Unplanned Downtime Isn’t a Maintenance Issue ; It’s a Margin Crisis

The Core Shift: From Run-to-Failure → Predict-and-Prevent

In discrete manufacturing (automotive, electronics, aerospace, industrial equipment),
unplanned downtime is one of the largest and least visible profit leaks.

Industry Reality:

Large manufacturing plants lose ~$532,000 per hour due to unplanned downtime
( Forbes / Siemens )

So What?

Plants are losing millions not because machines fail, but because failures are not
predicted in time.

AI Unlocks the “Golden 48 Hours” Humans Can’t See

Why Traditional Maintenance Always Reacts Too Late

Conventional systems rely on fixed thresholds and alarms, they trigger only after
damage is already underway.

Capability Reactive / Preventive AI-Driven Predictive Detection Threshold-based
(late) Pattern-based (early) Accuracy High false positives 80-90% ML accuracy Intervention Window Minutes 24-48 hours Downtime Impact Fully unplanned 30 – 50% reduction

What AI Sees Differently

Evidence:

AI models (Random Forest, LSTM) can predict failure cycles with ~80% accuracy,
providing 24–48 hours of lead time
(Journal of Industrial Engineering; McKinsey)

So What?

Predicting failure early creates a 30–50% efficiency buffer operations never had
before.

Cutting Downtime Requires Architecture, Not Just Algorithms

Why AI PdM Succeeds Only with a Structured Data-to-Decision Pipeline

Plants achieving consistent 30–50% downtime reduction follow a disciplined,
repeatable architecture.

The 5-Step PdM Operating Model

What Leaders Do Differently

So What?

AI PdM becomes a new reliability operating model, not a dashboard experiment.

30–50% Downtime Reduction Is Now an Industry Benchmark

Why the ROI Is Proven Across Discrete Manufacturing

AI Predictive Maintenance is no longer experimental, it is repeatable at scale.

Real-World Evidence

• Automotive (GM):

• Semiconductor Fab:

• Heavy Machinery:

Beyond Downtime

So What?

AI PdM has moved from best practice to competitive baseline.

The Competitive Window Is Closing Fast

Why AI PdM Is a Margin Play Today, and Survival Tomorrow

The gap between AI-enabled plants and legacy maintenance models is widening.

Market Signals

Risk of Inaction

Final Recommendation : Where to Start

Run a 4-week Critical Asset Audit to identify the top 3 machines where a 30%
downtime reduction delivers immediate ROI.

Closing Thought:

AI doesn’t eliminate maintenance work, it eliminates surprises.

And in discrete manufacturing, surprises are the most expensive failures of all.

About the Author :    

Mr. Gulshan Kumar Saini
Senior Director
Samsung Electronics

Mr. Gulshan Kumar Saini is a global manufacturing, operations excellence, and smart factory transformation leader with over 30 years of leadership experience at Samsung Electronics, spanning senior roles across India, Vietnam, Korea, Russia, and the United States.

Mr. Gulshan Kumar Saini currently serves as Senior Director – Smart Factory at Samsung Electronics India, where Mr. Gulshan Kumar Saini spearheads enterprise-wide digital transformation initiatives across advanced manufacturing lines for consumer electronics and durables.

Mr. Gulshan Kumar Saini leads the deployment of AI-driven manufacturing systems, industrial robotics, IIoT platforms, machine vision, advanced analytics, and Digital Twin technologies to drive next-generation factory performance.

Mr. Gulshan Kumar Saini is widely recognized for delivering large-scale automation, step- change productivity improvements, and sustainable cost, quality, and efficiency gains in high-volume, high-complexity manufacturing environments. Across his career, Mr. Gulshan Kumar Saini has successfully led global NPI programs, greenfield and brownfield plant setups, capacity expansions, lean transformations, and multi-country manufacturing excellence programs.

Mr. Gulshan Kumar Saini is a PMP-certified professional and Six Sigma Black Belt, Mr. Gulshan Kumar Saini also holds an MBA in FinTech from BITS Pilani, combining strong digital, financial, and operational acumen to translate technology into measurable business outcomes.

Mr. Gulshan Kumar Saini is Bestowed with the following Licenses & Certifications:

https://www.linkedin.com/in/gulshan-kumar-saini/details/certifications/

Mr. Gulshan Kumar Saini is Accorded with the following Honors & Awards:

https://www.linkedin.com/in/gulshan-kumar-saini/details/honors/

Mr. Gulshan Kumar Saini can be contacted at:

Linked In | E-mail (1) | E-mail (2)

Also read Mr. Gulshan Kumar Saini‘s earlier article:

The post AI Predictive Maintenance appeared first on Industry4o.com.

Going by the Accounting Principles, an Organization is to be Construed, Conceptualized and Carried-on for Perpetual Existence – in colloquial terms, Ever Lasting & Never Ending for Generations to come. But in reality, there are Business Exits – for Strategic, Tactical or sometimes Conditions induced reasons.

When Exits are planned and structured, as in the case of Strategic and Tactical Decisions, its very controlled and the impact of which is carefully taken into consideration prior to implementation. In the 3rd case, Business Conditions induced Exits are usually uncontrolled and therefore are undesirable, resulting in People Issues and Emotional Conflicts, Business issues apart.

As the Famous adage goes “Today, You need to run to remain in the same place. And to Succeed, You need to run faster than the fastest.” This is same for Businesses. To remain where they are, they have to run atleast to match the speed of Industry Growth. And if they are to Grow, they have to run faster than their Competitors – as Growth comes only by acquiring Market and Business Share.

One of the essential aspects of Growth, is to Change and Change Constantly, Consistently, Continuously. As much Change and Growth are inseparable, they are essentially People Dependent and hence, Leadership Oriented. Apex Level Changes – are supposed to be Strategic leading to Compounded results, Operational Level Changes are Tactical leading to Incremental results and Execution Level Changes are for Sustainable results.

It’s all about 3Cs – Conviction, Commitment & Consistency.

Growth comes essentially because of the ability of the Organization to easily Adapt to the Changes effected by SPELT (Social, Political, Economic, Legal and Technological) factors. Other way to manage Change is to create one. Such strategies also define Market Leaders, Followers and Laggards. There are both merits & demerits in being either one.

Lag Vs Lead Indicators

Businesses thrive on Revenues, Profits and Cash Flows. Prudent Leadership will agree these are results based on past and facts. Certainly they are required for Business continuity and therefore entrusted with Executional Level. Growing Organizations usually focus on influencing SPELT environment to their advantage. Focus then shifts to Market Share, Business Share, New Businesses/ New Customers/ New Markets and New Products.

Failures as stepping stones to success

Growth oriented Leadership encourage Changes, despite knowing the risk of Failures. Its important to accept that there is always a 50% probability to succeed if we try, which otherwise is zero. Successful Organizations actually celebrate failures – wherein it incentivizes to discuss, understand and analyze so as to not repeat them. This is essentially helpful when its 100% transparent and organization wide. Learning from Failures then becomes a part of the Business Process, well documented and widely accessible.

Actions and Inactions:

Actions can be Predictive, Proactive, Reactive and Responsive. Each of these have their own consequences – while being Predictive and Proactive may be choices that induce Change and thus Growth, Reactive and Responsive are essential to sustain businesses. If we don’t Act and failure to act appropriately at an appropriate time may cause irreparable consequences. Ofcourse, to wait and watch is also an action but with limitations, sometimes when overused becoming a liability.

Fundamentals

Many effective Leaders who are Change Makers believe in questioning the Fundamentals. This is certainly needed in a dynamic, fast changing Business Environment. For Business Sustainability, even to change the Fundamentals, there is a need to understand and believe the prevailing fundamentals to know what to change and what not to change. Change for the sake of Change has to be avoided and therefore Change & its impact should be measurable. Reliability then is a result of Repeatability which relies on Processes & People Capability.

Learning & Growing

Growth comes out of the ability of the Organization to Learn and Lead, which means People who are part of the Change and Growth process should be willing to Learn and Grow and thus Grow the Organization. As much as People and Processes are inseparable, Organization and People are.

Cannibalization is an effective Business Strategy which comes out of Learning from past and from customers. When the same applied for People, its Learning to move out of old knowledge and habits (comfort zone) to acquire new knowledge and new skills.

About the Author :

Dr. NITHYANANDAN DEVARAAJ (NITHIN) BE., MBA., Ph.D
Independent Board Member, Business Excellence Expert, Technical Due Diligence & Capability Assessor.
Managing Partner – SAAE Enterprises
President – Coimbatore Management Association
Past National Secretary – The Institute of Indian Foundrymen.
Former MD / CEO of a German & Japanese MNC
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 : +91 94879 46900 , +91 99653 11288

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

Also read Dr. NITHYANANDAN DEVARAAJ’s earlier articles:

The post CHANGE is GROWTH appeared first on Industry4o.com.

Key Takeaway: Reinvention isn’t some Silicon Valley luxury. It’s survival. Any organization that wants to keep up in a world where data, tech, and priorities change overnight needs to get good at this (and fast).

Introduction: The Power of the Pivot

Picture this: a scrappy team in San Francisco, founders of a startup called Odeo, suddenly find their business model flattened by Apple’s iTunes. Most people would’ve called it a day. But not these folks. They tossed out their old idea and doubled down on something wild: an app for sharing what you’re doing in just 140 characters. That gamble gave us Twitter, and changed how the world connects.

Across the city, another crew had their own mess. Their app, Burbn, did everything from check-ins, to games, to social stuff, to photos. But users only cared about posting pictures with cool filters. So, Kevin Systrom and Mike Krieger made a tough call. They stripped Burbn down to just photo sharing and turned it into something called “Instagram.” The result? A cultural juggernaut that changed how we capture the world.

While it is certain that a majority of people have never heard of Odeo or Burbn, Twitter (now “X”) and Instagram have become household terms. Strategic reinvention put them both on the map, and they’re here to stay. At least for now.

So, what does any of this have to do with lean data governance? Actually, more than you might think. Twitter and Instagram didn’t win at reinventing themselves strategically by piling on features or more rules. They won because they knew when to focus, when to simplify, and when to let go of anything that didn’t matter. That’s the heart of lean data governance: cut the clutter, stay focused, and build systems that serve the business. Not the other way around.

The Trap of Traditional Governance

Let’s be real. Most organizations start with good intentions for data governance. They want to keep data safe, follow the rules, and use data responsibly. But here’s the problem: old-school data governance frameworks were built for a slower world. When things speed up, those same frameworks turn into roadblocks.

All those committees, sign-offs, and endless paperwork? They add friction, not value. Teams get tangled up in bureaucracy instead of actually doing something meaningful with data. It’s like trying to sprint with weights around your ankles. Sure, you can move forward, but doing it that way is so much harder than it should be.

Lean data governance takes a page from the Twitter and Instagram playbook. Instead of tweaking the old system, it asks the question: “What if we started fresh?” Rather than being satisfied with only rebranding, it goes a step further and encompasses real transformation; that is, putting users first, making bold decisions, and knowing when to move on. That’s what sets effective data governance apart from the usual red tape.

There are five cornerstones upon which this “strategic reinvention” mindset is built: focus, strategic constraints, user-centric design, timing and execution, and building on strengths. Let’s break them down one at a time.

Focus: The Instagram Principle

Rather than merely trimming the fat, Instagram went all in on what mattered and ditched everything else. Systrom and Krieger weren’t aiming for a “simpler” app. They wanted something so clear in its purpose that it could really take off.

I’ve seen it over and over: when companies pick one thing to do really well, and ignore the urge to please everyone, they win. They figure out what makes them special and cut out the distractions.

Data governance has the same problem. Too many organizations try to cover every possible scenario, and end up with a mess of policies that slow everyone down. Lean data governance borrows that Instagram mindset: find what truly matters and get rid of the rest.

Strategic Constraints: The Twitter Lesson

Twitter’s 140-character limit (later 280) may seem like a tech quirk, but it was a deliberate choice. That boundary forced people to get creative and gave Twitter its unique style.

In data governance, strategic constraints work the same way. Instead of endless approvals and red tape, lean governance gives teams clear rules (such as data quality standards, who can access what, retention timelines, and so forth), then lets them get to work. These boundaries keep things on track without smothering innovation.

The goal isn’t just to make things run smoother. It’s about giving people room to innovate, but within clear boundaries.

User-Centric Design: Building for People, Not Just Policy
Look at Twitter and Instagram. They show how user-focused design can drive big changes. Twitter didn’t invent hashtags in a boardroom. Users started tagging their tweets, and the company just paid attention and made it official. Over on Instagram, filters went beyond simply making photos look nice. They turned regular folks into artists, making everyone feel like their photos were worth sharing.

Good data governance takes the same approach. Instead of treating people like compliance problems to manage, smart organizations see them as partners who add value. When you bring data users into the process, you build systems that actually help them work, not slow them down. Adoption rates go up, and suddenly, data governance goes from being a box to check off to becoming a competitive advantage.

Timing and Execution: The Decisive Edge

Timing really matters. Twitter made its move before Odeo tanked; Instagram shifted gears just as mobile photography took off. Both saw the turning point and jumped in before anyone else could own the space.

Data governance works the same way. The hard part isn’t knowing what needs to change, but rather, making it happen at the right moment. Too many organizations treat lean governance as just another tech project and forget the cultural shift that makes it stick. If you wait until your old systems totally fall apart, you’re in for a rough ride. The best results come when you start changing things while the old ways still sort of work; when the team is frustrated but not in full crisis mode yet.

Building on Strengths: Evolution, Not Revolution

One thing stands out: neither Twitter nor Instagram tossed out what already worked. Twitter’s real-time messaging became microblogging. Instagram’s photo filters built the backbone for social sharing. Instead of rebuilding from the ground up, they adapted what they already had.

That’s exactly how organizations should approach data governance. Don’t throw away the systems that deliver value. Figure out what’s working, cut the red tape, and evolve. Period. No need for a dramatic overhaul; just build on what you know works.

Conclusion: The Courage to Reinvent

Here’s the bottom line. If you want to reinvent your organization, you need vision and discipline to execute. Like Twitter and Instagram, you have to drop what’s not working, but hang on to what really drives the business. It takes courage to let go of comfortable routines and focus on what actually moves you forward. But if you get it right, lean data governance sets you apart. Decisions get faster, friction drops, and you gain a real edge in a world where data rules.

Summary Box:

Twitter and Instagram prove that transformation is both possible and necessary for anyone ready to focus, simplify, and take action. For lean data governance to be effective, forget about simply piling on more rules and calling it done. Stand up and make every process count.

“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 Lean Data Governance appeared first on Industry4o.com.

When Hackers Employ AI: Is It Time to Rethink our Security Defences?

The rapid expansion of artificial intelligence (AI) across industries has changed countless capabilities, delivering benefits in efficiency, automation, and innovation. However, the same technology has also become a powerful weapon in the hands of predators, essentially altering the landscape of digital threats and malicious activities. As cybercriminals leverage AI to craft more clever and obscure attacks, the level of data protection is questioned. Organisations must relook into their current security strategies to verify, redefine, enhance and remain fit for purpose.

Current Impact on Businesses: Key Metrics

As AI-powered cyber threats rise, businesses are experiencing tangible impacts. Here are some key metrics to illustrate the current landscape:

The Rise of AI-Powered Cyber Threats

Cybercriminals are increasingly leveraging artificial intelligence to enhance both the deception and efficiency of their data exploitation tactics. By implementing machine learning algorithms, these malicious actors can automate activities like credential theft, password cracking, vishing, smishing, and phishing on an unprecedented scale. AI not only accelerates the identification of vulnerabilities within an organization’s systems but also makes breaches and intrusions more challenging to detect and address promptly

Moreover, the emergence of generative AI has introduced new forms of deception, including highly personalised phishing emails and realistic fake content. These advancements empower cybercriminals to orchestrate multi-layered campaigns that adapt dynamically to security measures, leaving traditional defence mechanisms struggling to keep up. As the tools available to hackers become ever more advanced, the threat landscape continues to evolve at a rapid pace, demanding a fundamental rethinking of how we approach cyber security.

For instance, AI-driven deepfake technology has been utilized to produce highly realistic voice and video impersonations, which are exploited in social engineering attacks. Additionally, automated tools can swiftly scan extensive networks for vulnerabilities, outpacing human capabilities. Furthermore, AI-powered bots can execute coordinated attacks that dynamically adjust to the defences they encounter in real-time.

Risks on using Traditional Security models to manage AI Threats:

• Inability to detect unusual Threats: Traditional security models often rely on static signatures and predefined rules, making them ineffective against new and previously unseen AI-generated malware or attack techniques.

• Slow Response Times: Manual analysis and intervention are no match for the speed at which AI-powered attacks operate.

• Overwhelmed Resources: The sheer volume and complexity of AI-driven threats can quickly overwhelm traditional security teams and tools, resulting in missed alerts and insufficient coverage across networks and endpoints.

• Weakness in handling Social Engineering: Conventional defences are ill-equipped to recognise sophisticated social engineering tricks, such as deepfake-based phishing or voice impersonation, which can deceive even security-aware personnel.

• Limited Threat Intelligence Integration: Legacy tools often do not integrate with global threat intelligence platforms, missing out on real-time updates about emerging, AI-driven attack vectors.

• Reactive Rather Than Proactive Approaches: Traditional security models tend to respond to attacks after they occur, rather than predicting or preventing threats by analysing behavioural anomalies and unusual patterns.

• Lack of Automation: Without automation, traditional security operations centre (SOC) teams may become bogged down by repetitive tasks, unable to focus on strategizing against the most sophisticated threats.

Limitations of Traditional Security Tools

Traditional security solutions, such as signature-based antivirus software and static firewalls, were designed to detect known threats using predefined rules. While these tools are still essential for baseline protection, they struggle to keep pace with novel, AI-driven attacks that constantly evolve and mutate.

• Signature-based detection: Relies on known malware signatures, but AI attacks create new variants that evade detection.

• Rule-based firewalls: Static rules are easily bypassed by adaptive AI attack strategies.

• Manual monitoring: Human analysts struggle with the speed and scale of AI-driven threats.

In short, the dynamic and unpredictable nature of AI-powered threats exposes the limitations of conventional defences.

The Case for Next-Generation Cyber Security

To keep pace with AI-enabled hackers, organisations must augment their traditional defences with advanced, intelligent security solutions. These include:

• AI-driven threat detection: Security platforms that use machine learning and behavioural analytics to identify anomalies and suspicious activities, even those not seen before.

• Automated response systems: Tools that can respond to threats in real time, containing breaches and neutralising attacks without human intervention.

• Adaptive security frameworks: Solutions that continuously learn and update their defence mechanisms, staying one step ahead of evolving threats.

• Comprehensive threat intelligence: Integrating global threat data and predictive analytics to anticipate and block emerging attack patterns.

By embracing these next-generation tools, organisations can level the playing field against AI-enabled adversaries.While AI is crucial for modern cyber defence, it is not a silver bullet. Skilled security professionals are needed to interpret AI-generated insights, make strategic decisions, and respond to complex incidents.The integration of AI by hackers has significantly transformed the cybersecurity landscape and it’s time for the Organisations to reshape their existing cybersecurity posture to handle AI threats. By continuously evolving our defences, we can strive to stay ahead in this dynamic digital arms race.

About the Author :

Ms. Kavitha Srinivasulu
CCISO | DPO| DTO| CISA | CRISC | CISM | CGEIT | PCSM | IAPP AIGP | ISO42001: LA
Program Director: Cybersecurity & Data Privacy
Tata Consultancy Services

Ms. Kavitha Srinivasulu is an Award winning Technology Leader.

Ms. Kavitha Srinivasulu is a Senior Cyber Risk and Resilience executive with over 22 years of global leadership experience advising Boards and Executive Committees across Financial Services, Healthcare, Retail, Technology, and regulated industries.

Ms. Kavitha Srinivasulu has delivered and led large-scale, regulator-driven cybersecurity, AI driven, PCI, and SOC transformations for Tier-1 banks, global healthcare organisations, and highly regulated enterprises operating across the UK, EU, USA, APAC, and ANZ.

Ms. Kavitha Srinivasulu is a trusted advisor to Boards, C-suite, regulators, and global enterprises, consistently delivering resilient, compliant, and scalable cyber operating models.

Ms. Kavitha Srinivasulu is an Advisory Member in National Cyber Defence Research Centre (NCDRC)

Ms. Kavitha Srinivasulu is a Board Member of Women in CyberSecurity (WiCyS) India

Ms. Kavitha Srinivasulu is an Executive Committee Member at CyberEdBoard Community

Ms. Kavitha Srinivasulu is an ambassador at ISAC

Ms. Kavitha Srinivasulu is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/details/certifications/

https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/details/publications/

Ms. Kavitha Srinivasulu can be contacted at :

LinkedIn

Disclaimer :

“The views and opinions expressed by Ms. Kavitha Srinivasulu in this article are solely her own and do not represent the views of her company or her customers.”

Also read Ms. Kavitha’s earlier article:

The post Next Gen Cyber Security for AI Powered Cyber Threats appeared first on Industry4o.com.

Is ASI – Artificial Super Intelligence here? Are T-1000 Terminator machines appearing?

Remember the Terminator movies, where Arnold plays the killer/savior Cyborg from the future? The line ‘Hasta la vista baby’ or See You Later? Several films have portrayed robots and cyborgs as good and evil machines. But are they the future of ASI?

Artificial Super Intelligence (ASI) is a conceptual construct of the future stage of AI. ASI will feature systems that supersede areas or domains of human cognitive capabilities. Some of the domains are cognitive skills, creativity, reasoning, and problem-solving.

Sam Altman, CEO of OpenAI, terms the era of ASI “The Intelligence Age.” This is when computers manage to improve on many complex tasks that only humans can do. He says that ‘deep learning has really worked. Algorithms are developed that truly learn any distribution of data, and the underlying rule to compute and give better results.

Mo Gawdat, in his book, cautions us and argues that ASI will surpass human abilities, think, see into the future, predict outcomes, and even use sensors to see around physical and virtual corners. ASI bots will certainly help humans to solve problems, provide healthcare, and improve the quality of life.

Popular movies and books however paint a dystopian and malevolent view of ASI. This perception is reinforced by sci-fi movies like M3Gan, 2001: A Space Odyssey, Blade Runner, Westworld, Avengers: Age of Ultron, Ex Machina, and many more, where AI bots are evil. They subjugate humans, create wars, and so on. These fantasy movies should not be taken seriously.

However, what if the nightmare becomes true? Find out what ASI is, technology, use cases in this blog.

What are AI Levels

It is essential to understand the various levels of AI and what each level can do. AI is defined at seven levels, from Level 1 to Level 7. Figure 1 illustrates the 7 levels and what AI does at each level.

L1 is the basic level and uses rules and logic circuits to automate some tasks. It is used mainly to reduce manual work. We are now at L3 and L4. Cyborgs and other entities are at Level 7+ and can be termed as ASI.

Still a long, long way to go? Let us look at the current research and emerging trends in ASI.

What are the latest Trends in ASI?

However, ASI will need exponentially advanced processors, hardware, a new environment or operating system, and programming languages. Likely systems in the future are discussed in the next section.

What future chips and Hardware are needed for ASI

Full-blown Artificial superintelligence will require a new generation and class of chips and processors, and advanced quantum computers. While these techs are not yet built, an assessment of future hardware and chips is speculated below.

New generation of frameworks and programming languages are needed for ASI. These are examined in the next section.

OpenCog Hyperon platform and MeTTa Programming Language for ASI

Artificial superintelligence will need a new platform operating system and programming language, among other developments. Let us look at OpenCog Hyperon and MeTTa Programming Language.

OpenCog Hyperon

MeTTa Programming Language

MeTTa, or Meta Type Talk, is a new programming language from OpenCog Hyperon. It is the language of thought and provides fundamental and general functionalities to handle symbols, groundings, variables, types, substitutions, and pattern matching.

The main objective of MeTTa is to provide a robust, flexible, and mathematically grounded foundation for developing ASI. It is used for human developers to script AI processes and for AI algorithms themselves to perform learning and reasoning activities.

MeTTa is the firmware that holds components of the OpenCog Hyperon cognitive architecture, allowing for seamless interoperability between diverse AI modules such as neural networks and probabilistic logic networks. It is the core running environment for the ASI Chain, a layer-1 blockchain infrastructure for decentralized AI applications.

Important components of MeTTa are:

Beneficial use cases of ASI

This is the most important question of artificial superintelligence. ‘How and where will artificial superintelligence be used?’ Let us look at areas where artificial superintelligence can be implemented for positive impacts.

Military use cases of ASI

Remember Skynet, the malevolent ASI from Terminator that took over humanity? Not sure if this will happen. But artificial superintelligence has several critical military use cases that are beyond current human and AI applications. It is expected to transform warfare and defense strategies.

Some of the possible use cases are:

The list is endless, as seen in several movies. However, the costs and resources needed to build these systems will probably exceed the global GDP. So, you can relax for now.

Can ASI take over humanity?

Manuel Alfonseca et al. argue that while ASI will be invaluable, it should be contained and secured so that it does not ‘escape’ and create a catastrophe for the world. One problem is that ASI will have all the knowledge that humans have, and it will figure out a way to crack the containment.

The concept of AI “taking over” humanity as narrated in science fiction is a speculative and controversial topic. Advanced artificial superintelligence can pose significant risks if not developed responsibly. A rogue military dictator or an evil corporation can attempt this exercise.

Such an effort will need massive, massive computing resources, global collaboration, and augmentation of human capabilities. This may not happen. However, one can never predict the irrational actions of deranged leaders.

Summing Up

The blog examined the fascinating concept of Artificial Super Intelligence (ASI). This technology of the future is expected to surpass human intelligence and cognitive skills. The technology is in the early development stage, but research is ongoing.

Several resources, such as hardware and processors, languages, frameworks, and investment, are needed. ASI promises several benefits that go beyond what humans can conceptualize. Certain negative impacts in military applications are also predicted.

Can artificial superintelligence compose original classics of Clapton, Willie, Waylon, Dolly, Emmylou, JP, IM, Motorhead, and other Monsters of Rock? An emphatic No! Once in a lifetime, geniuses created these classics, and ASI will attempt to copy them. I hope this never happens.

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Banner Image Credit: Shashi Kadapa with Google Gemini

About the author :

Mr. Shashi Kadapa

Based in Pune, India, Mr. Shashi Kadapa is an engineer, MBA and has worked with leading IT and manufacturing firms. A multi-hyphenate, he has roles as a technical writer, and SEO content writer with a focus on IT and tech topics.

Creative fiction is his passion, and he serves as the managing editor of ActiveMuse, a journal of literature. His stories across multiple genres are published in more than 45 US and UK anthologies.

His creative works

Mr. Shashi Kadapa can be contacted at :

E-mail | LinkedIn | Blog | Mobile : +91 7387492371

Also read Mr. Shashi Kadapa earlier article:

The post Artificial Super Intelligence ( ASI ) appeared first on Industry4o.com.

Abstract

AI and machine learning are fundamentally transforming capital markets beyond automation across three critical areas: market microstructure analysis, portfolio construction, and advanced risk modelling. Real-world implementations by JPMorgan’s LOXM, BlackRock’s Aladdin, and Morgan Stanley’s Portfolio Risk Platform demonstrate measurable improvements in trade execution, risk assessment, and optimization. These technologies enable real-time processing of vast data and complex market relationship identification, achieving 25-40% performance improvements while managing trillions through AI-enhanced platforms.

Core Applications in Market Operations

Market Microstructure Analysis

Advanced algorithmic systems have become powerful platforms in processing vast quantities of data simultaneously, revealing concealed market behaviours and emerging profit opportunities. JPMorgan Chase exemplifies this technological evolution through their groundbreaking LOXM (Liquidity Optimization Execution Machine), which leverages Azure Machine Learning to optimize client order execution. This intelligent system continuously analyses historical trade patterns while dynamically adjusting to evolving market conditions. The results demonstrate remarkable performance improvements: a 40% reduction in processing time and 25% improvement in successful trade execution, underscoring the transformative impact of AI-driven market analysis on modern trading infrastructure.

Portfolio Construction Evolution

Advanced intelligent systems are revolutionizing portfolio management through three key innovations:

Dynamic Real-Time Rebalancing Unlike conventional portfolio management with predetermined rebalancing schedules, intelligent systems enable continuous optimization. These platforms monitor market volatility, asset correlations, and macroeconomic developments in real-time, automatically adjusting allocations to capture opportunities while minimizing risk. This eliminates human delays and maintains optimal distribution without manual intervention, particularly valuable for evolving markets.

BlackRock’s Aladdin (Asset Liability Debt and Derivative Investment Network) exemplifies this advancement, utilizing machine learning to analyse thousands of portfolios daily. The platform’s growth reflects its effectiveness: investment advisory and administration fees from Aladdin-based assets under management surged from $355.5 million in 2023 to $926.5 million in 2024.

As of 2020, Aladdin oversees more than $21.3 trillion in assets, with technology services revenue reaching a record $1.6 billion in 2024.

Fig 1: Aladdin Technology Services Revenue (2018-2024)

Alternative Data Integration Modern portfolio systems extend beyond conventional financial metrics like earnings reports and price movements, incorporating diverse data sources including satellite imagery, ESG sustainability indicators, consumer transaction patterns, geospatial intelligence, and supply chain analytics. This comprehensive data integration enables AI-driven platforms to identify subtle market trends, consumer behaviour shifts, and geopolitical risks, facilitating more strategic and anticipatory investment decisions.

BlackRock’s Aladdin Climate demonstrates this capability by quantifying climate-related risks and opportunities in financial terms, combining climate science, policy projections, asset information, and financial modelling to generate climate-adjusted valuations and risk assessments.

Personalized Risk Management Intelligent systems customize portfolio strategies based on individual investor characteristics, including risk tolerance, investment objectives, and timeline preferences. These platforms analyse behavioural patterns and financial goals to dynamically modify asset allocations, implement protective hedging strategies, and recommend personalized approaches—whether targeting aggressive growth, steady income generation, or capital preservation.

JPMorgan’s Athena platform illustrates this personalization capability, delivering comprehensive cross-asset risk assessment, pricing, and trade management solutions. The system processes billions of daily risk calculations while achieving a 30% reduction in processing time and approximately 80% decrease in hourly calculation costs.

Risk Modelling Advancement

Modern computational approaches have transformed risk assessment by:

1. Identifying Complex, Non-Linear Relationships Between Market Variables

Traditional linear risk models miss complex market correlations. Advanced machine learning—neural networks and decision trees—captures non-linear relationships conventional models overlook. JPMC’s AI research demonstrates this through risk-sensitive decision trees using reinforcement learning for trade execution, helping investors make better risk-adjusted decisions by understanding underlying price dynamics.

The application of these technologies has achieved impressive results and inspired broader uses of deep neural networks for agent representation in trading applications. Deep Q-trading systems have emerged as effective solutions for algorithmic trading, while LSTM (Long Short-Term Memory) representations of reinforcement learning trading agents have demonstrated significant potential in market applications.

2. Enhancing the Accuracy of Tail Event Predictions

Extreme market events like crashes are difficult to predict using traditional models that inadequately weigh risks. AI-driven deep learning and Bayesian networks improve tail risk forecasting by analysing vast datasets. Morgan Stanley’s Portfolio Platform, combined with BlackRock’s Aladdin Risk Engine, helps foresee potential impacts of market shocks on portfolios.

These advanced systems simulate a wider range of market scenarios, utilizing thousands of potential risk factors to help investors and their financial advisors understand how various risks may affect their holdings, including both internal investments and external positions. Morgan Stanley has further strengthened this capability through their patent (US8370241B1) for systems and methods that utilize probabilistic networks—essentially Bayesian networks—to analyse financial models.

This patented approach enables dynamic modelling of financial variables and their interdependencies, significantly aiding in the assessment of complex risk scenarios and tail events. By incorporating these sophisticated AI-driven techniques, the platform enhances portfolio resilience through proactive risk mitigation strategies, allowing investors to better prepare for and respond to extreme market conditions that could otherwise cause substantial losses.

3. Adapting to Shifts in Market Regimes

Financial markets undergo structural shifts from monetary policy, geopolitical events, or liquidity crises, altering dynamics. Traditional models struggle with real-time adjustment, while intelligent systems use adaptive learning algorithms to detect regime shifts early through pattern recognition.

S&P’s Kensho AI platform NERD identifies 142 million entities—companies, people, organizations, events, places—across market regimes, connecting them to S&P Capital IQ data . This entity recognition monitors multiple information streams simultaneously, providing early warning signals for market changes and enabling dynamic investment strategy recalibration before risks materialize.

AI-driven platforms continuously learn from market data and adapt to regime shifts, unlike static traditional models, ensuring risk strategies remain effective and protecting investors against unexpected transitions.

Conclusion

The next evolution in intelligent financial systems will likely centre on quantum computing integration, federated learning across institutions, and autonomous trading ecosystems that require minimal human intervention. Financial institutions should prioritize three strategic imperatives: investing in scalable cloud infrastructure to handle exponentially growing data volumes, developing cross-functional AI expertise through targeted talent acquisition and training programs, and establishing robust governance frameworks to ensure ethical AI deployment while maintaining regulatory compliance. Organizations that proactively build these capabilities today will be positioned to capitalize on emerging technologies like quantum-enhanced portfolio optimization and real-time regulatory reporting systems.

The organizations that will thrive in this intelligent finance era are those that view AI not merely as a technological upgrade, but as a fundamental reimagining of how financial markets can operate more efficiently, transparently, and effectively for all participants.

The dawn of intelligent financial systems is not a distant future—it is happening now, reshaping capital markets one algorithm at a time.

About the Authors :

Mr. Bhushan Joshi
Competency Leader for Banking ISV, Financial Markets and Wealth Management
IBM

Mr. Bhushan Joshi is Competency Leader of Financial Markets and Wealth in IBM India.

Mr. Bhushan Joshi has 25+ years of experience in Driving Business Transformation for clients in Banking, Capital Markets and Wealth Management Industry.

Mr. Bhushan Joshi  has played variety of roles including setting up new practices, working with business units and ISV ecosystem partners to drive growth, in program management and working on strategic consulting engagements for CxO level clients at Global Banks in Legacy Modernization with Microservices and Cloud, Business Architecture, Process Strategy and Optimization, Package Implementations, Vendor selection and Innovation.

Mr. Bhushan Joshi Specialties are Consulting, Digital Banking, Capital Markets and Wealth Management

Mr. Bhushan Joshi is Bestowed with the following Licenses & Certifications :

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

Mr. Bhushan Joshi can be contacted at:

LinkedIn

Mr. Saravanakumar Dinakaran
Senior Managing Consultant
IBM

Mr. Saravanakumar Dinakaran is a Managing Consultant at IBM with over 23
years of experience leading business transformation initiatives across Financial Markets and Investment Banking.

Mr. Saravanakumar Dinakaran brings deep expertise in front-office trading support, risk management, regulatory reporting, and end-to-end business analysis.

Mr. Saravanakumar Dinakaran with strong domain knowledge across all major asset classes—including Equities, Debt, Derivatives, and OTC products—he has successfully partnered with leading global banks and  regulatory bodies to deliver complex, high-impact programs.

Mr. Saravanakumar Dinakaran recent work focuses on applying AI-driven capabilities to modernize capital markets, enhance risk controls, and accelerate digital transformation across the financial ecosystem.

Mr. Saravanakumar Dinakaran is Bestowed with the following Licenses & Certifications :

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

Mr. Saravanakumar Dinakaran can be contacted at:

LinkedIn

References and Sources

https://www.linkedin.com/pulse/ai-powered-trading-financial-revolution-driven-guy-massey-8wole/

https://thefinancialtechnologyreport.com/blackrocks-powerful-aladdin-platform-over-20-trillion-and-counting/

https://s24.q4cdn.com/856567660/files/doc_financials/2025/ar/BLK_AR24.pdf

https://www.blackrock.com/aladdin/products/aladdin-climate

https://www.efinancialcareers.com/news/2022/05/jpmorgan-technology-jobs-and-spending

https://advisor.morganstanley.com/the-stalwart-group/articles/investing/portfolio-risk-management-prepare-unexpected?

https://patents.google.com/patent/US8370241B1/en

https://kensho.com/nerd

The post How AI is Revolutionizing Capital Markets appeared first on Industry4o.com.

Data Storytelling – Part 1

The first part ( PART 1 ) set the stage: why narrative makes data memorable, how context changes interpretation, why scale needs translation, and where action comes from. It also laid out the groundwork for building a data story-clarifying purpose, knowing the audience, and finding the signal in the noise. With that foundation established, we turn to shaping the arc, choosing visuals, and carrying the story across the finish line.

Shape the arc and connect to human stakes

A data story needs a beginning, a middle, and an end. Set the scene. State the tension or question. Make the audience feel the stakes. Then walk them through the evidence in a sequence that reveals what matters, ideally with a moment of surprise that forces a rethink. Close with what they should do next.

And keep it human. People lean in when they see themselves, their customers, or their teams on the page. Emotion isn’t fluff; it’s how we remember. A customer quote, a frontline anecdote, a before-and-after image, a simple analogy that maps a complex system to something familiar can shift a room’s temperature. That human thread keeps abstraction from draining away urgency.

Visuals that carry their own weight

Choose visuals for what they do best. A line chart for a trend. Side-by-side bars when the question is “which is bigger.” A heat map when you want to show concentration. A scatter when you want people to see a relationship (and where it breaks). Label clearly. Use titles that say the point, not just the topic (“Q3 growth came from three products” beats “Q3 product performance”). Keep design clean and consistent, so your audience can spend their attention on meaning, not on deciphering.

When interactivity helps, use it; filters to let stakeholders explore their region, toggles that show how assumptions shift outcomes. But make sure the core message stands still even when the audience starts to click around.

Practice the delivery and stay honest

Stories become real in the room. Rehearse to shorten the path to the main point and to hear where the story drags. Share a draft with someone who mirrors your audience and ask them what they remember five minutes later. If that memory aligns with your intended message, you’re close. If not, adjust.

Above all, be straight with the data. Surface caveats in plain language. Make uncertainty visible. Don’t bend a chart to juice a point. Trust built in one meeting saves you from skepticism in the next.

Why Diocles works as a teacher

Let’s add a few more stones to the mosaic. Diocles started in the provinces and came to Rome as a young man, joining one faction, then another, before finding his longest stretch of success with the Reds. He drove quadrigae (four-horse teams) again and again, famously winning from behind in races that rewarded nerve and timing. He retired at 42, a rare age to walk away from a sport that often left men crushed under wheels or dragged by reins tied at the waist, knives at the ready to cut free. His winnings were enormous; moreover, they were recognized by his contemporaries with an inscription that lists the sum, a record meant to be seen and believed.

Even the comparisons people use to translate his fortune into modern terms tell a story. The sums are offered in grain for the city or pay for soldiers to make the number land for a Roman audience and, by analogy, for us. In different frames, the same quantity points to different meanings: abundance, power, spectacle. That’s context. That’s perspective. It’s the muscle that makes a number relevant rather than merely large.

From dashboard to decision: what the audience needs from you

• Start on the ground. What’s the decision in front of your audience? Once you can state it in a sentence, you can trim away the chart junk that doesn’t move that decision forward.

• Use a clear arc. One screen = one point. If you need three points, that’s three screens. Guide the audience with signposts: a question as a subtitle, a takeaway sentence as a caption, a visual that answers it.

• Put the caveats where they belong. If you’re presenting to the C‑suite, keep caveats on backup slides and summarize them in words. If you’re with a technical team, show method and sensitivity analysis so they can trust the conclusion.

• Close the loop with action. No “therefore” is complete without a next step—run a pilot, set a threshold, stop a project, call three customers, reroute a budget line. Tie the action to a metric so you can come back with a result.

Looking ahead: tools will accelerate the “find,” humans will win the “tell.” 

AI and machine learning are astonishing at sifting mountains of data and surfacing patterns you would’ve missed otherwise. They accelerate the “find” part of the work. But turning those findings into a story that feels true to a specific team on a specific Tuesday; that still takes a human hand. Empathy, audience sense, ethical judgment, and the nuance to weigh context are what make a story land and a decision stick. Expect more interactivity, more personalization, and more adaptive narratives that change as the audience explores. The aim won’t change: help people grasp what the numbers say and then move, together, in the right direction.

The quiet coda: what data can’t hold 

For all the wealth Diocles amassed, the person remains hazy. We can read his totals, trace his wins, even sketch his tactics. But the private cost (the days he limped home, the fear that came after a crash in the lane beside him, the friendships made and lost in the stables) doesn’t sit inside theinscription. That’s not a failure of the record. It’s a reminder. Data reflects life, but never all of it. There’s always a remainder.

That remainder doesn’t argue against data storytelling. It argues for humility in the telling. We work to clarify, to give the audience enough context to think clearly and to act. We make scales feel human with the right comparisons. We choose visuals that show rather than decorate. We invite emotion because we’re speaking to people, not machines. And we leave room for what can’t be counted to matter.

The arena empties. The dust settles. The inscription endures. We stand in front of it and do what humans do: we tell a story that helps us see.

“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 2 appeared first on Industry4o.com.

Have become an avid anlayser of History of Businesses, as part of my recent Professional Engagement of evaluating Companies. Its intriguing to see different styles of the Business Managers, probably defined by their personalities and personal experiences. I repeat, irrespective of Size and Stature, its clear that a Leader’s Decision (Entrepreneur or Professional) is completely dependent on his experience. Once bitten, twice shy types mostly. This is probably from where Resistance to Change also emulates.

In Business & in Real Life, there are two approaches – Outside-In and Inside-Out..

Typical Entrepreneurs, Professionals and Leaders believe in Outside-In as the best approach – laying emphasis on SPELT (Social Political Economic Legal & Technological Environment, summed up in SWOT Analysis) based decisions. Which is quite logical …which also follows Porters Principles of Five Forces.

However in my view, true Innovators and Disruptors believe in Inside-Out approach. It typically envisions a scenario which believes the Market and Society (which is essentially the Market) at large may not know what they want. This in Management Jargon is referred to as Concept Selling & Creativity. I always vouch for this approach.

While the first approach is believed to understand and mitigate risks, in the second approach, risk is the essence and hence returns are disproportionately high as a reward for the high stakes. Ofcourse, failures will always be there…it takes confidence and courage to take the second approach. World over, its a proven fact that, successes even if it is around 10% (meaning as high as 90% failures), will more than Compensate and Contribute to Growth & Profitability. This approach is what brings Leap Frog Growth rather than market dependent incremental growth as in the first approach.

For the first approach, Competitiveness, Consistency and Efficiencies should be at their best to sustain success. Whereas, by nature, the second approach inbuilds inefficiency and thrives only on newness and market attractiveness – mostly due to the First Mover advantage.

In my assessment, for first one, good examples shall be Lee Iacocca… for the second i would rate Akio Marita, Steve Jobs and more recently, Elon Musk. Google, Microsoft, Intel, N-Vidia – off late i believe have lost out on their abilities to be creative – may be due market pressures and share holder expectations, these always being the limiting factors
especially when businesses are investor driven, which in the recent years are too many.

In Indian context, i place L&T, Jindals, Tatas in first category. Though many may not agree, I place Mukesh Ambani, Damanias and Adanis in the second category.

Drawing parallel to our personal lives, many try to evaluate others (Outside – In approach) , rather than trusting themselves (Inside – Out approach). In the process we become judgmental, either consciously or otherwise. Though colloquial, easiest examples could Indian Arranged Marriages as an example for the First Approach and Love Marriages for the second approach.

Irrespective of the approaches, which mostly is associated with Personality, ability to recognise that going is not right and take course correction at that instant is what ensures sustainable success. Then its all about timing – a withdrawal or an exit strategy.

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 Personal Or Professional, Its all about Style and Substance… 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.

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.

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.

Bobby Leach fancied himself a man of daring. In 1911, he climbed into a steel barrel lined with padding, secured himself like a sack of flour, and plunged over Niagara Falls. He survived, albeit with a fractured jaw, smashed kneecaps, and a long stay in the hospital. Years later, after countless lectures and victory laps describing his reckless ride, Leach met his end not at the hands of nature’s fury, but by slipping on an orange peel during a stroll in New Zealand.

The fall didn’t actually kill Bobby, but here’s my point: There’s something remarkably poetic about this. Leach faced down a natural behemoth and lived. But the very ordinariness of his death is what gets people’s attention. It’s almost Shakespearean: a man who survived the unthinkable taken out by the mundane.

In 2013, Target launched itself into Canada in a fashion that would’ve made Leach proud. A hundred-plus stores opened nearly overnight. People watched, some in admiration, some in awe, and others with concern. There was momentum, media attention, corporate confidence. It looked like a barrel headed for a record-setting splash, if you’ll pardon the pun.

By 2015, it was over. Stores shuttered. Employees laid off. Target Canada was officially out of business. Done. Finished. Kaput.

No catastrophic market collapse. No economic tsunami. Just a series of basic mistakes that no one had the courage (or the clarity) to point out in time. Problems with inventory data, mismatched systems, supply chain snafus, and unrealistic expectations quietly festered. The company that looked fearless in its plunge didn’t fall prey to competition. It, well, slipped on its own orange peel. Again, pardon the pun.

And behind all of this? A massive failure to communicate through data. Not a lack of information, mind you, but a failure to tell the right story to the right people at the right time.

The Launch That Looked Like a Victory Lap

From the outside, Target’s entrance into Canada was textbook. The company moved quickly, acquiring retail spaces and aggressively opening locations from coast to coast. There was fanfare. There were commercials. There was confidence in abundance.

But speed turned out to be the enemy of sense. Their enterprise resource planning (ERP) systems weren’t fully ready. The company tried to build out complex supply chain infrastructure while simultaneously onboarding thousands of employees and managing new vendors. Most of the data that fed purchasing and inventory systems had been entered manually—by interns, no less.

And in the midst of that whirlwind, no one paused long enough to ask whether the numbers made sense.

The Small Mistakes That Became an Avalanche

Here’s the irony: Target had ample data. Mountains of it. But their teams couldn’t align those numbers with the broader business narrative. Product availability in the systems didn’t match what was on the shelves. Distribution centers sent duplicate stock to some stores, while others went empty. Customers arrived expecting the “Target magic” they’d heard about from south of the border. Instead, they found barren aisles and mismatched pricing.

Customer confidence eroded quickly, and the disconnect between expectation and experience proved too great to mend. After being in the Canadian market for less than two years, Target decided to withdraw. The results were stark: 133 stores closed, almost 18,000 employees impacted, and the company incurred a pre-tax loss of $5.4 billion. For a brand known for its precision and refinement, this downfall was both visible and expensive.

This failure cannot simply be attributed to bad luck or outside influences. The real issue that led to this collapse was a failure in the way insights were communicated and framed. Data trickled into different corners of the business, but it never cohered into a story that sparked alignment or foresight. Product shortages, system misfires, and unmet customer expectations—these weren’t unpredictable. They were preventable, had the signals been woven together into a clear narrative.

Just like Bobby Leach’s demise wasn’t from a fruit peel alone. While walking through Auckland, he slipped and broke his leg. The injury led to gangrene, and ultimately, his leg was amputated. Despite surgery, complications ensued, and he died shortly after. The fall didn’t kill him, but the consequences of unaddressed trauma did. In the same way, Target didn’t fall from a lack of ambition, but from failing to read the signs their own data was waving in front of them.

The Story Never Told

What could’ve prevented this?

Good data storytelling isn’t some decorative flourish at the end of a deck. It’s the core mechanism that transforms raw figures into actionable understanding. It’s the voice that stands up in a meeting and says, “Here’s what these numbers mean. Here’s why it matters. Here’s what we risk if we ignore it.” It’s the means by which observations become conversations—between functions, across silos, and within leadership ranks. It’s a way of translating uncertainty into navigable terrain.

Organizations often invest in tools that surface performance metrics or trend lines, but rarely in frameworks that ensure those metrics are understood and acted on. The result? A warehouse full of barrels, but no direction on where the river is headed.

Target didn’t suffer from a lack of intelligence. They suffered from a lack of insight translation. And insight that isn’t understood isn’t insight at all.

Storytelling Done Right: What It Looks Like in Practice

Context Is the Current

Before Bobby Leach took his dive, the current had already decided part of his fate. The river above Niagara doesn’t plunge suddenly, but rather, it accelerates in intensity, shaping the descent long before the drop begins. In much the same way, context isn’t a backdrop in data storytelling; it’s the current that determines whether your insights will glide or spiral.

Without context, data points drift aimlessly. Metrics pulled in isolation (like customer foot traffic, product velocity, or market share) may appear self-evident. But without understanding what shaped them (a new competitor, regional holidays, shifting consumer sentiment), those metrics mislead more than they inform.

A strong data storyteller is like a seasoned guide upstream. They see not just where the data is, but where it’s heading, based on surrounding factors. That awareness lets them tell a story that doesn’t merely describe, but anticipates; that is, one that’s built on the nuances of timing, external forces, and system interdependencies.

Empathy Is the Harness

When Leach strapped into his barrel, he didn’t rely on bravado alone; he used a harness, braces, and padding. He understood that survival meant working with the chaos, not pretending it didn’t exist. In the same spirit, empathy is what secures your data story for the audience. It keeps it from becoming a hollow stunt and transforms it into a shared journey.

Too often, data presentations are crafted as if everyone in the room is equally versed in the same metrics or concerns. But CFOs, logistics managers, and regional directors all view the business through different lenses. The story has to meet each group where they are, using terms they trust and framing that fits their priorities.

Empathy also demands clarity over complexity. Rather than simplifying the data, it involves respecting your audience’s time and cognitive load. A cluttered dashboard or a jargon-laden report disengages people before you’ve even reached the point. Empathy guides you to present information in a way that affirms their role in the story and equips them to act.

Tension Drives Action

The moment before Leach went over the brink, I can imagine that there was silence. The crowds were still; watching the barrel edge toward inevitability. That tension gave the spectacle its power. You knew what was coming, but not whether he’d come out of it alive. Guess what? Data storytelling thrives on that same dynamic.

Tension in a business narrative comes not from fear-mongering, but from contrasts: between the expected and the actual; between the current path and its plausible alternatives. Showing that customer churn has risen 15% is useful. But showing that churn is accelerating while satisfaction scores remain static? Now that’s tension. It implies an unknown force at play, and compels decision-makers to ask “why now?” and “what’s next?”

Effective data storytelling doesn’t stop at surfacing risk; it sharpens focus on what’s at stake when key insights go unheeded. When it’s done well, it creates urgency without panic, clarity without oversimplification. It’s the difference between watching a barrel float by and realizing you might be in one yourself!

Lessons from the Splash Zone

If Leach had had a rope tied to that barrel, maybe his ride would’ve been a bit less treacherous. By the same token, if Target had tied their strategy to a more grounded narrative; i.e., one informed by real-time insight and cross-functional alignment, they might still be standing in Canada.

Organizations don’t need more dashboards. They need better storytellers.

Leaders should invest not only in data infrastructure but also in building a culture where insight travels fast, where anomalies trigger conversation, and where every initiative starts with one simple demand: “Tell me the story behind these numbers.”

What ties both stories together is this: vision alone isn’t enough. It’s how insight moves across an organization that determines whether bold actions turn into brilliant outcomes or embarrassing setbacks.

CONCLUSION: Final Word from a Fellow Storyteller

Over the years, it becomes easy to spot the inflection points where a project veers off course; not because the data was wrong, but because the narrative never reached the people who needed it most. Strong organizations unravel because they mistook analysis for understanding.

Don’t be the company that barrels toward glory and trips on its own untied shoelaces.

Learn the craft of data storytelling. Teach it. Champion it. Because what you don’t explain clearly today may be the orange peel that takes you down tomorrow. A good data story needs direction, a harness, and just enough tension to keep its audience awake. But most of all, it needs to be told. Because it’s not the leap that ruins you. It’s the silence that follows when no one hears the story that could have changed the ending.

“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 appeared first on Industry4o.com.

Introduction

Artificial Intelligence (AI) has evolved from rigid rule-based systems to powerful, data-driven learning models that now permeate every major industry. As enterprises adopt AI to drive efficiency, enhance decision-making, and offer personalisation at scale, two foundational paradigms shape this transformation:

Traditional AI – Symbolic or rule-based systems built on logic and expert knowledge.

Discriminative AI – Data-centric models that learn to make distinctions or predictions from labelled data.

Understanding the differences between these paradigms is critical for leaders evaluating AI adoption paths. This article explores their evolution, real-world use cases, and future trajectories, with a focus on model complexity, explainability, and business value.

Evolution of AI Paradigms

Traditional AI (Symbolic AI)

Traditional AI refers to systems that mimic human reasoning using a set of predefined rules. These systems, popular from the 1960s through the early 2000s, relied heavily on:

• Knowledge bases designed by domain experts.
• Inference engines to perform reasoning.
• Deterministic decision trees and if-then logic.

Example: Early fraud detection systems used hard-coded rules like:
“If transaction amount > $10,000 AND country = Nigeria, then flag for review.”

While interpretable and easy to audit, these systems struggled with:

• Adaptability to new data patterns.
• Complexity scaling in dynamic environments.

Discriminative AI (Machine Learning-Based AI)

Discriminative AI models learn from data to predict outcomes or classify inputs, focusing on P(Y|X) — the probability of output Y given input X.

Key characteristics include:

• Learning from data rather than rules.
• Higher accuracy in prediction tasks.
• Continuous improvement as more data becomes available.

Example: A telecom churn model built using XGBoost can analyze 100+ customer features to predict the likelihood of churn with over 90% precision.

Real-World Industry Use Cases

1. Banking and Financial Services

Real Case: Mastercard uses machine learning to detect fraudulent patterns in less than 300 milliseconds per transaction, combining customer behaviour history with external threat intel.

2. Healthcare and Life Sciences

Real Case: Google Health’s AI models now assist in diagnosing diabetic retinopathy with accuracy comparable to board-certified ophthalmologists.

3. Retail and E-Commerce

Real Case: Amazon’s recommendation engine uses discriminative algorithms like neural collaborative filtering (NCF) to drive 35% of total revenue.

4. Manufacturing and Smart Industry

Real Case: General Electric (GE) deploys discriminative AI to predict turbine and jet engine failures, saving millions in unplanned outages

Model Complexity and Comparison

Below is a comparison of two most widely used complex models in each paradigm:

Why Discriminative AI Dominates Today

Today’s digital environments — from banking to healthcare — demand models that learn, adapt, and scale. Discriminative AI meets these demands with architectures that continuously improve as more data becomes available.

The Future: Hybrid AI and Beyond

As we move toward a future of explainable, trustworthy AI, there’s growing momentum to blend symbolic reasoning with statistical learning:

Neuro-symbolic AI: Combines deep learning for perception with symbolic logic for reasoning (e.g., IBM Project Debater).

Explainable ML: Tools like SHAP, LIME, and counterfactual analysis make black-box discriminative models more transparent.

Low-code/No-code AI: Democratizing access through platforms that fuse logic-driven workflows with AI insights.

Closing Thoughts

As the AI landscape continues to evolve, the divide between Traditional and Discriminative AI is no longer one of opposition but of opportunity for convergence. Traditional AI, with its foundations in rule-based reasoning and symbolic logic, offers unmatched transparency and interpretability-essential in domains like legal tech, finance compliance, and healthcare diagnostics where traceability is critical. Discriminative AI, on the other hand, has shown explosive growth due to its ability to learn from massive datasets and deliver high-performance predictions in real-time.

However, neither paradigm alone holds all the answers. In an era where accuracy must coexist with accountability, and speed must align with explainability, the future belongs to organizations that can synergize both approaches. Emerging models such as neuro-symbolic AI and causal learning are paving the way for hybrid intelligence—models that not only predict what will happen but also explain why.

The strategic imperative for industry leaders is clear: embrace the strengths of both worlds. Build AI systems that are not just smart, but also trustworthy, transparent, and aligned with human values. The next frontier in AI is not just about automation—it’s about augmented intelligence that empowers responsible innovation.

References & Further Reading

About the Author:

Mr. Abhaya Kant Srivastava

Consulting Partner – APAC, Risk & Compliance, BFSI Strategic Initiative

Tata Consultancy Services (TCS)

Mr. Abhaya Kant Srivastava is a seasoned Risk Analytics senior leader in banking and financial sector with over 18 years of experience.

Mr. Abhaya Kant Srivastava is currently a Consulting Partner – APAC, Strategic Head Model Risk and Analytics with Tata Consultancy Services (TCS), where he leads the strategic initiative to acquire new projects in the area of risk and compliance analytics and advise clients to deploy advanced statistical and mathematical modelling.

Mr. Abhaya Kant Srivastava, prior to Tata Consultancy Services (TCS), headed a big size team for India Risk Analytics and Data Services Practice at Northern Trust Corporation.

Before Northern Trust Corporation, Mr. Abhaya Kant Srivastava worked at KPMG Global Services, Genpact, EXL and startups like Essex Lake Group and Cognilytics Consulting to lead risk and analytics.

Mr. Abhaya Kant Srivastava is a B.Sc. (Honours) in Statistics – Gold Medallist from Institute of Science – Banaras Hindu University, M.Sc. in Statistics from Indian Institute of Technology, Kanpur and currently doing executive Ph.D. in Statistics/Machine Learning from Indian Institute of Management, Lucknow.

Mr. Abhaya Kant Srivastava, also has a certification in “Artificial Intelligence for Senior Leaders ” from Indian Institute of Management, Bangalore.

Mr. Abhaya Kant Srivastava is Accorded with the following Honors & Awards :

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

Mr. Abhaya Kant Srivastava is Bestowed with the following Licences & Certifications :

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

Mr. Abhaya Kant Srivastava has Led the following Projects :

https://www.linkedin.com/in/abhayaiitk/details/projects/

Mr. Abhaya Kant Srivastava can be contacted :

E-mail | LinkedIn

Also read Mr. Abhaya Kant Srivastava‘s earlier article:

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In the world of insurance, AI adoption is no longer a question of “why”- it’s about “how” and “what truly moves the needle.”

At DSW, we believe the future of AI in insurance isn’t just about building individual use cases. It’s about aligning every use case – every model, every agent, every interaction — to a clear Statement of Business Purpose (SBP).

Because when AI connects directly to what the business is trying to achieve, adoption becomes not just easier — it becomes inevitable.

DSW UnifyAI: A Platform Built Around Business Purpose

UnifyAI isn’t just a platform to build and deploy AI/ML or GenAI use cases – it’s a real-time AI engine designed to:

Our approach is simple: Start with your business purpose. Then, map the AI/ML and GenAI use cases needed to get there.

What Do Business Purposes in Insurance Look Like?

Here are a few key Statements of Business Purpose (SBPs), but not limited to -emerging across insurers, where we see AI gaining real traction :

Why Is This Shift Important?

When AI use cases operate in isolation, value stays fragmented.

But when they are linked back to a unifying business purpose, here’s what changes:

They’re easier to justify internally, outcomes are measurable and visible, AI adoption becomes part of the business rhythm, stakeholders across functions rally around impact, it unlocks cross-functional compounding value

The DSW UnifyAI Advantage

DSW is not just bringing a platform. We’re bringing an AI operating layer, along with the expertise to walk with insurers through:

More than just an UnifyAI platform – we are a committed AI partner, co-owning the journey to embed intelligence into the very core of insurance workflows.

Thought: AI That Understands Why

AI adoption tied to “what the business cares about” isn’t just more impactful – it’s more sustainable.

That’s the DSW UnifyAI difference.

Let’s build for outcomes, not just experiments.

Let’s align to purpose, not just pilots.

Let’s make AI count – where it matters most.

About the Author :

Mr. Sandeep Khuperkar
Founder and Chief Executive Officer (CEO)
DSW | Data Science Wizards

Ambassador – AI Frontier Network

Entrepreneur | Open-Source Advocate | CEO Data Science Wizards | Founder VYugma and Regional Mentor of Change AIM Niti Ayog (Govt. Of India) | Adjunct Professor APSIT Engineering College | Isha Foundation Volunteer.

Before Founding VYugma and Data Science Wizards, Mr. Sandeep Khuperkar was the CTO and board member at Ashnik, where he built its India business from the ground-up and played an instrumental role in its expansion in Southeast Asia.

Mr. Sandeep Khuperkar brings 25 years plus of Industry experience, with 19 plus years in open source and building open source and Linux business models. He worked in various leadership capacities in IBM, RedHat to bring open source technologies to customers’ businesses by providing better solutions and even better services are his core areas of expertise.

Mr. Sandeep Khuperkar has been active in open source technology for over 19 years. Has a proven track record of building technology businesses across India and South East Asia. On the advisory board of Engineering Colleges. He is Regional Mentor Of Change for Atal Innovation Mission by NITI Aayog, an initiative by Govt. Of India. Accomplished speaker and contributor on open source solutions and consulting across India and South East Asia. He is a member of OSI and Linux Foundation and Community Moderator Alumni at opensource.com. Also being an active Isha Foundation Volunteer, he avidly helps create mindshare for holistic wellbeing, especially among students.

Mr. Sandeep Khuperkar earned his Bachelor of Engineering in Electronics from Shivaji University, Kolhapur, India.

Mr. Sandeep Khuperkar is Bestowed with the following Licences & Certifications:

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

Mr. Sandeep Khuperkar can be contacted at:

Mobile : +91 99875 36436 | E-mail | LinkedIn | Twitter

About  Data Science Wizards:

DSW is a visionary Data Science Consulting, AI Solutions & Services Company founded in 2019, head quartered in Mumbai, India, and having a presence in Bhopal, Dublin Ireland, and London.

We are a passionate team of individuals, collectively bringing extensive knowledge and experience in Enterprise Architecture, software development, data science, and open-source space with expertise across industry and technology solutions. By leveraging Artificial Intelligence, our data-driven products, platform solutions, and services enable enterprises and communities alike, making AI available for everyone.

Our goal is to empower customers with our ethical artificial intelligence-based services and solutions to help them achieve their business goals by harnessing their data. Our consultancy on data management and data engineering enables customers to make the best use of their data to boost business outcomes.

We would be glad to schedule an initial walkthrough. Please write us at contact@datasciencewizards.ai or call us at +91-9987536436

LinkedIn | X | FaceBook | Instragram

Also read Mr. Sandeep Khuperkar‘s earlier article :

The post AI in Insurance appeared first on Industry4o.com.

Business Case

The Indian Banking and Finance Industry must introduce reforms in the lending products & services for the retail customers. Introduction of artificial intelligence-based solutions that are developed with robust models to ascertain risk, predict customer behaviour and market fluctuations is the need of the hour.

Banks and Finance companies often provide their commercial and corporate clients with tailor made products & services to manage debts consolidation and restructuring. However, there are no tailor-made solution available for the retail customers to manage their debts and get financial relief. The development of a solution for debt restructuring and retail customers pay-off is the focus of the article.

Problem Statement

Financial mismanagement is a major risk for retail customers. Since the beginning of recorded history there are incidences when people have lost their wealth due to bad financial decisions and mismanagement of both liquid and immovable assets. Also, retail customers when faced with adversities like natural disaster, crop failure, job loss, significant loses to income source due to spread of epidemics, or because of treatment of critical diseases like cancer or kidney failure, lose a lot of money and become dependent on multiple loans.

According to the 2022 study by PwC and Equifax*, smaller ticket size loans such as consumers and personal loans, had the highest rates of Delinquency while Covid outbreak. The higher delinquency among the rural and semi-urban population was attributed to the reduction in household income during lockdowns. The greatest burden of Non-Performing Assets (NPAs) fell upon NBFCs who targeted retail sectors with unsecured personal loans.

Figure 1.1 Below RBI data also proves a correlation between adversity such as COVID and percentage of NPAs in the retail segment. Due to lock down and job losses at the end of 2018-19, Gross and NPAs were highest in the Financial Year (FY) 2018-19. However, a gradual decline in NPAs was observed with unlock and improvement in market & health conditions and increase in the employment opportunities.

Retail customers initially rely on a single lender but as the situation become more extreme, they become dependent upon multiple loans for survival and attempt to bail out of their financial difficulties. Any mismanagement of funds/misses in EMI payments on account of limited knowledge of financial products, lack of/incorrect financial advice, eventually pushes them in a debt trap. Soon it becomes impossible to keep track of multiple loans, finances and life becomes a hellish experience where one is constantly harassed by threatening phone calls and visits from recovery/collection that the lenders have appointed as well as legal notice sent by their advocates.

Figure 1.2 Below RBI data shows impact of numerous factors upon growth of NPAs/Loan Defaults.

If the financial situation does not improve, a few retail customers become very distressful and may take desperate measures- start criminal activities, commit suicide, send/sell their women folks to prostitution and more to fulfil financial requirements.

What do retail customers who have failed to manage their debts may be looking for?

Most customers suffering the above-mentioned problem statement are looking for relief from the threatening follow ups for payments through home visits and over phone calls. They want to consolidate their debts by closing all the smaller loans with one single loan which offers them flexibility in terms of release of funds, tenure of payments and interest rates. Possibly rewards or incentives and some financial advice.

Existing financial products that may help retail customers to manage debt issues.

A few solutions for debt managements that are offered to retail customers are listed below for illustrative purposes.

1. Loan Balance Transfer: Help retail customers consolidate their loans; financial organizations offer balance transfer loan products. Idea here is to close multiple smaller loans with single large one. However, this is upselling of personal loans. Such products often fall short of a retail clients need as they do not provide any financial advice on debt restructuring and pay off. Moreover, customer having poor credit bureau scores are not eligible.

2. Payday loans: Payday loans are short-term loans that are having tenures ranging from 7 days to 35 days. These have high processing fees and interest rates, sometimes up to 30%. These are mostly provided by NBFCs and non-regulated lenders. Payday loans require minimum paperwork and are disbursed quickly; hence can be attractive to few retail customers. However, smaller tenure and excessive cost of funds can push customers to debt traps.

3. Gold loans: Gold loans are one of the oldest lending instruments that are useful to people in urgent need of funds. However, inflexible payment schedule and risk of losing the collateral that hold family emotions and sentiments make them not the preferable option for some retail customers.

4. Debt free consultancy services: Few companies provide debt advisory services to both business and retail customers at premium charges. These businesses hire expert financial counsellors and in-house lawyers who may negotiate with creditors on behalf of their clients to manage their debt challenges. The only drawback is that they themselves do not offer any loan/funds, and their solution would mostly depend upon the mercy/discretion of the lenders.

Proposed Solution

A solution that will employ recommendations from an Artificial Intelligence Assisted to ascertain an individual’s financial health and accordingly suggest loan products and customized path for debt relief is the need of the hour. Such a solution can be integrated with the lenders Loan Origination Software to identify, onboard and service retails customers especially those, who are poor in managing finances. The proposed solution should have an intelligence/capability to identify retail customers who have the capacity to payback loans yet are unable to secure debt consolidation due to the lack of financial knowledge and limitations of existing lending products and services.

The regulator should lay down guidelines for private sector, public sector banks and NBFCs to work on tailor made solutions for retail debt consolidation and restructuring.

How will such a solution work?

Roadmap: Onboarding & Servicing of debt -ridden retail customers with Artificial Intelligence based Technology solution for Debt consolidation, restructuring and pay off.

Benefit and Incentives

Benefits to the lenders

1. Once this line of business picks up new business opportunities will come to Banks and NBFCs in form of new customer segments or previously unexplored/untapped customer segments. Ensuring steady profits. Retail customers are safer than business and corporate customers because individuals have fixed income and their monthly income and spends can be easily tracked for any risk basis the account statements of their salary accounts.

2. Some of lenders may argue the retail segment would provide them low return but also there is lower risk.

Benefits to the retail customers:

Future Direction

Conclusion

Implementation of a financial product, which has the capability to ascertain customers credit profile and predict a tailor-made debt restructure plan basis artificial intelligence has become the need of the hour.

About the author:

Mr. Sudhir Kumar Ghosh
Senior Business Analyst – IBM

Mr. Sudhir Kumar Ghosh is seasoned Business Analyst with 16 years of experience in helping Banks and Financial Institutions improve customer experience and engagement through the implementation of innovative digital journeys.

Mr. Sudhir Kumar Ghosh takes interests in conceptualizing technology based financial solutions that can be easily accessible to the less privileged section of the society.

Mr. Sudhir Kumar Ghosh can be contacted at :

LinkedIn

Ms. Swati Sharma
Business Analyst – IBM

Ms. Swati Sharma is an experienced Business Analyst specializing in the financial markets sector, with a focus on market intelligence, risk analysis, and financial product strategy. With over 11 years in the investment banking sector.

Ms. Swati Sharma‘s background spans business analysis, solution development, and securities services.

Ms. Swati Sharma is analysing and refining financial processes, products, services, and systems.

Ms. Swati Sharma can be contacted at:

LinkedIn

The post AI Assisted Debt Consolidation & Pay off Solution appeared first on Industry4o.com.

The oil & gas industry is undergoing transformative changes, driven by a combination of market dynamics, technological advancements, and environmental considerations. Here are some key trends currently shaping and expected to further influence the industry:

Digitalization and Data Analytics : This is the era of integration of digital technologies like IIoT( Industrial Internet of Things), artificial intelligence (AI), machine learning (ML), and advanced data analytics-the importance of these technologies is on the increase to enhance efficiency, safety, and productivity by enabling predictive maintenance, optimizing workflows, and improving decision-making processes.

Decarbonization and Clean Energy Transition : Every organization has signed up to NetZero goals resulting in a huge focus on reducing its carbon footprint. This includes investing in renewable energy projects, low-carbon hydrogen, fossil fuels etc. Major oil & gas companies are also exploring opportunities in biofuels, synthetic fuels, and direct air capture technologies.

Electrification and Energy Storage : Consumers are also becoming more conscious of the environment, rising fuel prices and sustainability-electrification is driving a shift towards electric vehicles (EVs), which in turn increases demand for cleaner electricity. Oil & gas companies are positioning themselves to meet this demand by investing in power generation, grid infrastructure, and EV charging facilities. Furthermore, they are involved in developing energy storage solutions to manage the intermittency associated with renewable energy sources.

Sustainability and Circular Economy : There is a growing emphasis on sustainable practices across the value chain. This includes responsible sourcing of raw materials, reducing flaring and methane emissions, and adopting circular economy principles in waste management. Some companies are also exploring biodegradable materials as alternatives to plastics used in drilling and production.

Regional Shifts and Geopolitical Dynamics : The geopolitics of oil & gas is evolving. Regions like the Middle East, traditionally dominant in the industry, are facing increasing competition from U.S. shale, Russian exports, and growing energy independence in some countries. Meanwhile, developing countries in Africa and South America are emerging as new oil & gas frontiers.

Looking ahead, several future trends are likely to further impact the industry:

Increased Automation : With advancements in robotics and automation, the industry anticipates greater use of autonomous machinery and drones for tasks ranging from exploration and production to maintenance and inspection.

Hydrogen Economy : As the world moves towards a low-carbon future, hydrogen is expected to play a pivotal role, particularly in hard-to-decarbonize sectors like heavy industry and long-distance transportation. Oil & gas companies, with their existing infrastructure and expertise in gas handling, are well-positioned to participate in this hydrogen economy.

Growing Focus on ESG : Environmental, Social, and Governance (ESG) criteria are becoming increasingly important for investors, consumers, and regulators. Companies that can demonstrate strong ESG performance are likely to enjoy better access to capital and enhanced reputational benefits.

In conclusion, the oil & gas industry is navigating a period of significant transformation. By embracing digitalization, advancing low-carbon solutions, and focusing on sustainability, the industry can not only adapt to the changing energy landscape but also contribute to a more sustainable future. The successful navigation of these trends will require strategic foresight, significant investment in new technologies, and a commitment to responsible business practices.

Let’s further understand how oil & gas companies are pivoting their strategies – alternate energy sources, responding to current trends and consumer requirements to remain essential.

The industry perspective :

The oil & gas industry, historically a cornerstone of global energy production, faces unprecedented challenges from electrification. The primary driver of this change is the increasing emphasis on reducing greenhouse gas emissions to combat climate change. Governments worldwide are implementing stricter regulations on carbon emissions and are promoting clean energy alternatives, thereby forcing oil & gas companies to adapt or risk becoming obsolete.

One of the key ways the oil & gas industry is responding to these challenges is through diversification. Many oil majors are investing in renewable energy projects, such as wind and solar farms, to expand their energy portfolios beyond fossil fuels. For instance, Shell has invested heavily in offshore wind power, while bp has ventured into biofuels and carbon capture technologies. Such diversification not only helps these companies meet the demands of a changing energy landscape but also positions them to capitalize on new market opportunities.

Another critical response from the oil & gas industry is the development of ‘low-carbon’ hydrogen. Unlike traditional methods of hydrogen production that rely on natural gas and produce carbon emissions, low-carbon hydrogen is produced using renewable energy sources or natural gas with carbon capture technology. It’s seen as a promising solution for hard-to-decarbonize sectors like heavy industry and long-distance transportation, where electrification might not be feasible.

Furthermore, oil & gas companies are leveraging their expertise in infrastructure and logistics to play a pivotal role in the electrification process. They are investing in charging infrastructure for electric vehicles (EVs), power grids, and battery storage solutions. For example, Equinor, the Norwegian oil company, has launched a subsidiary dedicated to developing EV charging infrastructure in Europe.

Despite these efforts, it’s essential to acknowledge that the transition won’t be smooth. The oil & gas industry’s future is uncertain, with potential job losses and economic disruptions in regions heavily reliant on fossil fuel extraction. Therefore, a just transition that supports workers and communities affected by this shift is crucial.

In this article, let’s delve deeper into the electrification trend, the pace at which it is moving, and the debate around pros and cons.

The future of the oil & gas industry lies in its ability to innovate and adapt to the electrification trend. While the industry’s core business may shrink, it can still contribute significantly to the global energy mix through low-carbon technologies and services. Ultimately, the success of this transformation will depend on how effectively oil & gas companies can navigate this period of profound change and how well they align with the broader goals of a sustainable, low-carbon future.

From an emissions standpoint, electrification holds significant promise for reducing carbon footprints. Electricity generated from renewables such as solar and wind is virtually emission-free at the point of use, unlike fossil fuels that emit CO2 during combustion. However, the overall emissions reduction depends largely on how that electricity is produced. If it’s sourced from coal, for instance, the emissions reduction would be less than if it were sourced from renewables. Therefore, a rapid and significant expansion of renewable energy capacity is critical to maximizing the climate benefits of electrification.

On the other hand, the production and disposal of lithium batteries used in electric vehicles (EVs) and energy storage systems present a different set of sustainability challenges. Lithium mining, predominantly done in arid regions, can lead to water scarcity issues and soil degradation. Moreover, the energy-intensive process of lithium extraction contributes to emissions.

Regarding battery disposal, while recycling technologies are improving, current recycling rates remain low, and the process itself involves energy use and potential environmental impacts. The improper disposal of spent batteries can also lead to environmental contamination due to hazardous materials like cobalt and nickel.

The oil & gas industry, with its expertise in handling large-scale infrastructure projects and managing complex supply chains, can play a vital role in addressing these sustainability challenges. Companies can invest in research and development to improve battery efficiency, extend battery life, and enhance recycling technologies. They can also participate in sustainable mining practices, ensuring minimal environmental impact during raw material extraction.

Moreover, the industry can leverage its experience in emissions management to optimize the lifecycle emissions of electricity generation. This could involve investing in cleaner energy sources, carbon capture and storage technologies, and promoting energy efficiency in the manufacturing and usage phases of lithium batteries.

In conclusion, while electrification presents a pathway to reduced greenhouse gas emissions, it is not without its sustainability challenges, especially concerning battery production and disposal. The oil & gas industry, with its vast resources and expertise, has a significant role to play in addressing these challenges. By investing in cleaner battery technologies, more sustainable mining practices, and optimizing the emissions profile of electricity generation, the industry can contribute to a more sustainable future. This transition, however, must be managed carefully to ensure that it results in net environmental benefits.

Consumer perspective: As an early adopter of electric cars, here is my perspective on the EV trend

Environmentally Friendly: One of the most significant advantages of EVs is their reduced environmental impact. Electric cars produce zero tailpipe emissions, contributing less to air pollution and climate change. Moreover, as the electricity grid becomes cleaner with increased renewable energy sources, the overall carbon footprint of EVs continues to decrease.

Lower Operating Costs: Electric vehicles are generally cheaper to run than conventional cars. Electricity is less expensive per mile than gasoline or diesel. Additionally, EVs have fewer moving parts, which means less wear and tear, resulting in lower maintenance costs.

Energy Efficient: Electric motors convert a higher percentage of the electrical energy from the grid to power at the wheels. This results in less energy loss compared to internal combustion engines, which makes EVs more energy-efficient.

Lower Noise Pollution: Electric vehicles operate much quieter than conventional vehicles, which can significantly reduce noise pollution, particularly in urban environments.

Reduced Dependence on Fossil Fuels: By driving an electric car, you’re lessening the demand for oil and contributing to energy security.

Government Incentives: Many governments worldwide offer financial incentives, such as tax credits, rebates, or exemptions from congestion charges, to encourage the adoption of electric vehicles. These incentives can offset the initial purchase price of an EV.

Advanced Technology: EVs often come with cutting-edge features such as regenerative braking, instant torque for quick acceleration, and smartphone app integration for vehicle control and monitoring.

Potential for Vehicle-to-Grid Services: Some advanced EVs can potentially feed electricity back into the grid when parked, providing services like peak load shaving or frequency regulation, which could generate additional revenue for owners.

While electric vehicles offer numerous benefits, it’s important to note that they also have limitations, including range anxiety, longer refueling times compared to conventional vehicles, and the environmental impact of battery production. However, ongoing advancements in battery technology are continually addressing these issues, making EVs an increasingly attractive choice for environmentally conscious consumers.

In conclusion, the benefits of electric vehicles – from their positive environmental impact and cost efficiency to their advanced features and potential grid services – make them an appealing alternative in the evolving landscape of personal mobility.

About the Author :

Ms. Anita Ganesh
Partner – IBM Consulting

As Partner of IBM Services, Ms. Anita Ganesh handles key client  portfolio – with responsibilities spanning delivery & team management from the India Client Innovation Center and Global Account Operations Leadership.

Fostering a culture of trust, collaboration and innovation are Ms. Anita Ganesh key focus areas, towards implementing the IBM Services strategic vision for clients-Cognitive Enterprises enabled by the Hybrid Cloud.

An IBM certified Senior Project Manager, Ms. Anita Ganesh have traversed across multiple roles in technical leadership, delivery management, portfolio management, delivery excellence & solutioning in the following organisations :

– L&T Infotech, Powai (1996-2004)

– Infosys (2004-2006)

– IBM India Pvt. Ltd. (2006-till date)

Ms. Anita Ganesh‘s key challenging roles performed:

1. Technical leadership of an end-to-end underwriting application for a large insurance company in UK

2. Leading the Managed Services Transformation of the Non-ERP portfolio for a large oil & gas company

3. Organisation transformation @ IBM – in the areas of Delivery excellence, Project management centre of excellence & transformation, Governance effectiveness, Process, Methods & Tools refinement & standardization for a business unit

Ms. Anita Ganesh is Bestowed with the following Licenses & Certifications :

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

Ms. Anita Ganesh can be contacted at :

LinkedIn 

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