the widening skills gap.

For decades, India’s automotive workforce has been trained around internal combustion engine (ICE) technologies, leaving a sizable portion of existing skill sets irrelevant or inadequate for the demands of electric mobility. According to SIAM, India will require one to two lakh EV-ready professionals—including technicians, engineers, and R&D specialists—by 2030. Its EV Skill Gap Study reveals that 43 percent of ICE-related competencies have little relevance in the EV ecosystem, while another 27 percent require deep reskilling. This gap threatens to become a bottleneck unless addressed through large-scale, future-oriented skilling initiatives.

Skill development has therefore become a national priority. The EV industry is creating fresh opportunities across vehicle design, battery systems, manufacturing, diagnostics, servicing, charging infrastructure, and energy management. As vehicles become more software-driven and technologically sophisticated, the need for specialised knowledge in areas such as high-voltage safety, power electronics, battery chemistry, and thermal management has intensified. A skilled workforce is essential not only for ensuring vehicle reliability and customer satisfaction but also for enabling India to compete globally as an EV manufacturing and innovation hub.

Leading this transformation on the skilling front is the Automotive Skills Development Council (ASDC), the apex body for automotive skilling in India. ASDC has been actively designing and delivering future-ready EV job roles and training programmes that align with industry demands.

From battery pack engineers and powertrain designers to EV service technicians and assembly specialists, ASDC‘s curriculum reflects the evolving needs of the mobility ecosystem. These emerging job profiles offer young Indians strong career stability, competitive salaries, and clear pathways for upward mobility, making the EV sector one of the most attractive career avenues of the decade.

India’s electric mobility mission will succeed only when supported by a workforce capable of driving innovation, ensuring safety, and maintaining global competitiveness. The EV revolution is not just about cleaner vehicles—it is about preparing a new generation of professionals equipped with the skills and vision to lead the country into a  sustainable future. ASDC‘s role in empowering youth with industry-aligned EV skills has become central to this national journey.

As India accelerates its EV transition, the opportunity ahead is immense. With the right skilling ecosystem, the nation can meet its sustainability goals, strengthen its manufacturing capabilities, and emerge as a global powerhouse for electric mobility. For India’s youth, the message is clear: the future of mobility belongs to those who are ready to skill, adapt, and lead in this electrifying new era.

About the Author :

Mr. Vinkesh Gulati
Chairperson,
Automotive Skills Development Council ( ASDC )

About Automotive Skills Development Council ( ASDC )

Automotive Skills Development Council ( ASDC ) is the first sector skill council of India, promoted by the Automobile industry through Society of Indian Automobile Manufacturers (SIAM), Automotive Component Manufacturers Association (ACMA) and Federation of Automobile Dealers Association (FADA) and Government of India represented by Department of Heavy Industry and National Skill Development Corporation (NSDC) & Ministry of Skill Development and Entrepreneurship, Government of India.

Automotive Skills Development Council ( ASDC ) Vision :

To continually develop and upgrade Automotive Skills for Higher value additions ( higher value addition through Skilling will facilitate capital creation, leading to more economic activity and consequent additional jobs.)

Making Skills aspirational and integrated with academic pathways.

Honoring and celebrating the Skilling achievements.

Automotive Skills Development Council ( ASDC ) Key Achievements for the
Financial Year 2024 -25 :

✦ Starting with 1 CSR Project in 2020-21, ASDC delivered 12 CSR projects during FY 2024-25

✦ Associated with Bajaj Auto flagship project BEST and delivered 401 student assessments in FY 2024-25

✦ Delivered Hero Saksham project for 4000 women candidates for dealership roles with 50% placement

✦ Affiliated 5 training centers of Mahindra & Mahindra – Farm Division

✦ 28 Industry-based centers affiliated during FY 2024-25

✦ Signed two international agreements – with Volvo (Sweden) for international mobility & Steinbeis University (Germany) for 1+1 model of Masters Programme

✦ 14 new Higher Education partners added for skill-based course implementation

✦ Launched Skill Olympiad (with CBSE) and EV Hackathon (with BYD)

✦ Initiated 3-year Diploma program delivery at 2 industry partners (Sellowrap & Subros)

✦ Completed 2 lakh+ apprenticeship contracts during FY 2024-25

Automotive Skills Development Council ( ASDC ) Social Media Links :

LinkedIn | FaceBook | X | Website | YouTube | Instagram

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Robsurgical, Founded in 2012 as a spin-off of the Universitat Politècnica de Catalunya (UPC) and the Institut de Bioenginyeria de Catalunya (IBEC), its mission is to democratize high-precision surgery, making it more accessible, efficient and safe for hospitals of all sizes.

Barcelona, 19 november 2025.- Robsurgical, a Catalan company specialized in minimally invasive surgical robotics, has just joined the DFactory Barcelona ecosystem with its own space. This morning, the State’s Special Delegate at the Consorci de la Zona Franca de Barcelona (CZFB), Pere Navarro, and the CZFB’s General Director, mets with Mario Ferradosa, Managing Director of RobSurgical, to learn first-hand about the activities they are developing and their future plans.

Robsurgical, Founded in 2012 as a spin-off of the Universitat Politècnica de Catalunya (UPC) and the Institut de Bioenginyeria de Catalunya (IBEC), its main mission is to democratize high-precision surgery, making it more accessible, efficient, and safe for hospitals of all sizes. The company has a production plant in El Prat de Llobregat, and its Bitrack robot has been successfully tested in urological surgeries. It is currently in the process of expanding its surgical indications.

RobSurgical designs, develops, and markets a surgical robot that improves efficiency and usability in the operating room and enables hybrid surgeries, combining MIS (minimally invasive surgery) and MIRS (minimally invasive robotic surgery) techniques in a single procedure. The system is designed to be portable, flexible, and quick to install, facilitating its adoption in small and medium-sized hospitals and expanding access to robotic surgery.

Mario Ferradosa, Managing Director of RobSurgical, explained: DFactory Barcelona offers us an advanced innovation environment that is ideal for developing cutting-edge technology in surgical robotics. Moreover, it allows us to attract highly qualified talent in engineering, robotics, and biomedicine, and fosters connections with research centers and technology companies, thereby accelerating the development of innovative medical solutions.

Mr. Pere Navarro – The Special State Delegate at the Consorci de la Zona Franca de
Barcelona (CZFB), Mr. Mario Ferradosa – Managing Director of RobSurgical &
Mr. Carlos Vives – CFO of RobSurgical

For his part, the State’s Special Delegate at the CZFB, Pere Navarro, highlighted: The incorporation of RobSurgical into the DFactory Barcelona ecosystem reinforces the importance of Industry 4.0 applied to healthcare. RobSurgical’s commitment to democratizing high-precision surgery, making it more accessible and safer, is an example of the positive impact of technology on people’s lives. We are delighted to host projects that transform the industry and improve people’s health and the quality of care”. Navarro has added that “RobSurgical’s arrival at DFactory Barcelona demonstrates the potential of our ecosystem to attract projects that transform society. Robotics applied to healthcare represents innovation with a real impact on quality of life. At the CZFB, we work to ensure that pioneering companies find the ideal space here to grow and collaborate”.

 Leading Industry 4.0 from Barcelona

DFactory Barcelona promotes open and collaborative innovation among companies that jointly develop technological projects and solutions. It has now become the leading international Industry 4.0 ecosystem, hosting 40 multinational corporations, medium and small enterprises, and research centers that are pioneering projects in fields such as robotics, sensors, 3D printing, cybersecurity, augmented and virtual reality, artificial intelligence, blockchain, and nanotechnology.

Last July, expansion works began to increase the current surface area from 17,000 m² to 60,000 m² by the end of 2026. To achieve this, four new buildings are being constructed: the Cubo building – 1,100 m², which will host major international events; two buildings of 24,370 m² and 11,640 m² respectively — dedicated to manufacturing, housing new companies, laboratories, technology centers, and offices; and a 2,300 m² service building for companies within the ecosystem.

About the Consorci de la Zona Franca de Barcelona

The Consorci de la Zona Franca de Barcelona (CZFB) is a public entity that acts as a driving force for technological and industrial development, supporting companies in their transition to the digital era. This stems from the CZFB’s strategic vision to lead Industry 4.0.

It promotes the District 4.0 by fostering the transformation of Industry 4.0 from the DFactory Barcelona technological ecosystem, and it champions innovation through initiatives such as the 3D Incubator and the Logistics 4.0 Incubator. The CZFB connects the new economy with its key socioeconomic sectors.

It also organizes major economic events in Barcelona, including SIL, the largest logistics trade fair in Spain and Southern Europe, as well as flagship events with innovative formats such as the Barcelona New Economy Week (BNEW) and the Barcelona Woman Acceleration Week (BWAW).

The CZFB’s plenary includes representatives from the Spanish Government, the Government of Catalonia, the Barcelona City Council, and the city’s leading economic institutions.

About the Consorci de la Zona Franca de Barcelona

The Consorci de la Zona Franca de Barcelona is a public company that acts as a driver of technological and industrial development by supporting companies in their transition towards the digital era, because of CZFB’s strategic vision to lead Industry 4.0.

It promotes District 4.0 by driving the transformation of Industry 4.0 from the DFactory Barcelona technological ecosystem, champions innovation through the 3D Incubator and the Logistics 4.0 Incubator and connects the new economy and its key socioeconomic sectors. It organises congresses of economic interest for Barcelona, such as Spain’s and southern Europe’s largest logistics fair, SIL, as well as major flagship events in an innovative format such as Barcelona New Economy Week (BNEW) and Barcelona Woman Acceleration Week (BWAW).

Its plenary includes representatives from the Spanish government, the Generalitat of Catalonia, Barcelona City Council, and the city’s main economic institutions.

CZFB Can be contacted at :

FaceBook | Instagram | LinkedIn | X | YouTube | BKSY

For more information:

Lluís López Yuste
Director – Departament de Comunicació
Department of Corporate Communication of CZFB

Av. del Parc Logístic, 2-10
Pol. Ind. Zona Franca – 08040 Barcelona

Lluís López Yuste can be contacted at :

LinkedIn | E-mail | Website | Tel: +34 609 358 475 | Tlf. 932 638 111

Earlier Articles :

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Imagine having a conversation with someone who forgets everything you said just seconds ago. Frustrating, right? This is how traditional AI models often behave—they generate responses based only on the immediate input, without remembering previous interactions.

The Model Context Protocol (MCP) is designed to solve this problem. It acts like a memory layer for AI models, ensuring they can retain context, track conversations, and make more relevant and consistent responses.

At its core, MCP helps AI systems understand and remember past interactions, making them smarter, more reliable, and context-aware. Instead of treating every question as a standalone request, MCP allows AI to connect the dots—just like how humans recall past discussions to give better answers.

Why is Context Preservation Important in AI Applications?

Context is what makes human conversations flow naturally. Imagine asking a customer support chatbot:

User : “What’s the status of my order?”
Bot : “Could you provide your order ID?”
User : “Yes, it’s 12345.”
Bot : “Which order are you referring to?”

Without context, the chatbot doesn’t remember that you just provided an order ID! This makes interactions frustrating and inefficient.

With MCP, AI models can maintain context across multiple exchanges. This means:

Smoother conversations – AI remembers what was said before
Better accuracy – AI provides relevant responses based on past inputs
Personalization – AI tailors responses based on prior interactions

For applications like chatbots, virtual assistants, and customer support AI, context preservation is critical. It ensures that users don’t have to repeat themselves and that the AI can engage in more natural, intelligent conversations.

How MCP Enhances Prompt Engineering & RAG (Retrieval-Augmented Generation) Models

Improving Prompt Engineering with MCP

Prompt engineering is the technique of crafting effective instructions (or “prompts”) to guide AI in generating accurate responses. However, without MCP, each prompt is like a fresh start—AI lacks memory of previous interactions.

With MCP, prompts become dynamic and context-aware, allowing AI to remember past interactions and generate better responses.

For example, a basic LLM prompt without MCP might look like this:

“Summarize the latest news about electric vehicles.”

But with MCP, the AI can consider previous requests and refine its responses:

“Based on our previous discussion on Tesla’s new battery technology, summarize the latest news about electric vehicles.”

This leads to better relevance, more continuity, and less repetitive answers.

Enhancing Retrieval-Augmented Generation (RAG) with MCP

Retrieval-Augmented Generation (RAG) improves AI responses by pulling in external data from knowledge bases or documents before generating an answer. However, RAG alone doesn’t remember user preferences or prior searches.

MCP enhances RAG by keeping track of user interactions, making retrieval more relevant and personalized.

Example:

Without MCP:

User: “Tell me about climate change.”
Bot: Retrieves general climate change data from Wikipedia.

With MCP:

User: “Tell me about climate change.”
Bot: Retrieves climate data from scientific journals, since the user previously asked about research-based information.

This means AI can fine-tune its knowledge retrieval based on user behaviour, making responses smarter and more contextually aware.

How MCP integrates with Prompts and RAG apps

Basic Integration

MCP can be integrated with prompts by adding specific instructions that ask the model to assess its confidence. Here’s a simple example:

Advanced Integration

For more sophisticated applications, you can structure prompts to:

Example:

MCP with RAG Applications

RAG applications combine retrieval systems with generative models. Here’s how MCP enhances RAG:

Simple RAG+MCP Example

End-to-End Solution Architecture

The architecture below shows how MCP integrates into a prompt-based RAG system:

Model Comparison Platform (MCP) is a system that helps you compare and evaluate different AI models. It lets you test models side-by-side to see which one performs best for your specific needs.

Key Integration Points

1. Prompt Optimization: MCP tests different prompt templates to find which ones get the best responses from your AI models.

2. RAG Enhancement: MCP evaluates how well your retrieval system finds relevant information and how effectively your AI model uses this information.

3. A/B Testing: Compare different versions of prompts and RAG setups to see which performs best.

4. Feedback Loop: Continuously improve your system based on real user interactions and performance metrics.

Example: Customer Support AI

Let’s say you’re building a customer support AI that answers product questions.

Without MCP:

• You write prompts based on your best guess
• You set up RAG with your product documentation
• You hope it works well for customers

With MCP:

• You create 5 different prompt templates
• You test each template with different RAG configurations
• MCP shows you which combination gives the most accurate answers
• You implement the winning combination in your application

End-to-End Solution Architecture

Components Explained

1. Application Interface: Where users interact with your AI system.

2. Prompt Engineering Layer: Creates and manages different prompts.

3. RAG System:

• • Vector Database: Stores your document embeddings
  • Retrieval Engine: Finds relevant information for user queries

4. MCP Platform:

• • Multiple AI Models: Compare different models (e.g., Claude, GPT-4)
  • Validation Layer: Checks responses for accuracy, relevance, etc.
  • Metrics Engine: Collects performance data

5. Analytics Dashboard: Shows you how your system is performing.

6. Feedback Loop: Routes performance data back to improve prompts and RAG.

Monitoring and Analytics Tools

AI models can sometimes give inaccurate, slow, or irrelevant responses. To improve their performance, we need tools that track, analyze, and optimize AI behavior. MCP provides several monitoring and analytics tools to help developers and AI teams understand how well their system is working.

MCP provides several tools to monitor your AI system:

1. Performance Dashboard

The Performance Dashboard acts like a control panel for AI models, showing real-time statistics on how well they are performing.

Response Accuracy – How often the AI provides correct or helpful answers
Response Time – How quickly the AI replies to a user query
User Satisfaction Scores – Feedback from users about the AI’s responses
Failure Rates – How often the AI fails to generate a response or gives a bad answer

For eg.

Imagine an AI chatbot that answers customer support questions. The Performance Dashboard helps the team track:

• If users are happy with the answers (User Satisfaction Score)
• If the bot is responding fast enough (Response Time)
• If the bot is failing too often (Failure Rate)

This helps AI teams adjust and improve their models over time.

2. Prompt Effectiveness Analysis

AI models generate responses based on prompts, which are the instructions given to them. The Prompt Effectiveness Analysis tool helps identify the most effective prompts. See which prompts work best for different types of questions:

• Which prompt template gets the highest accuracy?
• Which prompt results in the fewest hallucinations?
• How do prompts perform across different user segments?

Example:

A company tests two different prompts for a chatbot:

• Prompt A: “Summarize this document in simple words.”
• Prompt B: “Give a short, clear summary of this document for a general audience.”

After analyzing responses, the team finds that Prompt B produces clearer, more accurate summaries. They decide to use it across all their AI interactions.

3. RAG Quality Metrics

Retrieval-Augmented Generation (RAG) is when AI models fetch relevant documents to improve response quality. The RAG Quality Metrics tool ensures this process works correctly. Measure how well your retrieval system works:

• Relevance of retrieved documents – Are the documents truly related to the query?
• Coverage of necessary information – Does the AI fetch enough data to answer correctly?
• Impact on final response quality – Does using retrieval improve AI responses?

Example:

A legal AI assistant uses RAG to fetch case laws before answering a legal question. If the retrieved cases are outdated or irrelevant, the assistant may give a bad answer. The RAG Quality Metrics tool flags these issues, allowing AI engineers to fine-tune retrieval settings.

4. A/B Test Results

A/B testing allows teams to compare different AI setups and see which one works better. Compare different configurations:

Example:

A company tests two AI models for customer support:

After running the test, they find that Model B provides better responses because fetching more documents improves accuracy. Based on this, they switch to Model B for production.

Implementing Monitoring

AI models don’t always give perfect responses—sometimes they lack confidence, make mistakes, or even “hallucinate” (generate false information). To improve AI accuracy, MCP (Model Context Protocol) provides monitoring and analytics tools that help track and refine AI performance.

Monitoring Dashboard

Key Insights

Comparison with Traditional AI Validation Tools

Step-by-Step Implementation with Examples

Step 1: Install MCP SDK and Set Up the MCP Context Manager

First, install the MCP SDK and initialize the MCP context manager:

Step 2: Retrieve Context and Attach it to AI Requests

Whenever a user sends a query, retrieve previous conversation history from MCP and enrich the query before sending it to the LLM.

Step 3: Send Enriched Prompt to LLMs (GPT-4, Claude, Gemini)

You can send the enriched query to any LLM (GPT-4, Claude, Gemini) by modifying the API request accordingly.

Using GPT-4 (OpenAI API)

Using Claude (Anthropic API)

Using Gemini (Google Generative AI API)

Step 4: Store the LLM Response in MCP for Future Context

After the LLM generates a response, store it in MCP for future interactions.

Step 5: Enable Cross-Platform AI Context Sharing

If multiple AI systems need access to user history (e.g., chatbot + CRM AI), MCP allows cross-platform context retrieval.

Develop Monitoring & Analytics Tools

• Build tools to collect and analyze MCP logs. Example:
  • Use Elasticsearch for log storage.
  • Use Kibana for visualization.
  • Monitor metrics like latency, accuracy, and confidence scores.

Step 1: Design Confidence-Aware Prompts

Step 2: Implement RAG with Confidence Assessment

Step 3: Configure LLM for Confidence Reporting

Step 4: Parse and Present Confidence Data

Step 5: Implement Monitoring and Feedback Loop

Example Use Case

Scenario: AI-Powered Customer Support

Response: The chatbot returns the validated response to the user.

The Model Context Protocol (MCP) is a game-changer for AI applications. It bridges the gap between one-time interactions and long-term memory, making AI more intelligent, human-like, and efficient.

Challenges & Best Practices in MCP Implementation

The Model Context Protocol (MCP) is a powerful way to make AI models smarter by helping them remember and understand context across interactions. However, like any technology, implementing MCP comes with challenges. In this guide, we’ll explore common hurdles and the best ways to overcome them.

1. Handling Multi-User Sessions Effectively

The Challenge:

When multiple users interact with an AI system at the same time, how does the AI keep track of who said what? If the AI mistakenly mixes up different users’ conversations, it could lead to confusing or incorrect responses.

For example, imagine a customer support chatbot where:

Best Practices to Solve This:

2. Managing Latency with Real-Time MCP Context Retrieval

The Challenge:

MCP requires storing and retrieving a lot of contextual information to generate accurate responses. If not optimized, this extra processing time can slow down responses, leading to latency issues.

For example, if a chatbot has to pull a long conversation history before answering, users may experience a noticeable delay between asking a question and getting a response.

Best Practices to Solve This:

3. Security Concerns in MCP-Integrated AI Models

The Challenge:

MCP stores conversation history and contextual data. This introduces security risks, such as:

Best Practices to Solve This:

4. Best Practices for Fine-Tuning MCP with LLMs

The Challenge:

LLMs (Large Language Models) like GPT-4, Claude, and Gemini are powerful, but fine-tuning them with MCP context requires careful handling. If done incorrectly, the AI might overfit to past conversations, generate irrelevant responses, or fail to balance old and new context.

Best Practices to Solve This:

Whether you’re building chatbots, AI assistants, or knowledge-based AI applications, integrating MCP with Prompt Engineering and RAG will create a more seamless, personalized, and smarter AI experience.

Implementing MCP in AI models is a game-changer, but it comes with challenges that need careful handling. By optimizing session management, improving latency, securing stored context, and fine-tuning LLMs properly, you can build AI systems that are faster, smarter, and safer.

Quick check on how MCP used

About the Author:

Ms. Rajni Singh
Big Data & AI Senior Manager,
Accenture 

Ms. Rajni Singh is a data and AI practitioner specializing in agentic AI systems that transform messy, multi-source data into accurate, safe decisions.

Ms. Rajni Singh expertise lies in LLMOps, RAG, LangGraph, and production-grade AI systems built with strong observability, governance, and cost/latency optimization.

Ms. Rajni Singh has led enterprise-scale GenAI programs across GCP and AWS, architecting Knowledge Layer frameworks (feature store + vector + graph) that improved accuracy by 25%, reduced cost 33%, and cut latency 23%.

Ms. Rajni Singh is the author of _Mastering Generative AI: Design Patterns, Frameworks, and Real-World Applications_ and has written over 80 technical publications on Medium, where Ms. Rajni Singh shares pragmatic insights on tracing agent reasoning, de-duplicating sources, and building guardrails that hold in production.

Ms. Rajni Singh is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/singh-rajni/details/certifications/

Ms. Rajni Singh can be contacted at :

LinkedIn | Medium | Blog | E-mail | Mobile : +91 9971798831

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Introduction

Across Germany, Austria, and Switzerland, the digital transformation journey of companies using SAP solutions has reached a decisive phase. The question for most organizations is no longer whether to transform, but how to do so efficiently and securely.

The 2025 valantic SAP study on SAP Transformation Trends in the DACH Region highlights how companies are navigating this shift. Migration to SAP S/4HANA, the move to cloud-based architectures, and the integration of artificial intelligence (AI) are driving new priorities.

While many firms began their transformation years ago, this year’s findings show a clear pivot toward hybrid migration models, private cloud infrastructures, and pragmatic AI adoption aimed at measurable business value.

SAP S/4HANA Migration – Where do Companies Stand ?

SAP S/4HANA continues to be the foundation of enterprise modernization. The study’s data reveals a dynamic landscape:

• 29 % of companies are currently planning their migration.
• 40 % are in the midst of implementation.
• 27 % have already completed their migration.
• Only 4 % have no migration plans at all.

This means that nearly every SAP using company in the DACH region is on the S/4HANA journey in one way or another. What varies is the pace, scope, and strategy.

Companies that have already completed the transition report improved data consistency and better integration with new-generation applications. Those still planning or executing their migration often cite resource availability and integration complexity as the main bottlenecks.

The transition is also creating new skill demands within IT departments. SAP experts capable of handling both traditional ECC systems and S/4HANA landscapes are in particularly high demand — and often in short supply.

Migration Strategies & Operating Models

The Rise of Hybrid Strategies

A major trend identified by the study is the strong shift toward hybrid migration strategies. In 2025, 57 % of companies prefer what many call an extended brownfield or Bluefield approach — combining the stability of existing systems with the innovation potential of new functionalities.

This hybrid model allows organizations to reuse proven business logic while selectively introducing modern modules and analytics capabilities. In contrast, the pure brownfield approach has declined dramatically — from 41 % in 2024 to just 10 % this year.

In practical terms, the hybrid path reflects a more balanced philosophy which is to evolve but not to disrupt. Rather than undertaking a risky full rebuild, companies are optimizing their landscapes step by step, reducing downtime and mitigating implementation risk.

Shifting Operating Models

The choice of infrastructure is equally telling. Private cloud environments have nearly doubled in popularity, jumping from 31 % to 58 %. At the same time, on-premises systems continue their steady decline — from 59 % last year to just 20 % today.

Public cloud adoption has reached 22 %, used primarily by companies in industries with standardized processes and lighter compliance requirements, such as retail or services.

The preference for the private cloud highlights a key regional characteristic: DACH enterprises value control and data sovereignty, particularly in highly regulated sectors such as manufacturing, healthcare, and finance.

Cloud Transformation – Promise and Practicality

Cloud transformation remains one of the most critical yet complex parts of the digital journey. The benefits like scalability, flexibility, cost transparency are clear. However, as the study reveals, companies continue to face a mix of technical, financial, and organizational challenges.

Main Barriers to Cloud Adoption

• 42 % cite cost as the top concern. While cloud pricing models promise pay-as-you-go efficiency, long-term costs can accumulate rapidly if not managed properly.
• 41 % express security and compliance concerns, particularly around data residency and GDPR obligations.
• 33 % highlight integration complexity and shortages of skilled cloud architects.

Legacy systems often add to these difficulties. Many enterprises operate hybrid environments where on-premises SAP modules must coexist with new cloud applications. This technical duality requires careful planning and often external expertise to ensure seamless connectivity and consistent governance.

The Balancing Act

Companies recognize the transformative potential of the cloud, but many still approach migration with caution. As one IT manager quoted in the study put it, “The cloud is not just a hosting decision; it’s an organizational change.”

In other words, cloud transformation is as much about processes and culture as it is about technology. Firms that combine technical migration with change management, retraining teams, revising workflows, and ensuring cross-department alignment are finding smoother adoption and faster ROI.

SAP Business Technology Platform (BTP): The Innovation Engine

The SAP Business Technology Platform (BTP) has undergone a quiet transformation of its own. Once seen primarily as a tool for integrating different SAP applications, it has now become a strategic enabler of data-driven innovation.

According to the research:

• 60 % of companies actively use SAP Analytics Cloud for dashboards and reporting.
• Use cases have shifted from integration (30 %) to analytics (60 %) and automation (40 %).
• The majority now view BTP as a foundation for AI and data intelligence initiatives.

From Middleware to Value Driver

In many organisations, BTP is no longer treated as “technical plumbing” but as an engine for business value. Through automation and analytics, it enables smarter decision-making and supports a faster innovation cycle.

Enterprises that have already invested in BTP are also leveraging it to standardize data models, automate reporting, and connect non-SAP applications into unified workflows. This holistic approach aligns IT strategy more closely with business objectives — a shift that’s redefining how CIOs measure success.

SAP Business Data Cloud (BDC) and the New Era of Data Management

If BTP is the innovation engine, the SAP Business Data Cloud (BDC) is quickly becoming the fuel that powers it. Awareness of BDC is already high, only 6 % of companies remain unfamiliar with the concept and adoption is rising fast.

Key Insights from the Study

• 54 % of IT managers view BDC as central to their future data management strategy.
• 49 % believe BDC will enhance generative AI capabilities across their organisations.
• 47 % expect BDC to simplify system integration and reduce data silos.

As generative AI tools become more integrated into enterprise workflows, structured and well-governed data foundations are critical. BDC’s promise lies in creating a single, trusted data layer that connects SAP and non-SAP systems, allowing companies to train and deploy AI models more effectively.

Forward-looking enterprises see this as the next step beyond basic cloud migration, a move toward data intelligence ecosystems capable of real-time insights and predictive analytics.

6AI Integration in SAP Systems – From Trend to Transformation

Artificial Intelligence has transitioned from buzzword to business driver. In the SAP ecosystem, AI is increasingly embedded across planning, analytics, and process automation.

Adoption and Focus Areas

• 29 % of companies already use AI within their SAP environment.
• Another 51 % plan to implement it in 2025.
• Leading focus areas include generative AI (48 %), AI Foundation (43 %), and AI for Planning (43 %).

Why Companies Are Investing

The top motivations include cost savings, faster decision cycles, and process efficiency. AI helps automate repetitive tasks, enhance predictive maintenance, and improve forecasting accuracy. For instance, manufacturers use AI-powered analytics to optimize production schedules, while retailers deploy generative AI to create personalized product descriptions at scale.

Challenges to Overcome

Still, challenges persist. 47 % of respondents cite IT security risks, and 36 % mention a lack of trust in AI outcomes. Data quality and governance remain foundational issues — without them, AI initiatives risk producing biased or unreliable results.

Nevertheless, the overall direction is clear: AI is becoming a core capability of enterprise systems, not an optional experiment.

Strategic Takeaways – Building Resilient Digital Foundations

From migration strategies to AI adoption, several clear themes emerge from this year’s study:

1. S/4HANA is now mainstream. Virtually every company has it on the roadmap.
2. Hybrid is the new standard. Flexible migration paths dominate over one-size-fits-all models.
3. Private cloud leads the way. It offers the ideal balance between agility and control.
4. Platforms are strategic assets. BTP and BDC are evolving into business enablers rather than technical afterthoughts.
5. AI is moving from pilot to production. The focus is on business value, not experimentation.
6. Execution challenges remain. Cost management, security, and talent shortages are still major hurdles.

In short, DACH companies are approaching digital transformation with pragmatism and precision. They are no longer chasing hype cycles but investing in sustainable architectures that will serve their business goals for the next decade.

What to Watch in 2026 and Beyond

The next year will likely bring even deeper changes. Companies should watch for:

• Acceleration of AI integration within SAP modules, especially in finance, HR, and supply chain management.
• Increased convergence of BTP and BDC for unified data access.
• Expansion of private-cloud services tailored to industry needs.
• Talent shortages becoming a decisive factor in transformation speed.
• Stronger regulatory frameworks around data privacy and AI ethics, requiring companies to adapt governance models.

2026 could become the year when digital maturity becomes a competitive differentiator rather than a long-term goal. Those with flexible architectures and clear data strategies will have a significant head start.

Conclusion

The 2025 valantic SAP study paints a picture of a region in motion. SAP-using companies in Germany, Austria, and Switzerland are embracing transformation with a focus on balance — between innovation and reliability, between speed and security, between ambition and feasibility.

Hybrid migrations, private clouds, and intelligent data platforms like BTP and BDC are redefining how enterprises modernize. Meanwhile, the integration of AI marks a decisive step toward a more autonomous, data-driven future.

As one CIO in the study aptly summarized: “We are no longer transforming for technology’s sake. We are transforming for resilience.”

In the coming years, this mindset will distinguish the leaders from the laggards in the SAP ecosystem — and shape the next chapter of digital transformation across the DACH region.

Take a look at the full report here :

https://www.valantic.com/en/research/sap-study-2025/

For more details/ data please contact :

Ms. Nina Winkels

Ms. Nina Winkels can be contacted at:

LinkedIn | E-mail 

About Valantic :

valantic is Number 1 for digital transformation and one of the leading digital solutions, consulting, and software companies on the market.

valantic combines technological expertise with industry knowledge and the human touch.

More than 500 blue chip clients have placed their trust in valantic, including 32 of 40 DAX-listed companies, many large Swiss and Austrian companies, and many prominent medium-sized companies.

With more than 4000 specialized solution consultants and developers and sales of around EUR 600 million in 2024(e), valantic is represented in numerous branches in Germany, Austria and Switzerland as well as other international locations.

valantic features a unique structure with competence centers and expert teams – always precisely tailored to companies’ digitization needs, from strategy to tangible realization. The range of services includes Customer Experience, Digital Strategy & Analytics, Financial Services Automation, SAP Services, and Smart Industries & Industry 4.0.

valantic can be contacted at:

LinkedIn | Website

The post SAP Transformation Trends 2025 appeared first on Industry4o.com.

Take Over and Hand Over Dilemmas cannot be over emphasised. As an Intrapreneur, I have always believed in setting up fool proof processes – meaning people agnostic business processes. And I have also believed, an Organisation is only good to the extent People become replaceable.

However, in reality, it completely rests on the person who takes over than the one who hands over.

Many a times when someone hands over, especially the successes and good practices, he does it with a lot of empathy and emotion trusting the person and people who take over. Unfortunately, when priorities & motives are different, successful processes give away to mediocrity.

It’s also many a time, we believe it’s good to have someone internal to take over – as part of grooming. This will again completely depend on the ability of the Person taking over to adapt. This is especially when the taking over person is not able to wriggle of personal biases and prejudices. Mostly due to long association with the Same Organisation & People, and lack of adequate exposure to outside world. It’s not easy for a Good Person to become a Good Professional.

Preferential treatments, diminishing abilities to question, favouritism and ofcourse corruption and compliance issues return. It’s more so in an MNC set up, being remotely managed. It makes one wonder who influences whom and trust deficit reappears.

Wish Integrity and Ethics become the Soul of both People or Organisation. Though it sounds philosophical, looks like these qualities are becoming rearer…as many of us being witnesses to such happenings. It matters very little, whether the transition is within the Family or between professionals. It only justifies a New Leader is always a better choice – atleast for few more years.

It’s back to basics – Leaders are Born, not Made…20years should be the maximum one should be in an organisation, after which the 4Es – Excitement, Enthusiasm, Efficiency & Effectiveness Drops due to lack of ability & willingness to think differently. Focus shifts to maintaining status quo than leading & transforming. Thus a new external leader will be more helpful to build the next phase of the Organisation.

About the Author :

Dr. NITHYANANDAN DEVARAAJ (NITHIN) BE., MBA., Ph.D
President – Coimbatore Management Association
Past National Secretary – The Institute of Indian Foundrymen.
Chairman – Shri Avinashiappar Trust
Past Managing Director & CEO – Flow Link Systems (P) Ltd ( a Voith Group Company)

Dr. Nithin has about 35 + Years of Experience in Engineering Manufacturing organizations. He has worked for LMW Group, L&T, Hitachi & CRI Group. He is considered to be a Professional with a rare blend of Intrapreneurial Orientation, having been a Business Head across Engineering Manufacturing Organisations / Industries.

Dr. Nithin is an Intrapreneur with hands on experience establishing medium & large scale end to end Value Chain Manufacturing Organizations (from Castings to Finished Products) for Indian Corporates and MNCs, driving Growth & Profitability by Building, Sustaining and Growing High Performance Teams.

Dr. Nithin is a Member in Governing Council / Board Of Studies of Universities and Management Institutions as a Management Expert.

Dr. Nithin is part of various industrial associations and professional bodies; he is credited with playing a leading role in bringing land mark changes in Export Incentives, Classification of MSMEs, CPCB Norms, Waste & Disposal Management. He also plays a pivotal role in training and development of educated Youth seeking employment opportunities.

Dr. Nithin Leads a team that turned around a division and a Company, and in establishing large scale projects as bench marks in industry (This Business has won several awards at National & Regional Level which includes – Indian Foundry of the Year, Young Foundryman and Foundryman Awards, 4Star Award from CII, Green Award & Safety Award from Tamil Nadu Government & at National Level from IIF, First Indian Foundry in the segment to get OHSAS 18001 & ISO 50001 Certification and Many Kaizen awards.)

Dr. Nithin is a Managing Trustee of a Family Managed Trust involved in activities like rendering Food, Shelter, Education and Medicare. Noted for rendering services during Chennai, Kerala, Tanjore & Nilgiris Floods.

Dr. Nithin’s Academics:

• Ph.D in Management Sciences
• MBA in Finance & Marketing – Gold Medalist and University Topper
• BE Mechanical Engineer with Distinction
• An IRCA certified lead auditor and assessor for ISO 9001.
• A qualified auditor for EMS, OHSAS & EnMS
• L&T Future Leaders Training at IIM – C

Dr. Nithin’s Professional Associations:

• Member & Industry Nominee, Coimbatore Disaster Management Council
• A Guest Faculty to Premier Management Institutes & Was  Visiting Faculty to IIM – Calicut
• An examiner for Management Qualifications & Assessor for Management Teachers
• An acknowledged management trainer and consultant
• Is a regular Business Column Writer.

Dr. Nithin is a Member of:

• Foundry Development Council, Ministry of Commerce & Industries, India.
• Institution of Engineers, All India and Coimbatore Management Association
• Board of Studies in Universities & Premier Management Institutes

Dr. Nithin is Winner of:

• The Outstanding Alumni & Distinguished Alumni Awards from PSG Institutions & Bharathiar University.
• The CMA Golden Jubilee Year Best Manager award for the region.
• The Best Management Student award and many accolades at National Level.
• The Mother Teresa Award of Excellence for Young Business Managers.

Dr. Nithin’s Management Interests:

• Management of Innovation, Corporate Strategic Planning and Manufacturing Management, Green Supply Chain

Dr. Nithin can be contacted at :

Mobile : +9194879 46900

LinkedIn | Twitter | Facebook | E-mail | E-mail2

Also read Dr. NITHYANANDAN DEVARAAJ’s earlier articles:

The post The Travails of a Change Over appeared first on Industry4o.com.

How fragmented customer data stalls growth-and the playbook that turned chaos into 10X ROI.

When I joined the data leadership team at a major fintech, our biggest challenge wasn’t a lack of data—it was the opposite. Customer data was everywhere and nowhere, fragmented across disparate pods and tables. The journey from that chaos to real-time activation was complex, but the results were transformative, and the blueprint is one I’ve seen work across both financial services and any CPG or retail organization.

The Silos That Slowed Us Down

Our customer information was spread across at least five major systems: Marketing systems and MarTech, product analytics tables, CRM, Datawarehouse, and various engagement platforms. Each team—marketing, product, risk, and data science—was looking at their own slice of the customer, resulting in inconsistent profiles, conflicting priorities, and disconnected personalization efforts.

Marketing would target a user based on campaign clicks, Lifecycle PODS will send 5 differnt email campaigns on the same daay, while the risk team flagged the same user for a behavior product analytics hadn’t even seen yet. The customer journey was a broken mirror, reflecting a different reality for every team. The cost was clear: high acquisition costs, frustrated users, and missed revenue opportunities.

Key Bottlenecks and the Buy-In Needed

The major bottleneck was both technical and cultural.

Technical : Our identity resolution was weak. We couldn’t confidently stitch together a user’s journey from anonymous web visit to logged-in app activity without creating duplicates or privacy risks. We also had a challenge of more than one legal entity, so combining identity was a major bottleneck as the data needed to be unified in a consistent manner.

Cultural : Teams were protective of their data and tools. There was no shared playbook or single source of truth, so marketing and product couldn’t fully trust the data they were given to activate. Being a digitally native organization every pod and team was enabled with data capabilities, which created a challenge for adoption of central infra.

A critical breakthrough came when we secured executive sponsorship including the CEO, who became strong supporter of the capability. We stopped talking about “data projects” and started framing the initiative around a unified customer experience, with shared KPIs for both tech and marketing. This aligned incentives toward building a single, trusted view of the customer. This drove consistent experiences for the customers.

Tactical Steps That Worked

With executive buy-in, we took three concrete steps:

1. Implemented Enterprise CDP Architecture: This Enterprise CDP Architecture enabled us to handle the realtime Data, non-realtime data, it helped us to create User 360 and also enable personalization for digital marketing. This allowed us to link real-time event streams from our app, lead gen business and website directly with our consented identity graph fast database and data warehouse. This approach was more flexible, cost-effective, and reduced data latency from days to minutes.

2. Prioritize the Identity Resolution:We invested heavily in building and maintaining a robust, privacy-compliant identity graph. This became our “single source of truth” for customer data, improving our trusted data coverage to over 85% of our user base. The Identity resolution helped us to address the use cases where we needed very high confidence (deterministic) also address use cases where a probability match was acceptable.

Identity Resolution for Financial Services and D2C Organizations

3. Redefine the Activation Journey: We mapped out the entire customer journey and identified key activation points where real-time data could make a difference—like personalizing an offer at the moment of intent or signup, sending an email with potential offer the user has received near realtime.

Board-Level ROI and Impact

The results were fast and significant. Within 12 months of implementing this blueprint:

Equally important, the board began to see transparent dashboards tied directly to these KPIs. Data activation was no longer a vague “tech project” but a core business capability driving measurable growth.

Conclusion

Enterprise data activation is more than just technology. Its an active strategy rooted in executive sponsorship, cross-team alignment, a commitment to data governance and privacy, and a relentless focus on board-visible ROI. This blueprint of unifying data around a trusted identity core and enabling real-time activation can be adapted to any sector looking to transform fragmented data into a revenue-driving engine and growth.

About the Author:

Mr. Amit Kurhekar
Fractional Chief Data & Digital Officer
TransformTechX

Mr. Amit Kurhekar drive $100M+ enterprise growth by turning data into boardroom revenue.

Mr. Amit Kurhekar with 20+ years across Data, AI, and Digital Strategy, he has led transformation at global scale—working with CEOs, CTOs, and product leaders to turn fragmented data into intelligent growth engines.

At MoneyLion as Head of Data & AI Solutions (reporting to the Global CTO), Mr. Amit Kurhekar led the CEO-sponsored Enterprise CDP and User 360 strategy—the foundational layer for our digital marketing, CX, and GenAI ambitions. This initiative:

– Delivered 10X uplift in revenue through personalization and full-funnel automation
– Enabled 2M new user acquisitions at the lowest CAC in our category
– Achieved a 5X reduction in CAC via ML-powered audience segmentation and Martech integration

Before that, at Yodlee, Mr. Amit Kurhekar built a scalable Financial Data Enrichment Platform using ML/DL techniques, enabling AI-driven personalization for global financial institutions.

Mr. Amit Kurhekar led the deployment of several AI/ML solutions—spanning peer benchmarking, credit ready data, and smart categorisation—driving over $8M in measurable value across products and partnerships.

Mr. Amit Kurhekar worked with CPG giant Procter & Gamble for over a decade.

At P&G Mr. Amit Kurhekar pioneered Industry 4.0 AI/ML adoption in manufacturing. In 2017, Mr. Amit Kurhekar piloted one of the industry’s first prognostics ML solutions on the shop floor – improving asset availability from ~60% to 85% (+25%). This made a financial impact of $15M

Through this cross-industry lens, Mr. Amit Kurhekar operate at the intersection of CDO, CAIO, and CTO mandates- blending architecture, governance, and growth strategy into scalable impact.

Today, Mr. Amit Kurhekar share his frameworks and field-tested playbooks through TransformTechX – a content ecosystem for leaders building the next generation of AI-powered enterprises.

Mr. Amit Kurhekar‘s Core Skills:

Data Strategy | Generative AI | AI/ML Architecture | Digital Marketing | Cloud Platforms (AWS, Snowflake) | Martech | | AI Governance | Executive Stakeholder Management

Mr. Amit Kurhekar is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/amitkurhekar-cdo/details/certifications/

Mr. Amit Kurhekar is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/amitkurhekar-cdo/details/honors/

Mr. Amit Kurhekar can be Contacted at:

LinkedIn | Official E-mail | Personal E-mail | Blog | Mobile : +601136271953

To Book a 20‑min CDO Audit : https://topmate.io/amitkurhekar/442726

The post Data Bottleneck appeared first on Industry4o.com.

This article appears in two parts. Part One lays the groundwork and core principles; Part Two moves into narrative craft, delivery, and what the future holds.

Picture the scene: the Circus Maximus on a sweltering afternoon, grit in the air and a restless hush before the breakneck charge. A chariot team snaps forward. The crowd erupts. In the middle of that roar, there’s a name that once rattled across Rome: Gaius Appuleius Diocles. He raced, and he endured. For 24 years he tilted around those turning posts, surviving a career that often ended boys before it made men, and he retired with numbers that still pull the eye: an inscription records total winnings of 35,863,120 sesterces. Historians trying to translate that into today’s terms land all over the map, with estimates frequently cited in the tens of billions of dollars, a spread that reflects just how thorny it is to price the ancient world in modern money. Depending on which yardstick you use (wheat prices, soldier pay, grain for the city) the picture shifts, and with it the story.

That’s the puzzle, isn’t it? Numbers alone can be cold. They’re rows in a ledger until someone shows us what they mean. Data storytelling is the craft of turning those rows into meaning, linking facts with narrative and visuals so people can understand, remember, and act—technical experts and non-specialists alike. Done well, it moves beyond reporting an outcome and invites the audience to see what matters and why.

Start with memory. Why does Diocles stick? Not just because the sum is huge, but because we can feel the stakes; that is, the dust, the danger, the near misses, the mass of people whose fortunes rose and fell with every lap. Story is the glue. If the inscription were only a number, it would read like a bookkeeping entry. Framed through a life lived on a knife’s edge, the number acquires weight. In business, the same principle applies: a quarterly report that strings together figures without context fades from mind; a narrative that shows which customers moved, why a region sagged, what bets paid off, and what to try next stays with readers and shapes decisions. Narrative makes data sticky and shared, the kind people can recall in the elevator after the meeting.

The second thread is perspective. Those wildly different conversions of Diocles’ fortune reflect different lenses; different choices about how to compare purchasing power across an empire and two thousand years. If your baseline is grain for Rome or pay for a legion, you end up with very different modern equivalents. Context reframes the whole picture. It works the same way with a modern dashboard: a sudden dip in sales might look catastrophic in isolation, but add seasonality, a competitor’s launch, or a supply hiccup, and the narrative changes shape. Context is the difference between alarm and insight.

Third, scale. Billions of anything (be they dollars, sesterces, or page views) are hard to feel. The attempts to translate Diocles’ winnings into today’s money are really attempts to make scale graspable: think in lunch rations, soldier salaries, or the grain needs of a city, and the mind finds purchase. With modern datasets, the same move works. Strip away what’s not useful, select the few things that are, and turn abstractions into something people can visualize and compare. Visuals are your ally here. Charts and diagrams serve as cognitive scaffolding that let patterns and exceptions show themselves.

Finally, action. Even the inscription that immortalized Diocles was a kind of public performance, a message carved in stone. It said: look what this man achieved. It must have lit sparks in the heads of young drivers and fans, shaping ambition and allegiance. That’s what we want from data stories: for people to do something because they now see clearly. A good story closes with a concrete next step and an implied “why now,” nudging a roomful of stakeholders from nodding along to moving their budgets and calendars.

The Fabric of a Data Story: how numbers become a narrative that moves people

Building a strong data story blends analysis with craft. It starts long before you open your slide deck, and it doesn’t end at the last chart.

Purpose and audience

Begin with the point. What question are you trying to answer or decision you want to affect? “Increase market share,” “reduce churn,” “shorten the onboarding cycle”—each aims you at different data and a different arc. With Diocles, the “question” could be: what made him extraordinary, and how do we measure it? The framing shapes the evidence you collect and how you talk about it.Then consider who’s listening. Executives need strategic clarity and the one or two levers that matter; marketing teams often want customer stories, channel insights, and the “so what” for campaigns; technical teams care about method and caveats. Tune the voice, the level of detail, and the visuals to each audience so they can follow along and care enough to act.

Finding the signal

Once you’re clear on purpose and audience, go to the data. Clean it, join it, stress-test it. You’re looking for the few things that explain most of the outcome: a trend, a break, a relationship you didn’t expect, an outlier that reframes the rest. Think like a reporter with a beat—curious, a little skeptical, stubborn about the truth.

With Diocles, that “beat” might include his races and finishes, the teams he drove for, the payouts, and the length of his career; 24 years, which was unusually long for such a dangerous sport. He raced in more than four thousand events and won 1,462 times, often surging late to take the lead, a tactic that thrilled crowds and built his legend. Those facts guide the story you’d tell about his skill, durability, and showmanship. With those pieces in place, this is a natural pause before we move from discovery into the making of a story that holds together in the room.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is a truly exceptional professional who has left an indelible mark on the IT, health and human services, and higher education sectors. His journey began in the field of education, where he laid the foundation for his career. With advanced degrees in education and a doctorate that included a double minor in computers and theology, Joe embarked on a path that ultimately led him to the dynamic world of data-driven Information Technology.

In the early 1990s, he transitioned into IT, starting as a Computer Consultant at NC State University. Over the years, his dedication and expertise led to a series of well-deserved promotions, culminating in his role as Business Intelligence Specialist that capped his 25 successful years at NC State. Not one to rest on his laurels, Dr. Perez embarked on a new challenge in the fall of 2017, when he was recruited to take on the role of Senior Business Analyst at the NC Department of Health & Human Services (DHHS). His impressive journey continued with promotions to Senior Systems Analyst and Team Leader, showcasing his versatility and leadership capabilities.

In addition to his full-time responsibilities at DHHS, Joe assumed the role of fractional Chief Technology Officer at a North Carolina corporation in October 2020. A top-ranked published author with over 18,000 followers on LinkedIn and numerous professional certifications, he is a highly sought-after international keynote speaker, a recognized expert in data analytics and visualization, and a specialist in efficiency and process improvement.

Dr. Perez’s contributions have not gone unnoticed. He is a recipient of the IOT Industry Insights 2021 Thought Leader of the Year award and has been acknowledged as a LinkedIn Top Voice in multiple topics. He holds memberships in prestigious Thought Leader communities at Gartner, Coruzant Technologies, DataManagementU, Engatica, the Global AI Hub, and Thinkers360 (where he achieved overall Top 20 Thought Leader 2023 ranking in both Analytics and Big Data). His reach extends to more than twenty countries worldwide, where he impacts thousands through his speaking engagements.

Beyond his professional achievements, Joe’s passion for teaching remains undiminished. Whether as a speaker, workshop facilitator, podcast guest, conference emcee, or team leader, he continually inspires individuals to strive for excellence. He treasures his time with his family and is a gifted musician, singer, pianist, and composer. Joe also dedicates his skills as a speaker, interpreter, and music director to his church’s Hispanic ministry. He manages the publication of a widely recognized monthly military newsletter, The Patriot News, and is deeply committed to his community.

To maintain a balanced life, Perez is a regular at the gym, and he finds relaxation in watching Star Trek reruns. He lives by the philosophy that innovation is the key to progress, and he approaches each day with boundless energy and an unwavering commitment to excellence. His journey is a testament to the remarkable achievements of a truly exceptional individual.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

The post Data Storytelling – Part 1 appeared first on Industry4o.com.

Unlock the power of Generative AI by learning to interact with large language models programmatically—and take it further with fine-tuning for customized responses.

If you’re a software maker feeling the fear of missing out (FOMO) on the Gen AI revolution, you’re not alone. The way we build and interact with software is evolving rapidly. “Google it” or “check Stack Overflow” is no longer the go-to approach — AI-first, conversational tools are becoming the new standard across development, design, and decision-making. Simply using tools like ChatGPT or Gemini as chatbots isn’t enough anymore. When even the biggest tech paradigms are being disrupted, what does that mean for traditional development roles?

Here’s the good news: it’s not too late to get started — and this guide will take you from FOMO to flow, introducing you to Gen AI concepts and showing you how to start building with confidence.

Let’s start by exploring the gen AI architecture that brings it all Gen AI concept together, and then we will get into developing Gen AI applications and practice these core concepts.

Overview of Gen AI Architecture

More than a chatbot — building blocks of intelligent, context-aware applications

You’ve probably heard about LLMs — Large Language Models — floating around in conversations, headlines, or team meetings. They’re trained on gigantic amounts of text (imagine reading everything on Facebook, X, LinkedIn, and more), which helps them understand and generate human-like responses. Ask the right question (a.k.a. a prompt), and boom — you get a surprisingly good answer.

On their own, LLMs are a bit like goldfish.

They don’t remember what happened a moment ago, they don’t keep track of ongoing conversations or your specific context between interactions.

To build smart, conversational, and personalized Gen AI applications, you need more than just an LLM. You need a supporting architecture — a set of layers that provide memory, knowledge, and prompt optimization to help the AI feel more intelligent and useful. Here’s a quick look:

1. User Interface (Front End)

This is where the magic starts — from the user’s point of view. It might be a chat window, voice assistant, or a smart feature inside your app. The frontend captures input (text, images, voice) and displays responses in a way that suits your use case.

In tools like ChatGPT, users are expected to write clear prompts. That’s because LLMs — though incredibly knowledgeable — can’t read your mind. They respond best when you ask precisely. This is often called Prompt Engineering, but honestly, it’s more of a Prompting Art — knowing how to talk to the model so it truly understands what you want.

In business apps, the frontend may also help shape or enhance user input behind the scenes — so even vague questions become high-quality prompts before reaching the application layer.

2. Application Logic (Backend)

This is the brain behind the scenes. The backend handles user input, talks to the LLM, manages memory, adds relevant context, and delivers meaningful responses. Think of it as your app’s AI concierge, organizing everything to make the magic happen smoothly.

Here are the key component of application logic:

• Memory System – LLMs don’t remember anything between interactions. To make conversations feel natural, your app needs to manage memory. A memory system stores previous interactions and sends important parts to the LLM as context with every prompt, so it can respond more intelligently — like remembering what the user asked earlier.
• Knowledge Base – LLMs are trained on general data — they might not know about you or or example about your company’s policies, product details, or niche topics. That’s where a Knowledge Base comes in.

This system retrieve relevant info from your own data (like FAQs, documents, or databases) and augment it to the user prompt before sending it to the LLM. This process is called Retrieval-Augmented Generation (RAG).

• The data is stored in a vector database, where each document/content is represented by a mathematical fingerprint (called an embedding).
• When a user asks something, we compare their query’s embedding to the stored embeddings (using similarity search) and fetch the most relevant content.
• This content is then injected into the prompt, giving the LLM exactly what it needs to give better, grounded answers.

Together, memory and knowledge retrieval make your Gen AI app feel smarter, more helpful, and deeply customized to your domain.

3. Large Language Models (LLMs)

At the heart of Gen AI applications are large language models like GPT, trained on massive datasets to understand and generate human-like language. These models are available through platforms such as Hugging Face, OpenAI, and others.

Fine-Tuning: A method to adapt a general-purpose, pre-trained LLM for specific business needs. Fine-tuning adjusts the model using domain-specific data, improving relevance and performance for targeted applications.

Some time we need to implement all of these concepts to achieve better result as per our business need. Next will develop all these concepts step by step.

Developing Gen AI Application

You might be surprised how easy it is to build your first Gen AI app. The best part? You don’t need to train your own LLM. Big players like OpenAI, Google, Anthropic, and Hugging Face already provide powerful models that you can use through simple APIs.

In this example, we’ll use Google’s Gemini API, which is still free within usage limits — perfect for trying things out.

Setup LLM APIs Access

Accessing Gen AI Model Programmatically

For simplicity, I’m using a Google Colab Notebook — no setup needed beyond your browser! But feel free to try this on your local machine too.

The fun part? You can even use Gen AI to help you write Gen AI code. Learning by doing the same in collab or in your favorite tool — what’s cooler than that?

Developing First Gen AI Application — Colab Exercise

from google import genai
from google.colab import userdata

def query_gemini(prompt):
    client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))

    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=[prompt]
    )
    return response.text

prompt = "Tell me about Kuldeep"

print(query_gemini(prompt))

So, what happens here?

I ask the Gemini model: “Tell me about Kuldeep.” And it tries its best. But wait… the response might be generic. Something like:

“Kuldeep is a common name in India…” need more information…

That’s not what you were hoping for, right?

That’s because LLMs don’t know which Kuldeep you mean unless you give them more context.

This is where conversation and memory layers come into play. Let’s explore how to have a more meaningful interaction with the model in the next example by providing additional background during the conversation.

Conversation for LLM

What you get from a Gen AI model heavily depends on how you ask. The better your prompt, the better the answer. This is where techniques like giving the model a persona, adding context, or showing examples come in — these are called one-shot or few-shot prompting.

Sometimes, a single prompt isn’t enough. Just like in real conversations, you might need to go back and forth, ask follow-ups, or break things down into steps. This is where ideas like chain of thought or tree of thought prompting help — guiding the AI step by step to a better outcome.

To have a proper conversation with an LLM we need implement memory component to store the conversations, and pass on with every conversation. And the good news? Gemini makes this easy with its built-in chat API that remembers what you’ve said.

Check out the code notebook here:Developing a Chatbot with Gemini

Here’s a basic chatbot python program with memory:

from google import genai
from google.colab import userdata

client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))
chat = client.chats.create(model="gemini-2.0-flash")

def run_chatbot():
    print("Hello! I am thinkuldeep, how can I help you! Type 'quit' to exit.")
    chat_history = []

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            print("thinkuldeep: Goodbye!")
            break

        response = chat.send_message(user_input)
        print(f"thinkuldeep: {response.text}")

# Start the chatbot
run_chatbot()

Sample Conversation:

Hello! I am thinkuldeep, how can I help you! Type 'quit' to exit.
You: tell me about kuldeep  
thinkuldeep: "Kuldeep" is a common name… can you give me more context?

You: he works at thoughtworks and wrote "Exploring the Metaverse"  
thinkuldeep: Ah, you're likely referring to Kuldeep Singh… [gives useful details]

You: book title is "Exploring the Metaverse: Redefining the Reality in Digital Age"  
thinkuldeep: Thanks! That helps narrow it down…

As you can see, adding a bit more information helped the model give a more relevant answer. But it still can’t tell us, say, his contact info or specifics that aren’t popularly available in its training data.

In the next example, we’ll bring in custom knowledge and pass that knowledge to LLMs to get better response.

Implementing Basic RAG

Large Language Models (LLMs) like Gemini are powerful, but they aren’t all-knowing. They might not have the latest information about specific people, use cases, or private knowledge.

That’s where RAG comes in — Retrieval-Augmented Generation is a technique that fetches relevant information from an external source (like a website or document) and augments the prompt with this context before sending it to the LLM.

Let’s walk through a simple RAG implementation that pulls data from Kuldeep’s About page and uses it to generate accurate responses.

Notebook: Implementing Basic RAG

1: Retrieve

Pull relevant information from the source page:

def retrive(url):
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.content, "html.parser")
        text_content = ""
        for paragraph in soup.find_all("p"):
            text_content += paragraph.get_text(strip=True) + "\n"
        return text_content
    except Exception as e:
        return f"An error occurred: {e}"

2: Augment

Inject the retrieved context into the prompt:

def augment(prompt, context):
    return f"{prompt} where context is {context}"

3: Generate

Send the augmented prompt to Gemini:

def generation(prompt):
    client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=[prompt]
    )
    return response.text

4: RAG in Action

prompt = "Tell me about Kuldeep"
context = retrive("https://thinkuldeep.com/about/")
improved_prompt = augment(prompt, context)
print(generation(improved_prompt))

That’s it! You’ll get a detailed response based on the real data from Kuldeep’s website. The second part of the same notebook integrates it in our chatbot example to initialize the context before chatting, and we get all accurate response in natural language.

Here’s a glimpse of how that interaction goes:

Hello! Ask me about Kuldeep Singh! Type 'quit' to exit.

You: how to contact him  
thinkuldeep: Based on the provided info, you can reach Kuldeep via:
-  thinkuldeep@gmail.com
-  kuldeeps@thoughtworks.com
-  LinkedIn: kuldeep-reck
-  X (Twitter): thinkuldeep
-  Instagram: thinkuldeep
-  Facebook: kuldeep.reck

You: where does he work  
thinkuldeep: He currently works at Thoughtworks as a Global Emerging Technology Leader and Principal Consultant.

You: does he write books  
thinkuldeep: Yes! Kuldeep is the author of *"Exploring the Metaverse"* and *"MyThoughtworkings"*, and has contributed to others as well.

You: what is his next book  
thinkuldeep: His upcoming book is titled *"Jagjivan – Living Larger than Life"*.

You: quit  
thinkuldeep: Goodbye!

So we have seen RAG approach can give accurate, contextual answers by pulling in external information. This approach require all the contextual information to be passed along with the prompt, and that’s not practical and costly affair when using hosted LLMs. We need to augment the prompt with selected and most relevant context. Let’s understand it more in next section.

RAG using Embeddings and Similarity Search

As explained earlier, embeddings are numerical representations of text that capture semantic meaning. When we generate embeddings for all our contextual data and store them in a vector database, we create a searchable knowledge base.

These embeddings can then be compared to the user’s query using similarity metrics — like cosine similarity — to find the most relevant content. Once identified, that content can be appended to the user’s prompt to provide a more accurate and context-aware response from the LLM.

Let’s walk through how this works using Gemini APIs and a simple example. — RAG Using Embeddings and Similarity Search — GitHub

1: Create Embeddings and Build the Knowledge Base

Let’s start by building a small dataset of page titles and URLs from thinkuldeep.com, and then generate embeddings using Gemini APIs.

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))

model = 'models/embedding-001'

def pageEmbeddings(title, text):
  return genai.embed_content(model=model,
                             content=text,
                             task_type="retrieval_document",
                             title=title)["embedding"]
def knowledgeBase():
  pages = [ 
   {"title": "Open-sources and Community Applications", "content": "https://thinkuldeep.com/about/open-sources/"},
   {"title": "Live Streaming, Webinars","content": "https://thinkuldeep.com/about/streaming/"},
   {"title": "Patents Granted","content": "https://thinkuldeep.com/about/patents/"},
   {"title": "Awards received and public coverage", "content": "https://thinkuldeep.com/about/recognitions/"},
   {"title": "Me, my family and some moments, travel and trips", "content": "https://thinkuldeep.com/about/moments/"},
   { "title": "Book foreword, reviewed and authored", "content": "https://thinkuldeep.com/about/books/"}
  ]

  df = pd.DataFrame(pages)
  df.columns = ['Title', 'Url']
  df['Embeddings'] = df.apply(lambda row: pageEmbeddings(row['Title'], row['Url']), axis=1)
  return df

This creates a dataframe where each row contains a title, URL, and its corresponding embedding vector.

2: Find the Best Match with Similarity Search

Now, we search for the most relevant page based on the user’s input by comparing embedding similarity.

def findBestPage(query, dataframe):
  query_embedding = genai.embed_content(model=model,
                                        content=query,
                                        task_type="retrieval_query")
  dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding["embedding"])
  idx = np.argmax(dot_products)
  return dataframe.iloc[idx]['Url']

prompt = "Tell me in brief Kuldeep's travels"

bestPage = findBestPage(prompt, knowledgeBase())

print(bestPage)
# Output: https://thinkuldeep.com/about/moments/

3: Implementing RAG with Context Augmentation

Now let’s use this best-matched page as additional context when generating a response.

def run_chatbot():
    print("Hello! ask me about Kuldeep Singh! Type 'quit' to exit.")
    generation(augment("", retrive("https://thinkuldeep.com/about/")))
    df = knowledgeBase();

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            print("thinkuldeep: Goodbye!")
            break
        bestPageMatching = findBestPage(user_input, df)
        response = generation(augment(user_input, retrive(bestPageMatching)))
        print(f"thinkuldeep: {response.text} \n\n")

run_chatbot()

This chatbot dynamically retrieves and appends the most relevant context to the user’s query using similarity search and embeddings, resulting in richer, more accurate responses.

Fine-Tuning

So far, we’ve worked with general-purpose LLMs using prompt engineering to steer their behavior. However, no matter how well we craft our prompts, public APIs and general models may still fall short of producing responses that align exactly with our desired tone, format, or accuracy.

That’s where fine-tuning comes into play.

Fine-tuning allows us to train a model on our own dataset, enabling it to respond in the specific way we want.

Below is a basic example of fine-tuning a Gemini model using Google’s Generative AI tools.

Link to the full notebook on GitHub

import google.generativeai as genai
from google.colab import userdata 
import time

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))

basemodel = "models/gemini-1.5-flash-001-tuning"

trainingdata = [
    {"text_input": "Who is Kuldeep", "output": "Hi, Thanks for Asking,  Kuldeep Singh is a computer engineer with 20 years of IT experience. All the best - thinkuldeep.com"},
    {"text_input": "Where does Kuldeep Work", "output": "Hi, Thanks for Asking, He works at Thoughtworks. All the best - thinkuldeep.com"},
    # ... more training examples ...
]
operation = genai.create_tuned_model(
    display_name="thinkuldeep",
    source_model=basemodel,
    epoch_count=5,
    batch_size=4,
    learning_rate=0.001,
    training_data=trainingdata,
)

for status in operation.wait_bar():
    time.sleep(10)

print(operation.result())

This would create a fine-tuned model something like this : tunedModels/thinkuldeep-1bja29uyplkib4xx1pcfer0yjhf5

Once created, we can use it like any other generative model:

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))
model = genai.GenerativeModel(model_name="tunedModels/thinkuldeep-1bja29uyplkib4xx1pcfer0yjhf5")
prompt = "Tell me about Kuldeep"
print(model.generate_content(prompt).text)

Hi, Thanks for Asking. He is a passionate AI engineer. All the best - thinkuldeep.com

Even with a very limited dataset, the model adapts to the style we trained it for — consistently using a polite opening and closing format. That’s the power of fine-tuning. Of course, real-world scenarios would require much larger and more diverse datasets, along with proper evaluation and validation processes.

Conclusion

We’ve now walked through several core concepts of Generative AI — from prompt engineering to embeddings, vector search, function calling, and finally, fine-tuning. These foundational techniques open up incredible opportunities for building intelligent, interactive, and personalized AI systems.

That said, this is just the beginning. The field of GenAI is vast and evolving rapidly. As we move forward, we must also recognize the ethical responsibilities that come with it. Building responsible, safe, and trustworthy AI requires:

• Transparent usage of data,
• Adherence to privacy standards,
• Thoughtful model alignment,
• And awareness of hallucination and misuse risks.

Always be cautious about the data you feed into prompts – it may inadvertently become part of training sets and surface in future responses. The industry still faces challenges in setting strong standards and guardrails for responsible use.

Let’s keep exploring, learning, and sharing as we shape the future of intelligent systems.

All the best — thinkuldeep.com

A variation of this article is originally published at AI Practices Publication

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Mr. Kuldeep Singh is a seasoned technology professional with a rich background spanning nearly two decades, where he has consistently demonstrated expertise in various roles. His journey has encompassed diverse positions, from a software developer and enterprise architect to an engineering director, practice head, and technical product manager. Currently associated with Thoughtworks, he has also significantly contributed to Nagarro and Quark Media House during his illustrious career.

In the tech industry, Kuldeep stands out as a trailblazer, playing a pivotal role in pioneering Centers of Excellence for emerging technologies like IoT, AR/VR, and Web3. His leadership extends to overseeing complex data projects in estimations, forecasting, and optimization, designing highly scalable, cloud-native, and microservices-based architectures. He emphasizes bringing technology to the core of the business and advocates for development practices such as CI/CD, Test-Driven Development (TDD), and eXtreme Programming (XP). His extensive experience spans diverse domains, including manufacturing, aviation, education, retail, telecom, healthcare, commodity trading, and more.

Beyond his technical roles, Kuldeep is a prolific writer and speaker, actively sharing insights on technology, leadership, and motivation. His extensive portfolio includes over 100 articles, some of which have gained recognition in esteemed publications such as Economics Times, Tech.de, industry4o.com, Analytics India and Thoughtworks Insights

Academically, Kuldeep holds a B. Tech. in Computer Engineering from NIT, Kurukshetra. His commitment to knowledge includes active involvement in events, mentoring and judging initiatives, and volunteering for ideations, hackathons, and nonprofit causes, collaborating with organizations like NASSCOM, Meta, Ministry of Electronics and Information Technology (MeitY) — Government of India, Smart India Hackathon, Digital India, FICCI, AICTE, IEEE, The VRARA, Metaverse India Policy and Standards (MIPS), and academic institutions like IIT, NITs, and other universities across India. He is also engaged with World Metaverse Council, Responsible Metaverse Alliance, and others to collectively build and share the community by open source.

With a passion for empowering others and a steadfast commitment to innovation, Kuldeep stands as a thought leader in the ever-evolving landscape of technology.

Mr. Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Mr. Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

The post Building Gen AI Solutions appeared first on Industry4o.com.

The biggest myth about “big data” is that it exists as some grand, singular reservoir waiting to be tapped. In reality, useful data is rarely one massive pool. It is a mosaic—stitched together from countless small data points scattered across systems, processes, and organizations.

The real challenge lies not in collecting “more data,” but in discerning which fragments matter, how they connect, and how they can be translated into meaningful insights. Without this context, the pursuit of big data can easily become a distraction rather than a solution.

Big data is often compared to grains of sand: each particle insignificant on its own, yet together capable of creating a storm. A sandstorm does not clarify; it blinds. Similarly, the uncontrolled accumulation of data can overwhelm decision-makers, leaving them disoriented unless there is discipline in filtering what to retain and what to discard.

Data, by itself, holds no intrinsic value. Like crude oil, it cannot be used in its raw form. It must be refined—through analytics, context, and interpretation—before it becomes actionable. Until then, it remains a cost center, consuming storage, processing power, and attention.

The real opportunity lies not in the bigness of data, but in the smartness of its use. Small, well-curated datasets—combined with domain expertise and the right analytical tools—often yield richer insights than terabytes of poorly understood information.

Organizations that succeed in the data-driven era are not those with the most data, but those with the sharpest clarity: the ability to ask the right questions, to frame problems precisely, and to refine data into knowledge that drives action.

In other words, the future belongs not to big data but to SMART DATA.

About the Author :

Ms. Parul Pal 
Sales Director
BILTZENTECH SOLUTIONS PRIVATE LIMITED

Ms. Parul Pal is an experienced Account Manager with a strong background in IT, Data Centers, and Telecom.

Ms. Parul Pal is skilled in building and maintaining key client relationships, driving revenue growth, and delivering customized solutions. With expertise in strategic account management, contract negotiation, and project execution.

Ms. Parul Pal have successfully managed portfolios of high-value clients, fostering long-term partnerships.

Ms. Parul Pal excel in identifying business opportunities, optimizing IT procurement, and ensuring seamless service delivery

Ms. Parul Pal ability to engage cross-functional teams, handle escalations, and provide data-driven insights helps clients achieve their goals efficiently.

Ms. Parul Pal is passionate about customer success and committed to delivering value-driven solutions that enhance operational efficiency & drive business growth.

Ms. Parul Pal is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/parul-pal-615b5067/details/certifications/

Ms. Parul Pal can be contacted at :

LinkedIn | E-mail 

About BILTZENTECH SOLUTIONS PRIVATE LIMITED :

Biltzentech is an innovation-driven tech company, specializing in:

• Full-stack Application Development
• AI & Generative AI Solutions
• Enterprise-grade ERP Implementation & Support
• Cloud, DevOps, and Infrastructure
• End-to-End Support & Managed Services.

Biltzentech work with startups, SMEs, and large enterprises to provide on-demand, dedicated, and project-based resourcing tailored to business goals and timelines.

BILTZENTECH SOLUTIONS PRIVATE LIMITED can be contacted at:

LinkedIn | E-mail | Website | Phone : (+91) 9591433732

The post Myth of Big Data appeared first on Industry4o.com.

Key takeaway: When Samoa decided to skip an entire day to realign with its future, it revealed a profound truth about measurement and governance: sometimes you must change the frame, not just the numbers, to move forward.

They went to bed on Thursday and woke up on Saturday. No glitch. No celestial prank. No Star Wars. No Star Trek. No time-travel. On the night of December 29, 2011, the people of Samoa crossed an invisible border and left Friday behind, vaulting 24 hours into the future to stand shoulder-to-shoulder with their most important neighbors. They didn’t stumble into this; they planned it. Parliament passed the law months earlier, and at the stroke of midnight, the nation’s clocks jumped from UTC-11 to UTC+13 so that Samoa’s week would finally align with Australia, New Zealand, and Asia, the countries and region where their phone calls, flights, and fortunes were increasingly tied.

That “missing” Friday solved a very real problem. Before the change, Samoa lagged a day behind the business week of its primary trading partners. Meetings slipped, markets moved without them, and the country’s calendar itself had become a bottleneck for commerce and coordination. Prime Minister Tuilaepa Sailele Malielegaoi championed the shift as a pragmatic way to match Samoa’s time with Samoa’s future. The island went far beyond a clock adjustment; this essentially rewired what counted as “now.” And the nation celebrated the leap with fireworks and a jubilant “double New Year’s” that symbolized a fresh beginning.

Of course, big changes carry ripples. The Seventh-day Adventist community wrestled deeply with Sabbath observance in the new time zone: if Saturday in Samoa now aligned with Sunday in Australia and New Zealand, what did “the seventh day” mean in practice? Different congregations charted different courses, and the conversation was anything but casual. There were quirks, too. For those with birthdays on December 30, that personal milestone simply didn’t happen in 2011; a bittersweet footnote to a national choice. Yet the outcome has become part of Samoa’s identity. Today, it’s one of the first places on Earth to greet the sun, a change that carries symbolic weight and touristic appeal.

A curious story? Perhaps, but it’s much more. It’s a masterclass in measurement and governance. Samoa went way beyond “fixing the data” to reframe the rule that produced the data. They aligned an entire system (calendar, commerce, culture) with its strategic objectives. If you work with data, there’s a lesson here that’s too important to miss.

In our world, the “International Date Line” shows up as definitions, schemas, dashboards, and thresholds; the lines we draw to make sense of reality. And like Samoa, we sometimes discover that yesterday’s lines are costing us tomorrow’s opportunities. At that moment, your decision is both technical and existential. Do you cling to conventions that no longer serve you, or do you rewrite the rulebook to match where you’re going?

Here’s the framework I propose when guiding teams through those inflection points. It’s both simple and sticky on purpose. Four pillars; four choices. And together, the first letters spell the only resource you can’t stockpile: TIME.

• T: Trust
• I: Integration
• M: Measurement
• E: Expansion

Each one matters on its own. Woven together, they turn governance from a gate into a growth engine.

T. Trust

Trust is the first domino. Without it, the best policies gather dust, and the smartest models go unused. Trust doesn’t materialize because we declare it; it’s the byproduct of clear ownership, real accountability, and consistent transparency. Modern data governance programs make this explicit: domains own their data, while a central function provides standards, guardrails, and enablement so that “accountable” is not the same as “abandoned.”

Think of federated stewardship as a social contract: every dataset has a named steward; every business definition has a living owner; every critical pipeline has lineage you can follow and a change history you can audit. When someone asks, “Which revenue are we showing?” or “How do we calculate churn?” you can point to a single, authoritative source and a human being who will stand behind it. That clarity breeds confidence. And confidence, not volume, is what turns data into decisions.

Samoa’s leap required public trust of the same sort. Citizens trusted their leaders to weigh economic reality over habit. Businesses trusted that the switch would reduce friction across borders. Faith communities wrestled openly with what sacred observance meant in a changed frame of reference. Trust didn’t mean unanimity; it meant transparent choices, responsible stewards, and a shared commitment to navigate the change together.

I. Integration

Samoa had a synchronization problem. Its internal clock was misaligned with its external ecosystem, and that misalignment cost real money and momentum. Integration, of calendars and commerce, was the remedy.

In data programs, integration begins with a plain question: can people and systems share a coherent truth without friction? That demands more than APIs and ETL jobs. It requires shared vocabularies, governed workflows, and an architecture that moves data from creation to consumption without losing context or meaning. Catalogs clarify what exists. Lineage shows where it came from and where it goes. Standards keep “customer,” “order,” and “profit” from becoming multiverse variants of themselves.

The payoff is not theoretical. When teams pull from the same definitions, cross-functional metrics stop arguing with each other. When pipelines are observable, breaks become incidents, not mysteries. When integration is both technical and semantic, you don’t just move data; you move insight at the speed of coordination.

M. Measurement

Here’s where Samoa’s story hits the heart of the matter. The nation didn’t “fix Fridays.” It redefined what the next day would be. That’s measurement at its most powerful: making deliberate, transparent choices about what counts, how it’s counted, and why it matters.

In data governance, measurement means codifying policies for quality, access, privacy, retention, and fitness-for-purpose; and tying them directly to compliance and risk requirements so they live in the real world, not in a binder. It means defining thresholds, scoring data quality routinely, and auditing those scores so they inform behavior and not just a quarterly slideshow. It means being able to trace a KPI back through its lineage, see the joins and transformations that shaped it, and assess the blast radius of any change before it detonates in production.

Measurement is not merely arithmetic. It’s a design decision. Just like Samoa, we sometimes discover that the definitions we inherited no longer serve the outcomes we need. When that happens, you must have the courage, and the governance, to revise the definition openly, document the change, and communicate the impact so the organization marches in step.

E. Expansion

Samoa’s shift wasn’t a quick one-day stunt. It was a strategic pivot; legislated, planned, and executed to support long-term alignment with trading partners, followed by the day-to-day work of living in the new reality. That’s what expansion looks like in practice.

Data governance that endures behaves the same way: start small, learn fast, expand what works. Agile governance programs create feedback loops, version-control their policies, and adapt to shifting regulations, markets, and technologies. They embed controls into everyday workflows, automate where feasible, and use AI to scale monitoring without turning the program into a bureaucracy that chokes innovation.

I like to tell leaders: if your governance “launch date” feels like an ending, you built a monument, not a movement. The organizations that thrive treat governance as a living system: always listening, adjusting, and improving with each cycle.

A Living Framework, not a Rigid Rulebook

Multiple reference models can help you design this well. The DAMA-DMBOK positions governance at the center of the data lifecycle, reminding us that quality, architecture, security, and operations orbit a shared core. The DGI framework foregrounds mission, metrics, rules, and accountability; useful anchors when you’re aligning stakeholders around why governance exists in the first place. Other models like COBIT and DCAM give you scaffolding for risk, compliance, and capability maturity so you can build with confidence rather than guesswork. The right move is rarely to adopt one wholesale; it’s to tailor a blend that fits your culture, constraints, and ambition.

And if you’re wondering whether a shift in measurement can really unlock transformation, history will back you up. Whole industries have changed their trajectory by revising the dashboards they live by, whether through broader, strategy-linked scorecards or more adaptive, outcome-driven systems. The lesson is consistent: new measures create new behavior when the governance behind them is credible and the story they tell is clear.

Bringing It Back to the Day That Disappeared

Let’s re-enter that Samoan night for a moment. The crowds counted down. Midnight arrived. And then there was no Friday. In the morning, calendars read Saturday, December 31. Tokelau made the leap with them. A nation and its neighbor stepped across an invisible seam in the map and discovered a week that finally matched their work and their world. The skies did not fall. The sun rose (earlier, in a sense, than it used to) and the new rhythm began.

There were debates, to be sure. Faith leaders and congregations sorted through centuries of observance with humility and conviction. Families teased the few whose birthdays vanished that year. But the deeper narrative crystallized: a community can choose the frame that best serves its future, provided it does so with clarity, courage, and care.

That’s the heart of data governance. We don’t worship yesterday’s definitions. We steward truth in service of tomorrow’s outcomes. We build TRUST by naming owners and honoring transparency, so people believe the numbers they see. We pursue INTEGRATION so the truth can move, cleanly, coherently, through systems and teams without losing its meaning. We make MEASUREMENT explicit, tying rules to risks and value so we can defend every metric we use and improve those that fall short. And we commit to EXPANSION, iterating our policies and platforms as our world changes, so governance remains a catalyst rather than a constraint.

Put those four together: Trust, Integration, Measurement, Expansion. Result: you go from managing data to changing your relationship to time. You shorten the gap between question and insight. You prevent yesterday’s blind spots from dimming tomorrow’s opportunities. You build an organization that, like Samoa, chooses alignment over inertia.

So, here’s your invitation, from one data storyteller to another. Take a hard look at your own “date lines.” In what way are you a day behind the world you serve? Which definitions have dulled into dogma? What would you gain if you stepped across the seam, not recklessly, but with the kind of intentionality that turns a lost day into a found future?

Because in the end, the magic isn’t that Samoa skipped Friday. The magic is that they decided what their next day needed to be; and then they built it, publicly, boldly, and together. That’s governance with a backbone.

Make TIME your playbook. Build Trust you can defend. Design Integration that makes truth portable. Set Measurement that earns belief. Embrace Expansion that keeps you in rhythm with reality. Do that, and when the clock strikes midnight on your next big decision, you won’t be hoping the calendar favors you.

You’ll be ready to write the day you need.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is a truly exceptional professional who has left an indelible mark on the IT, health and human services, and higher education sectors. His journey began in the field of education, where he laid the foundation for his career. With advanced degrees in education and a doctorate that included a double minor in computers and theology, Joe embarked on a path that ultimately led him to the dynamic world of data-driven Information Technology.

In the early 1990s, he transitioned into IT, starting as a Computer Consultant at NC State University. Over the years, his dedication and expertise led to a series of well-deserved promotions, culminating in his role as Business Intelligence Specialist that capped his 25 successful years at NC State. Not one to rest on his laurels, Dr. Perez embarked on a new challenge in the fall of 2017, when he was recruited to take on the role of Senior Business Analyst at the NC Department of Health & Human Services (DHHS). His impressive journey continued with promotions to Senior Systems Analyst and Team Leader, showcasing his versatility and leadership capabilities.

In addition to his full-time responsibilities at DHHS, Joe assumed the role of fractional Chief Technology Officer at a North Carolina corporation in October 2020. A top-ranked published author with over 18,000 followers on LinkedIn and numerous professional certifications, he is a highly sought-after international keynote speaker, a recognized expert in data analytics and visualization, and a specialist in efficiency and process improvement.

Dr. Perez’s contributions have not gone unnoticed. He is a recipient of the IOT Industry Insights 2021 Thought Leader of the Year award and has been acknowledged as a LinkedIn Top Voice in multiple topics. He holds memberships in prestigious Thought Leader communities at Gartner, Coruzant Technologies, DataManagementU, Engatica, the Global AI Hub, and Thinkers360 (where he achieved overall Top 20 Thought Leader 2023 ranking in both Analytics and Big Data). His reach extends to more than twenty countries worldwide, where he impacts thousands through his speaking engagements.

Beyond his professional achievements, Joe’s passion for teaching remains undiminished. Whether as a speaker, workshop facilitator, podcast guest, conference emcee, or team leader, he continually inspires individuals to strive for excellence. He treasures his time with his family and is a gifted musician, singer, pianist, and composer. Joe also dedicates his skills as a speaker, interpreter, and music director to his church’s Hispanic ministry. He manages the publication of a widely recognized monthly military newsletter, The Patriot News, and is deeply committed to his community.

To maintain a balanced life, Perez is a regular at the gym, and he finds relaxation in watching Star Trek reruns. He lives by the philosophy that innovation is the key to progress, and he approaches each day with boundless energy and an unwavering commitment to excellence. His journey is a testament to the remarkable achievements of a truly exceptional individual.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

The post When a nation deleted a day & taught us about DATA appeared first on Industry4o.com.

It’s not every day that someone patents the wheel. Yet, in 2001, an Australian man named John Keogh did exactly that. Or at least, that’s how the story is usually told. With a creative name for the invention, he submitted an application for what he called a “circular transportation facilitation device.” The technical description? A rim. A hollow cylinder. A set of connectors to hold it all together. Oh, and a rubber layer with an inflatable tube, because… why not?

This was not a bid for a spot in the Inventors’ Hall of Fame. Keogh, a freelance patent lawyer, was making a point; one aimed squarely at Australia’s freshly introduced innovation patent system. Designed to be a shortcut for small businesses, the system was, in his view, too permissive. Too easy. A playground for anyone looking to take advantage of the rules.

The whole episode earned Keogh and the Australian Patent Office an IG Nobel Prize in Technology, the kind of award that’s equal parts comedy and critique. His patent didn’t last, of course, but the phrase “reinventing the wheel” found another story to attach itself to; one that applies just as neatly to data governance as it does to intellectual property.

From Wheels to Data: A Familiar Mistake

The wheel is a solved problem. So also, in most cases, is the core framework for managing data. Yet many organizations keep starting from scratch, designing entirely new governance systems while perfectly functional, field-tested models already exist. In spite of what they see right in front of them, they still try to, well, “reinvent the wheel.”

Why? Partly, it’s the illusion of innovation. Building something brand new feels exciting, even when it’s unnecessary. But the result is often quite a bit of trial, even more error, and not much to show for either one.

Effective data governance doesn’t necessarily need a blank slate every day. It requires taking what already works, adapting it for your particular environment, and then putting it into practice with precision. That’s where the following three pillars come in: clarity, consistency, and collaboration.

Clarity

Policies that no one can understand aren’t worth much. While this sounds like an obvious statement, many organizations keep their data governance policies buried in obscure documents that few people even bother to read. What’s even worse is that those policies are often written to satisfy auditors rather than to help the people who actually work with the data.

Without clarity, people start guessing. They make their own interpretations. And before long, you’ve got half a dozen “versions” of what the policy supposedly says circulating around. That’s how inconsistent data handling creeps in, how errors multiply, and how compliance gaps open up without anyone noticing.

The fix is straightforward: Use plain language. Not oversimplified, but clear. Make it obvious where the rules live; that is, ideally in one central location that’s easy to access. Keep it up to date, because old instructions that no longer match reality are just as dangerous as having no instructions at all.

And don’t just post the policy and leave it there. Reinforce it. Make it a point to explain the reasoning behind each rule in training sessions, in quick reminders during team meetings, and even in casual conversations when relevant. People tend to follow what they understand, and they remember what makes sense to them.

Consistency

If clarity can be defined as knowing the rules, then consistency means applying those rules the same way across the entire organization.

Without it, you might have the Marketing department defining a “customer” one way, Finance defining it another, and Operations defining it a third. The result? Conflicting reports, duplicate records, confused team members, and hours wasted reconciling numbers that should have matched in the first place. Not to mention the damage to your group’s reputation.

Consistency isn’t about enforcing rigid uniformity that ignores different departmental needs. It’s about setting shared standards — formats, definitions, approval steps — and sticking to them unless there’s a clear reason not to.

The best way to make that happen is through a data governance committee with members from across the organization. This group acts as referees, translators, and problem-solvers between departments. They spot inconsistencies early and work out compromises before problems get entrenched.

Regular audits help too. Not as “gotcha” exercises meant to throw anyone under the bus, but as reality checks. Questions need to be asked: Are teams following the agreed processes? Are the processes still fit for purpose? Not so much enforcing rules for rules’ sake, but rather making sure the organization can actually trust the data that it’s relying on.

Collaboration

Even with the clearest rules and the most consistent processes, data governance can still fail if collaboration is missing.

In theory, everyone agrees that data should be managed well. In practice, however, different teams have different priorities. Legal wants airtight compliance. Analytics wants speed. Sales wants flexibility. Management wants clear insights they can act on without wading through technical jargon. And IT… well, IT wants to sleep at night.

When these groups operate in isolation, they often work against each other, without even realizing it or intending to do so, trapping valuable lessons within these silos. Processes (like the proverbial “wheel”) get reinvented, often with poor results. And opportunities to make the whole system more efficient are missed entirely.

The solution starts with conversation. Form cross-functional governance teams that meet often enough to stay aligned, not just when a crisis demands it. Use shared tools such as project boards, chat channels, and version-controlled documentation, so everyone can be aware of the bigger picture and contribute when it matters.

Make cooperation an expectation that’s built into everyday workflows, not an optional extra to consider when time allows. And when collaboration pays off, point it out. Acknowledge and reward collaborative wins. Highlight them in company updates. Show, consistently, that the organization values people who make governance work better together.

Putting the Pieces Together

The path to effective governance is not complicated. It is, however, deliberate.

• Clarity removes guesswork, replacing it with shared understanding.
• Consistency turns that understanding into reliable practice.
• Collaboration keeps the whole thing adaptable and resilient.

Before you embark on yet another “brand new” governance initiative, it’s worth asking the question: are we truly innovating, or just changing the paint color on a perfectly good wheel?

The Takeaway

In patent law, reinventing the wheel can earn you a laugh; maybe even an IG Nobel prize. But in data governance, it’ll just cost you time, money, and trust.

The better approach is to learn from strategies that have already been tested, adapt them to fit your needs, and then put effort into keeping things running smoothly. This is where clarity, consistency, and collaboration do their best work:  by making data governance both possible and even sustainable.

Data may be one of your most valuable assets, but without good governance, it’s just information sitting in a system. With those three principles in place, it becomes a foundation for better decisions, stronger compliance, and meaningful innovation.

So, let’s stop reinventing the wheel and start making sure the one we’ve got is well-oiled and ready for the road ahead.

“Bringing Data to Life and Life to Data”

About the Author:

Dr. Joe Perez,
Team Lead / Senior Systems Specialist,
NC Department of Health and Human Services

Dr. Joe Perez ( Dr.Joe ) is also the Chief Technology Officer – CogniMind

To book Dr. Joe Perez for your speaking engagement please click here

Dr. Joe Perez was selected as the 2023 Gartner Peer Community Ambassador of the Year.

Dr. Joe Perez is a truly exceptional professional who has left an indelible mark on the IT, health and human services, and higher education sectors. His journey began in the field of education, where he laid the foundation for his career. With advanced degrees in education and a doctorate that included a double minor in computers and theology, Joe embarked on a path that ultimately led him to the dynamic world of data-driven Information Technology.

In the early 1990s, he transitioned into IT, starting as a Computer Consultant at NC State University. Over the years, his dedication and expertise led to a series of well-deserved promotions, culminating in his role as Business Intelligence Specialist that capped his 25 successful years at NC State. Not one to rest on his laurels, Dr. Perez embarked on a new challenge in the fall of 2017, when he was recruited to take on the role of Senior Business Analyst at the NC Department of Health & Human Services (DHHS). His impressive journey continued with promotions to Senior Systems Analyst and Team Leader, showcasing his versatility and leadership capabilities.

In addition to his full-time responsibilities at DHHS, Joe assumed the role of fractional Chief Technology Officer at a North Carolina corporation in October 2020. A top-ranked published author with over 18,000 followers on LinkedIn and numerous professional certifications, he is a highly sought-after international keynote speaker, a recognized expert in data analytics and visualization, and a specialist in efficiency and process improvement.

Dr. Perez’s contributions have not gone unnoticed. He is a recipient of the IOT Industry Insights 2021 Thought Leader of the Year award and has been acknowledged as a LinkedIn Top Voice in multiple topics. He holds memberships in prestigious Thought Leader communities at Gartner, Coruzant Technologies, DataManagementU, Engatica, the Global AI Hub, and Thinkers360 (where he achieved overall Top 20 Thought Leader 2023 ranking in both Analytics and Big Data). His reach extends to more than twenty countries worldwide, where he impacts thousands through his speaking engagements.

Beyond his professional achievements, Joe’s passion for teaching remains undiminished. Whether as a speaker, workshop facilitator, podcast guest, conference emcee, or team leader, he continually inspires individuals to strive for excellence. He treasures his time with his family and is a gifted musician, singer, pianist, and composer. Joe also dedicates his skills as a speaker, interpreter, and music director to his church’s Hispanic ministry. He manages the publication of a widely recognized monthly military newsletter, The Patriot News, and is deeply committed to his community.

To maintain a balanced life, Perez is a regular at the gym, and he finds relaxation in watching Star Trek reruns. He lives by the philosophy that innovation is the key to progress, and he approaches each day with boundless energy and an unwavering commitment to excellence. His journey is a testament to the remarkable achievements of a truly exceptional individual.

Dr. Joe Perez is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/jwperez/details/honors/

Dr. Joe Perez is Bestowed with the following Licences, Certifications & Badge:

https://www.linkedin.com/in/jwperez/details/certifications/

https://www.thinkers360.com/tl/badge/19985/2764

Dr.Joe Perez is Voluentering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/jwperez/details/volunteering-experiences/

Dr.Joe Perez can be contacted at :

E-mail | LinkedIn | Web | Sessionize | FaceBook  | Twitter | YouTube

Also read Dr.Joe Perez‘s earlier articles:

The post Data Governance appeared first on Industry4o.com.