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

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)

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

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

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