Dell Technologies

Helping lead Dell Technology's Services organization to help clients identify, validate, value, and prioritize where and how they can apply AI and data to derive and drive new sources of customer, product, service, and operational value.  Focused on leading AI Value Engineering workshops to drive cross-organizational alignment and consensus around the organization's data-driven, value-centric journey.

Customer AI and Data Innovation Strategist

Dean of Big Data

I combine a proven design-centric value engineering methodology with hands-on data science experience – enhanced by direct research with leading universities – to help organizations define, develop and scale their ability to unleash the business value of their data and analytic investments.  I transition the business and IT debate away from technology outputs and competencies to delivering business and operational outcomes.
- I teach, mentor and provide hands-on guidance to senior IT and business leadership to drive collaboration between your business stakeholders and data, analytics and data science teams to identify (envision) where and how data and analytics can deliver measurable, relevant business and operational impact
- I train organizations using a pragmatic, ROI-driven data monetization methodology - complete with a detailed training methodology, design templates, key economic and analytic concepts, and hands-on exercises - to identify, validate, value and prioritize the key use cases that deliver business and operational outcomes optimized with data and analytics
- I leverage economic concepts and tools to provide guidance on how to build out the organization’s strategic data and analytics (AI / ML) capabilities and assets using an ROI-driven use case-by-use case implementation approach
- Ultimately, I drive organizational consensus and alignment around the business and operational use cases from which to monetize the organization’s data and analytic assets

Data Science and Data Monetization Strategic Advisor

Menlo College

Teaching "Big Data MBA", "Thinking Like a Data Scientist" and "Future-proofing Your Career with Data Science, Design Thinking and Economics"

Adjunct Professor

NUI Galway J.E. Cairnes School of Business & Economics

Honorary Professor at NUI Galway

University of San Francisco School of Management

I teach a course titled the "Big Data MBA."  The purpose of the course is to teach the next generation of business leaders to embrace analytics as a business discipline.

Completed our research paper "Determining the Economic Value of Data" in 2016.

Executive Fellow

Hitachi Vantara

Responsible for creating, executing and managing Hitachi Vantara's value engineering-cased co-creation methodology to build strategic relationships with Light House customers.  Also responsible for leading the data science team to build composable, reusable, continuously-learning and adapting analytic modules and assets.

Think #BigData + #DataScience + #DesignThinking + #DigitalEconomics drives #DigitalTransformation

Chief Innovation Officer

Responsible for identifying, developing and rolling out analytics, data warehousing and MDM service offerings.

•	Set strategy, identified target markets and developed corresponding solution frameworks
•	Developed, trained and executed Analytics service offerings and GTM execution plan
•	Keynote speaker and publisher supporting EMC’s Big Data Analytics initiative
•	Lead analytics client engagements around the execution of vision workshops and analytic labs

Chief Technology Officer, Dell EMC Global Services Big Data Practice

City of San Jose Innovation Advisory Board

DataRPM

I advise DataRPM on market and product strategies, validate marketing messages and collaborate in joint webinars to help the market understand the unique big data and data science capabilities of the DataRPM Cognitive Data Science Platform.

Board of Advisors

Financial Ideas

Financial Ideas, Inc. is an online microfinance marketplace targeting Credit Unions for low to moderate income affinity groups.  The marketplace focus enables affinity Credit Union members to qualify for financial resources facilitated by a new complement to FICO, entitled SoCap (Social Capital Credit Scoring).  The SoCap marketplace platform facilitates lead generation and matchmaking for providing financial products to members.  SoCap support for loan application processing complements FICO for application analysis and underwriting which strengthens loan portfolio quality while reducing delinquency rates.

Advisor

- I am a pragmatic hands-on leader. Experienced in establishing, nurturing and empowering Data Science and Value Engineering teams, I uncover and monetize the business value buried in the organization’s data.  
I am customer-facing. The Value Engineering methodology, which I created, drives customer collaboration (co-creation) and organizational alignment in identifying, validating, valuing and prioritizing the organization’s most important business use cases where data and analytics can have material impact.

- I am value focused. Called the “Dean of Big Data,” I bring a business-first approach to helping organizations leverage advanced analytics and data science to uncover the customer, product and operational insights buried in the organization’s data that power digital transformation.

- I drive data monetization. Uniquely integrated Design Thinking with Data Science I employ a “Rapid exploration, rapid testing, failure-empowering, continuously-learning” data science development methodology that enables the monetization of the insights buried in the data.

- I am an influencer and innovator. Recognized as an industry leader in Big Data, Data Science, Design Thinking and Data Monetization, I integrate these disciplines to drive organizational empowerment and digital transformation.

-I am a Professor and Educator. I developed and teach the “Big Data MBA” – a course for integrating data and analytics into the operations of the business – at University of San Francisco School of Management and National University of Ireland-Galway School of Business & Economics. I also lecture to numerous universities and organizations worldwide.

-I am an author. I have written 4 books “Big Data: Understanding How Data Powers Big Business,” “Big Data MBA: Driving Business Strategies with Data Science” and the forthcoming “Economics of Data, Analytics and Digital Transformation;” and published over 350 industry-leading articles and educational videos on the application of Big Data, Data Science, AI / ML and IoT to drive data monetization and digital transformation.

Jump directly to my blogs at: https://www.linkedin.com/in/schmarzo/detail/recent-activity/posts/ 


🔑 AI & Data Literacy: Empowering Everyone to Thrive in an AI-Driven World! 🚀

The world is buzzing about Generative AI—OpenAI’s ChatGPT, Microsoft Copilot, and Google Gemini are transforming our work. But here’s the reality: AI is only as valuable as the thinking framework behind it. These GenAI tools lack a value-centric framework to guide the user's exploration and engagement process and ensure the generation of relevant, meaningful, and responsible outcomes.

To help business leaders, students, and the average layperson become “Citizens of Data Science” and get more value from AI, I’m giving away my book "The Art of Thinking Like a Data Scientist (2nd Edition)" – for FREE! 🎁 Yep, you can download it directly from this post and then upload it into your GenAI tool. You immediately have a GenAI framework to guide your exploration and unleash your curiosity to yield more relevant and accurate outcomes.

The "Thinking Like a Data Scientist" methodology has been honed across hundreds of workshops and classes and combines three valuable disciplines:

✅ Data Science (AI/ML) – Understand how to structure problems and extract value from data
✅ Design Thinking – Empathize with stakeholders and constituents to design AI-powered solutions that matter
✅ Data Economics – Monetize data assets and drive measurable business impact

Why am I giving it away?

Data and AI literacy are no longer optional. The more we empower people to leverage GenAI and think like data scientists, the better AI-driven decisions we’ll make – in business, healthcare, education, society, and more.

Here is my ask:
💡 Drop a comment: What’s your biggest challenge in leveraging AI for business decisions?
🔁 Tag a friend who needs to sharpen their AI & data skills!
❤️ Repost this to help others get smarter with AI!

Together, let’s rethink how we use AI—not just as a tool but as a force multiplier that unleashes our curiosity to power innovation.

#DataScience #AI #GenerativeAI #MachineLearning #ChatGPT #Copilot #DataLiteracy #AIforBusiness #Schmarzo #TLADS #BetterTogether

Bill Schmarzo

📣 Keynote Speaker and Corporate AI Educator || Dean of Big Data || 📚 Author 7 Books || Recognized global innovator, educator, and practitioner in Big Data, Data Science, AI, & Design Thinking

The Art of Thinking Like a Data Scientist by Bill Schmarzo

📘 The Cognitive Workout: Using AI to Build Mental Strength in the Age of Generative Intelligence

This is the first time I've done something like this - to create a short yet hopefully engaging eBook around a specific topic that I can share with my university and corporate students.

With all the recent articles and research about the mental atrophy concerns from GenAI usage, I thought that we needed a practical guide to turn GenAI into a training partner that expands our thinking rather than shrinking it.
- Six Cognitive Workstations.
- A Socratic YODA prompt.
- A structured process for pushing your mind two or three standard deviations beyond the mean.

This project would not have come to life without the invaluable insights from my friend and colleague Renée B. Lahti, who once again helped sharpen my thinking and refine the flow.

If you’ve ever wondered how to think with AI instead of through it, this guide is for you. I hope it serves you, your students, your teams, and your own intellectual journey.

Let me know if this resonates, and if so, please share with others.

Who knows, I just might do some more of these if people find this helpful.

Cheers! Bill

#DataStrategist #DataScience #AI #AI #DataManagement #DataEconomics #AILiteracy #AIHumanEdge #DeanofBigData #AI4IA Iowa State University - Ivy College of Business Coe College Menlo College Mouwafac Sidaoui University of Galway University of San Francisco School of Management Denis Dennehy

The Generative AI Cognitive Workout

Wow, TechInformed!  Thanks for the vote of confidence.

Yes, teaching (and learning to teach) is my passion. I hope to continue earning your respect as I advance my teaching regarding how to leverage big data and AI to create new sources of customer, operational, and societal value.

#AIForGood #AI4IA #DeanofBigData Iowa State University - Ivy College of Business Coe College

Bill Schmarzo is known as the “Dean of Big Data.”

No wonder he’s on our Top 10 Most Influential Tech Leaders this year.

If you’ve ever heard Bill speak or read his work, you’ll know this: he makes data feel useful and human. 

He’s helped leaders across the world understand how to actually use data. His Big Data MBA has changed the way companies think about value, decision-making and problem-solving. Through his teaching in the US and Ireland, he’s shaped how a whole generation of executives approach data today.

And that’s why he stands out.

Want to meet the others who made the list too?
Take a look → https://lnkd.in/eWPauDPw

Very powerful. A must read.

Science as METHOD:

- Ask questions
- Demand evidence
- Test claims
- Accept uncertainty
- Change minds when wrong

Very meaningful and appropriate advice in the age of AI. 

Thanks, Dimitrios A. Karras , for the share.

Assoc. Professor at National & Kapodistrian University of Athens (NKUA), School of Science, General Dept, Evripos Complex, adjunct prof. at EPOKA univ. Computer Engr. Dept., adjunct lecturer at GLA & Marwadi univ, India

Carl Sagan predicted 2025 in 1995.
Manufacturing industries gone.
Technology controlled by a few billionaires.
People unable to question authority.
Science replaced by superstition.
He died one year later, in 1996.
He never saw if his predictions came true.
They did. All of them.
1995. Carl Sagan is 61 years old, dying of myelodysplasia (a bone marrow disease).
He knows he doesn't have much time.
He's spent his life popularizing science—making astronomy, physics, and biology accessible to millions through his TV series Cosmos (1980) and books like Pale Blue Dot (1994).
But he's terrified.
Not of death. Of what he sees happening to America and the world.
So he writes The Demon-Haunted World: Science as a Candle in the Dark (1995).
It's part science book, part warning, part desperate plea.
In Chapter 2, he writes a passage like prophecy:

"I have a foreboding of an America in my children's or grandchildren's time—
when the United States is a service and information economy;
when nearly all the manufacturing industries have slipped away to other countries;
when awesome technological powers are in the hands of a very few,
and no one representing the public interest can even grasp the issues;
when the people have lost the ability to set their own agendas or knowledgeably question those in authority;
when, clutching our crystals and nervously consulting our horoscopes,
our critical faculties in decline,
unable to distinguish between what feels good and what's true,
we slide, almost without noticing, back into superstition and darkness..."

The book is published in March 1995.
Carl Sagan dies December 20, 1996.
He never saw smartphones, social media. Never saw Trump. Never saw QAnon. Never saw COVID conspiracy theories. But he predicted all of it.
Let's examine one of his predictions:

"A service and information economy"
1995: The U.S. still had significant manufacturing—automotive, steel, textiles.
2025: Manufacturing is 8% of U.S. GDP (down from 25% in the 1950s). Most physical goods are made in China, Vietnam, Mexico. The U.S. economy is dominated by:

Tech companies (Google, Apple, Meta, Amazon)
Financial services (banking, insurance, private equity)
Healthcare services
Fast food and retail

Verdict: ✅ ACCURATE

Etc. 

Carl Sagan died 28 years ago.
His warning is 30 years old.
And America chose not to listen.
Chose superstition over science.
Chose feelings over facts.
Chose billionaires over democracy.
Chose echo chambers over truth.
Exactly as he predicted.

He was right about everything.
So what do we do?
Sagan's answer: Science as a candle in the dark.
Not science as dogma.
Science as METHOD:

Ask questions
Demand evidence
Test claims
Accept uncertainty
Change minds when wrong

That's the candle.
It's still lit.
If we choose to see it.

And it's more relevant today than when he wrote it.
That's either prophecy.
Or tragedy.
Or both.
Read The Demon-Haunted World.
It's not too late.
The candle is still burning.
We just have to stop blowing it out.

This post and graphic from Jay Latta is a valuable and relevant way to think about the entire AI ecosystem and the importance of understanding, positioning, and leveraging the right AI capabilities for the right AI problem.

Here is an example of an exercise we do in my class to help students look beyond the current GenAI/LLM/Agentic AI hype and understand how these different AI capabilities can leverage one another to solve some nasty customer, operational, and societal problems.

"Synergy of Generative, Analytical, Causal, and Autonomous AI"
https://lnkd.in/gErjuuuV

LLM tunnel vision

We’ve fallen into the same trap the climate debate did:
Carbon Tunnel Vision became LLM Tunnel Vision.

“AI” today has been collapsed into one narrow branch - large language models - as if intelligence begins and ends with predicting the next token.

Surprise: AI ≠ LLMs

We’re mistaking fluency for understanding and scaling for progress.

Let’s be honest:
LLMs are extraordinary engineering achievements.
But they’re also statistical mirrors - reflecting the patterns of human text, not the structure of human thought.

And yet, the entire ecosystem is spinning around them:
🔹 Funding, research, and policy gravitate toward language models.
🔹 “AI strategy” now means GPU counts and prompt tricks.
🔹 Every startup pitch deck starts with “we fine-tuned an LLM.”

Meanwhile, whole fields that built AI’s foundation are fading into the background:
🔹 Symbolic AI - reasoning, logic, knowledge representation.
🔹 Causality - knowing why, not just what follows.
🔹 Reinforcement learning & control - how agents act, learn, adapt.
🔹 Robotics & embodied cognition - grounding intelligence in the real world.
🔹 Cognitive architectures - memory, planning, attention, self-models.
🔹 Ethics & interpretability - understanding what we’re actually creating.

The irony?
The AI community spent decades wrestling with these challenges - the very ones LLMs cannot solve alone.
And now, we act like “AI” was born in 2022.

This tunnel vision has consequences:
🔸 Research diversity collapses.
🔸 Public understanding gets distorted.
🔸 Policy is written for one architecture, not the ecosystem.
🔸 And the myth grows that scaling text prediction = intelligence.

We’re not witnessing “the dawn of AI.”
We’re witnessing a narrowing of it - a regression into linguistic myopia dressed as progress.

So stop talking about “AI” as if it’s one thing - ChatGPT, Claude, DALL·E.
That’s just one slice of it.

Yes, LLMs are impressive. But they’re not “AI.”
They’re one branch of a massive, decades-old tree.

Right now, the world is stuck in LLM Tunnel Vision -
acting as if text generation is intelligence,
and scaling GPUs is innovation.

Meanwhile, the rest of AI - robotics, reasoning, causality, cognitive architectures, planning - is starving for attention and funding.

If we keep calling LLMs “AI,” we’ll end up building systems that can talk about intelligence without ever having it.

The next wave of breakthroughs won’t come from scaling models.
It’ll come from reconnecting with the rest of AI - the forgotten disciplines that make intelligence more than autocomplete.

If we want true artificial intelligence,
we need to get out of the tunnel.
Time to zoom out.
AI is much more than language.

Something that I am working on for my Iowa State University - Ivy College of Business class.

John Thompson and Mark Stouse - this graphic is for you!!

✨ Honored and Grateful ✨

I’m deeply honored to be named one of TechInformed’s Top 10 Most Influential Tech Leaders of 2025.

But the real credit goes to my students and clients — the thinkers, dreamers, and doers who challenge me every day to see this AI and Big Data revolution through fresh and curious eyes.

They remind me that leadership isn’t about having all the answers — it’s about being fearless to ask the tough questions, collaborating to learn together, and building the confidence to not just adapt to the age of intelligence, but to shape it.

Your curiosity keeps me sharp.
Your courage keeps me inspired.
And your trust reminds me why I teach.

#AI #Leadership #DataScience #Innovation #Education #AIHumanEdge #LifelongLearning #AI4IA Iowa State University - Ivy College of Business Kevin Scheibe Coe College David T. Hayes Mouwafac Sidaoui Denis Dennehy

🥁 Drumroll please… 

Introducing the Top 10 Most Influential Tech Leaders of 2025!

Over the past few weeks, we asked you to nominate the people truly shaping the future of tech.

And you delivered.

We went through every submission, read every story, and listened to the reasons behind each nomination.

Today, we’re proud to announce the Top 10 Most Influential Tech Leaders who:

- Made a real impact.
- Inspired others to think bigger and do better.
- Led bold initiatives that are changing industries. 

Who are the changemakers redefining success for 2025? 

The results are in - see if your nomination made it → https://lnkd.in/eWPauDPw 

Julia Boorstin 
Alex Kantrowitz 
Jake Ward 
Bill Schmarzo 
Tamara McCleary 
Martin Harbech 
Kevin Barenblat 
Jonathan Horn 
Adel Al-Saleh 
Swayam Chouksey

Top 10 Most Influential Tech Leaders 2025

Determining Causality: Advancing from Correlation to Understanding

I probably spent over 20 hours crafting a 45-minute lecture on how organizations can design and apply causally driven analytics—so it felt wrong to let this one air only once a semester. Many of you have asked when I’d share more online lectures, so here you go!

Causal AI represents the next frontier in analytics maturity. It moves us beyond pattern recognition to understanding why outcomes happen—and how to change them. By identifying the causal factors driving specific entity outcomes (whether human or machine), organizations can use simulation and what-if analysis to design interventions that yield more meaningful, transparent, and trusted results.

This video complements Chapter 5 of my upcoming book, The AI-Human Edge, which explores how Entity Propensity Models (EPMs) and Entity Language Models (ELMs)—powered by Predictive, Causal, Generative, and Autonomous AI—work together to amplify human capability and unleash creativity across every domain.

If you’ve ever wondered how to turn AI from a tool that predicts into a system that understands, learns, and guides, this is the perfect place to start.

#DataStrategist #DataScience #AI #AI #DataManagement #DataEconomics #AILiteracy #AIHumanEdge #DeanofBigData #AI4IA

Open video: The Power of Causal AI: Determining Causality Lecture-14 mins on youtube.com

The Power of Causal AI: Determining Causality Lecture-14 mins

His alternative: world models. Systems that learn from visual data and build **causal understanding**, like a child learning physics by dropping things thousands of times.

Mark Stouse and John Thompson have been championing the essential importance of Causal Understanding for years as well.  They have also turned me into a believer (even dedicated a chapter in my forthcoming book - and several blogs in support of that chapter - The AI-Human Edge).

If you can't quantify cause-and-effect, then you can't do the counterfactual / what-if scenario exploration that is MANDATORY for building AI models that deliver more relevant, trustworthy, meaningful, responsible, and ethical outcomes.

Founder & CEO, Curiouser.AI | Berkeley Instructor | Building Values-Based, Human-Centered AI | LinkedIn Top Voice in AI

Yann LeCun just left Meta.
Let that sink in.

One of three people who invented modern deep learning. Turing Award winner. 

Chief AI Scientist at a company spending billions on AI.

Gone.

Not to a competitor. Not to retirement. 
To a startup building technology 
Meta apparently doesn't prioritize.

This matters more than most people realize

LeCun has spent 40 years being early and right. 

In the 1980s, he couldn't find a PhD adviser because machine learning was "a dead end." 

Three decades later, that dead end became the foundation of modern AI.

Now he's saying large language models, the technology behind ChatGPT, Gemini, and Meta's Llama, are another dead end.

"Nobody in their right mind would use LLMs of the type that we have today" within 3-5 years, he said at MIT last month.

His alternative: world models. Systems that learn from visual data and build causal understanding, like a child learning physics by dropping things thousands of times.

Current LLMs? 
They predict the next word. 
They don't understand gravity. 
They don't know that objects exist when you're not looking at them.

LeCun compares their intelligence to a cat's. He thinks the cat is smarter.

Here's what troubles me:

Meta is spending countless billions on technology that its own Chief AI Scientist publicly called a dead end. 

Either LeCun is wrong (possible, but his track record suggests otherwise), or Meta is chasing market narratives over technical reality.

His departure suggests he's betting on the latter.

The implications for current AI investments are significant. If LeCun's 3-5 year timeline holds, companies built entirely on LLM scaling may face architectural obsolescence.

That's not a guarantee. Even geniuses miss sometimes.

But LeCun has never been wrong about something this big before
********************************************************************************
The trick with technology is to avoid spreading darkness at the speed of light.
Stephen Klein
Founder & CEO, Curiouser.AI
Instructor, UC Berkeley Haas School of Business
WeFunder (link in comments)

Sources:

Wall Street Journal - LeCun departure and startup plans (November 2024)
MIT Symposium - LeCun remarks on LLM limitations (October 2024)
Meta FAIR Research Publications - World model architectures
Turing Award Archives - LeCun, Bengio, Hinton recognition (2018)

My ability to deepen my teaching experiences is not only fulfilling from a sharing and giving back perspective, but I am also always being challenged to learn new approaches and to explain and apply traditional approaches in ways that are more relevant with the advent of AI in training.

For example, we had an excellent conversation last week on determining causation, for which I did not have a sufficient answer.  So I've spent the better part of the previous few days consulting with others, and am preparing to not only address that question, but also to create some design templates that can be used by both the domain experts (to create Structured Causal Models) and the data science team (to code Directed Acyclic Graphs) necessary to determine causation.

There will be a detailed blog post following that blends into Chapter 5 of my new book, "The AI-Human Edge" (the pre-release of that book can be found at www.DeanofBigData.com).

#DataStrategist #DataScience #AI #AI #DataManagement #DataEconomics #AILiteracy #AIHumanEdge #DeanofBigData #AI4IA

Open article: Dean of Big Data by deanofbigdata.com

This is how we ended my Iowa State University AI-driven Innovation class last night.  The last slide of the semester.

Every major leap forward—every invention, every movement, every reinvention—began with a group of people who chose to think differently.

Awareness to see clearly.
Acceptance to confront reality.
Curiosity to ask bigger questions.
Commitment to act together.
Belief that tomorrow can be better than today.

This is the Innovation Mindset.

And when we embrace it—not as individuals, but together—there is absolutely nothing we can’t solve.

We are neither blue nor red.  We're not liberal or conservative.  We are humans. 

And when we bring our strengths together—collaboratively, respectfully, creatively—we can solve any problem and build a better world for all of us.

#BetterTogether #WeThePeople #Respect #AI4IA

🔥 More parameters. More GPUs. More synthetic data… Oh my!!

And yet, somehow less actual value. Yes — GenAI is running head-first into the Law of Diminishing Returns. And leaders across industries are feeling it:

- Productivity gains that never materialized
- “Smart” copilots that hallucinate at the worst moments
- Growing costs with shrinking marginal improvements
- Trust evaporates when the system can’t explain why

The problem isn’t GenAI. It’s the approach.

Instead of trying to scale our way to value, how about focusing AI on amplifying the decision effectiveness and creativity of the domain experts who drive real-world outcomes?

In my new blog, I explain why LLMs alone can’t deliver that understanding — and how combining GenAI with Entity Propensity Models (EPMs), Entity Language Models (ELMs), and causal reasoning unlocks the next frontier:

✅ AI systems that learn with your people
✅ Transparent, intervention-ready intelligence
✅ Counterfactual insight (“What would happen if we…?”)
✅ Personalized, entity-level decision support
✅ Domain expert amplification — not automation theater

If you want AI that actually moves the needle — not just a bigger model with a bigger bill — this is your roadmap.

👉 The AI-Human Edge Journey Map: How to Salvage and Amplify Your GenAI Investments

#DataStrategist #DataScience #AI #AI #DataManagement #DataEconomics #AILiteracy #AIHumanEdge #DeanofBigData #AI4IA

Open article: The AI-Human Edge Executive Journey Map by open.substack.com

🦸‍♀️ Captain Causal to the Rescue 🦸‍♀️

🧠💥 We’ve all been fooled by it — the invisible monster that hijacks our logic, fuels bad bets, and whispers, “You’re right.” It’s called the Decision Trap Monster, and it’s been running our world for far too long.

But a new kind of hero has arrived: Captain Causal — armed with Causal AI, counterfactual reasoning, and a shield forged from why.

She doesn’t predict the future — she explains it.
She's here to help us see through the fog of bias, rebuild trust in our decisions, and remind us that clarity is the ultimate superpower.

🦸‍♂️ In a world drowning in data and deception, truth now wears a cape — and her name is Captain Causal.

#DataStrategist #DataScience #AI #CausalAI #DataManagement #DataEconomics #AILiteracy #AIHumanEdge #DeanofBigData #AI4IA

Open article: Captain Causal and the War Against the Decision Trap Monster by open.substack.com

Captain Causal and the War Against the Decision Trap Monster

I originally wrote this blog in 2015 while working for EMC. I couldn't find the link to the original post, so I thought I’d republish it - typos and all. Sort of fun, given all the recent conversations about the dangers of correlation-based decision-making. Man, a lot has matured about Causal AI since I wrote this!

Open article: Thinking Like A Data Scientist: Attitudinal Approach by open.substack.com

Thinking Like A Data Scientist: Attitudinal Approach

In my final Iowa State University - Ivy College of Business lecture for my AI-driven Innovation class, I break down one of the most misunderstood truths about innovation: conflict isn’t a problem to eliminate—it’s the fuel that makes innovation possible. Different stakeholders see the world through different mental models and rationales. Finance focuses on costs, operations on efficiency, marketing on customers, legislators on public pressure, and citizens on clean, safe water. Instead of smoothing over these tensions, design thinking teaches us to harness them. Through frameworks like the Double Diamond, we channel diverse perspectives, uncover hidden assumptions, surface unstated needs, and transform disagreement into synergy — not compromise.

The lecture shows how innovation becomes powerful only when grounded in a shared definition of value. Using data economics, Jobs-to-be-Done template, Thinking Like a Data Scientist methodology, and the Causal AI Spine, I walk through how organizations define what “good” looks like, architect decisions and KPIs that matter, measure cause-and-effect, and ultimately deliver solutions that are ethical, measurable, transparent, and evidence-driven. 

The Iowa water-quality example demonstrates this process in action—mapping stakeholder needs, clustering tensions, exploring solutions, and aligning on metrics that matter through collaborative tools such as Entity Propensity Models (EPMs), Entity Language Models (ELMs), and YODA-empowered, learning-based dashboards.

The lecture closes by defining the nine tenets of Empowered Innovation: critical thinking, curiosity, ethics, universal AI literacy, organizational improv, and the courage to confront instead of avoid conflict. Above all, I emphasize empowerment—empowering individuals to ask better questions, challenge assumptions, rethink value, and participate as active “Citizens of Data Science.” 

In a world increasingly shaped by AI, innovation is no longer about technology alone—it’s about people, purpose, and shared progress.

#DataStrategist #DataScience #AI #AI #DataManagement #DataEconomics #AILiteracy #AIHumanEdge #DeanofBigData #AI4IA

Open video: Empowered Innovation - Turning Conflict into Innovation lecture - 20 minutes on youtube.com

Empowered Innovation - Turning Conflict into Innovation lecture - 20 minutes

We’re Missing the Real Promise of Generative AI.

Too many organizations are rushing to use GenAI tools as cost-cutting weapons—automating tasks, reducing headcount, and hollowing out the very expertise that made them great.

But the true power of these tools isn’t in replacing human domain knowledge.
It’s in amplifying it — accelerating decision-making, improving customer engagement, optimizing operations, and uncovering entirely new sources of business value.

Every time a company loses a seasoned expert, it loses more than a person.
It loses thousands of hours of intuition, learned patterns, and contextual understanding — the kind of knowledge that rarely lives in SOPs or project documents, but inside human experience.

Generative AI, when used wisely, doesn’t erase that knowledge — it helps convert it into reusable, scalable intelligence.

That’s the foundation of The AI-Human Edge: building systems where AI learns from humans, and humans learn through AI — creating an organization that gets smarter, faster, and more valuable with every interaction.

It’s not about cost reduction. It’s about value amplification — for your customers, your employees, and your future.

You can find a pre-release version of my upcoming book, "The AI-Human Edge," at https://deanofbigdata.com/.

#AIHumanEdge #GenerativeAI #Leadership #HumanIntelligence #AIEthics #DigitalTransformation

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Bill Schmarzo's LinkedIn Performance

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Bill Schmarzo's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

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Bill Schmarzo's Linkedin Analytics

Performance ScoreBill Schmarzo's LinkedIn Performance

Bill Schmarzo's Best Posts (last 30 days)