Data Monetization Secrets Data Leaders Must Know

MindSpeaking Podcast Episode 33 - Douglas Laney

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Highlights:

00:01 – Intro: Doug Laney’s data impact

00:52 – What you'll learn in this episode

07:16 – Innovation mindset for data professionals

08:00 – 9/11 and the idea behind Infonomics

09:31 – Treating data as a true business asset

11:20 – 12 ways to monetize data

12:42 – How to prioritize data ideas

14:40 – Creativity over dashboards

15:44 – Use cases as innovation drivers

16:54 – Doug’s “Digital Twin” chatbot

17:40 – What "Data Juice" really means

19:30 – 3 approaches to valuing data

23:01 – Walmart’s $1B search engine fix

25:18 – Leading indicators in data strategy

26:20 – Bridging the data-business gap

28:15 – Building trust through metadata and storytelling

29:30 – Rapid fire: mistakes, myths, and habits

31:12 – Aligning analytics with business goals

34:12 – Humor’s role in storytelling

40:40 – Final takeaway: treat data like an asset

Summary

In this conversation, 🎙️ Gilbert Eijkelenboom and 🗣 Douglas Laney explore data storytelling techniques, effective stakeholder communication, and practical ways to treat data as an asset. They discuss building trust with stakeholders, gaining stakeholder buy-in, how to move beyond hindsight dashboards toward business-focused data insights, and presenting data to stakeholders in a way that actually drives decisions. Along the way: early entrepreneurial lessons, Infonomics, Data Juice, use-case ideation, valuation methods (cost/market/income), confidence-building for analysts, managing stakeholder expectations, and tactics for connecting with non-technical audiences while avoiding excessive detail and technical language.

“Why storytelling matters in business.”

 “Gaining stakeholder buy-in through data insights.”

Data Storytelling Techniques for Business

🎙️ Gilbert Eijkelenboom

Doug, welcome to the show.

🗣 Douglas Laney

Thanks, Gilbert—great to be here.

🎙️ Gilbert

You’ve had a long, successful career in data and analytics. And now you’ve moved to Portugal to start a new chapter—I might join you someday. Thoughts?

🗣 Douglas

Are you calling me old? 😄 You’re very welcome—plenty of room here.

🎙️ Gilbert

Benvindo! I’m curious what inspired the move and how your first days have been. But first—let’s rewind. On this podcast we dig into the person behind the role. Who were you in high school?

🗣 Douglas

I straddled worlds: I played tennis (we won state) and I programmed. My dad brought home one of the first neighborhood computers when I was 13, so I coded my math and trig assignments—great training for both programming and quantitative thinking.I also did Junior Achievement with Walgreens as our sponsor. Each semester we formed a company, elected officers, sold shares, built and sold a product, then liquidated. We did this eight times.In our final run, I was president. We sold travel kits at shopping malls in the 1980s. Teenagers didn’t care—until our VP Marketing suggested adding something they would want: condoms. Sales exploded; we won Company of the Year in the Midwest. A nearby screenwriter heard about it and allegedly riffed on the idea for Risky Business. So… Tom Cruise basically played me. 😉

“Using stories to drive data impact.”

🎙️ Gilbert

Amazing. Early on you blended programming, business, and a real feel for end-users—understanding stakeholder needs long before you used that phrase.

Engaging Non-Technical Audiences with Data

🗣 Douglas

At university I studied math and computer science—until day one of differential equations, when a professor filled multiple chalkboards and then revealed it was the proof to the four-color map problem. Impressive—and intimidating.I leaned toward the business of computing. There was no MIS program, so I proposed my own. The committee rejected it: “Why would anyone use computers for business?” (mid-1980s!). Eventually the program passed, and I graduated in software engineering and business administration.

🎙️ Gilbert

Your travel-kit pivot shows how avoiding excessive detail and technical language and focusing on the end-user drives adoption—central to presenting data to stakeholders.

🗣 Douglas

Exactly. What separates great data leaders is innovation—not just more “pretty pie charts,” but new ways to activate the business with data. That’s real business-focused data insight.

Building Trust and Managing Stakeholder Expectations

🎙️ Gilbert

Let’s talk Infonomics. Where did the book come from?

🗣 Douglas

After 9/11, some Gartner clients lost not only people but data—this was pre-cloud. They asked us to value the lost data for insurance claims. Insurers rejected them: “Data isn’t property.” That stuck with me.I pursued the idea: if data is an asset, we should measure, manage, and monetize it like one. Infonomics lays out how to apply asset-management discipline—just as we do for physical, financial, and human capital.

🎙️ Gilbert

How can leaders practically treat data as an asset?

🗣 Douglas

Stop giving lip service. Borrow from ISO-style asset management: inventory, quality assessment, multi-source supply chains, lifecycle controls. We must value data and govern it with rigor—building trust with stakeholders by being explicit about lineage, quality, and usage.

“Translating data to meet end-user needs.”

From Dashboards to Decisions: Monetization & Value

🎙️ Gilbert

How do leaders find monetization opportunities?

🗣 Douglas

Start with creativity. We guide teams through hypothesis-generation, mapping users/buyers, packaging data, combining it with functionality, and defining measurable economic outcomes—not just reports.We might generate 60–100 ideas. Then we score them on economic, operational, ethical, and legal feasibility—prioritizing low-complexity/high-impact first. That’s how you gain stakeholder buy-in.

🎙️ Gilbert

Organizations often default to dashboards. How can they shift to innovation?

🗣 Douglas

Think from the consumer’s point of view and articulate a clear value proposition. Measure the value of data (methods are in Infonomics). And use use cases. I collected 1,000+ real-world cases and trained a chatbot on them—my colleagues call it “Digital Doug.” My second book, Data Juice, shares 101 stories on squeezing value from data—perfect for confidence-building for analysts and gaining stakeholder buy-in.

"Avoiding excessive detail and technical language.”

Valuation 101: Cost, Market, Income

🎙️ Gilbert

Measuring impact is hard. How do you do it?

🗣 Douglas

Use the three standard approaches:

1. Cost — What did it cost to acquire, collect, store, secure, govern—or to replace if lost?

2. Market — What would others pay? Unlike oil, data is non-depleting, non-rivalrous, and progenitive. You can license it repeatedly, creating additive value.

3. Income — What portion of an income stream is attributable to the data? Allocate across contributing resources.We track denominator (cost basis) and numerator (allocated value generated over time). If you don’t measure, you can’t manage.

   
   
   
   
   
   
   
   

Case Stories: Retail & Conservation

🗣 Douglas

Walmart noticed high cart abandonment for the search term “house.” Their engine routed to housewares, doghouses, dollhouses—but people wanted the TV show House. They upgraded search to factor trending signals (social, media), cutting abandonment by 10–15%—that’s massive incremental revenue.Another case: rhino conservation. Instead of tracking rhinos directly, sensors tracked leading indicators—the smaller animals rhinos follow to water. Anticipating rhino movement helped deter poachers.

🎙️ Gilbert

Great proof that data storytelling techniques and using stories to drive data impact work across commercial and non-profit contexts.

Bridging the Data–Business Gap

🎙️ Gilbert

Many multinationals see a gap between data teams and the business. Do you?

🗣 Douglas

Yes—even with CDO roles. Hiring a CDO who can “code Python” misses the point. You need a leader who understands the business, change management, and how to manage stakeholder expectations so people adopt analytics and build trust.

🎙️ Gilbert

How do we build that trust?

🗣 Douglas

Metadata is foundational—lineage, provenance, quality. Make it visible in catalogs for self-service analytics. And share internal use cases so success goes viral. This is central to presenting data to stakeholders and connecting with non-technical audiences.

“Connecting with non-technical audiences effectively.”

Rapid Fire: Practical Takeaways

🎙️ Gilbert

Biggest mistake companies make with data?

🗣 Douglas

Not valuing it—no cost basis, no revenue attribution.

🎙️ Gilbert

One skill data leaders need?

🗣 Douglas

Change management—help people accept data as part of their job.

🎙️ Gilbert

First action to bridge data and business?

🗣 Douglas

Stories. Share use cases to inspire—or shame—action.

🎙️ Gilbert

Biggest myth about monetization?

🗣 Douglas

That you must sell data to monetize it. There are ~a dozen patterns; selling is only one.

🎙️ Gilbert

How to ensure analytics drive decisions leaders care about?

🗣 Douglas

Deprioritize hindsight-only dashboards. Prioritize diagnostic, predictive, prescriptive, and automation use cases.

🎙️ Gilbert

Habit of top data leaders?

🗣 Douglas

They treat data as an asset—with measurement, governance, and monetization discipline.

Human Side: Humor, Confidence, and Learning

🎙️ Gilbert

What surprises people about you?

🗣 Douglas

I did stand-up comedy. My consulting manager once said it was “unbecoming.” Ironically, comedy made me better at storytelling and confidence-building for analysts. If you can handle hecklers, you can handle any boardroom.

🎙️ Gilbert

Role of humor in business?

🗣 Douglas

It anchors memory and attention—without taking ourselves too seriously.

🎙️ Gilbert

What are you most looking forward to in Portugal?

🗣 Douglas

The lifestyle—slowing down, enjoying life (and maybe some golf).

🎙️ Gilbert

Where do you learn?

🗣 Douglas

Podcasts. I skim business/data books for ideas.

🎙️ Gilbert

First dream career?

🗣 Douglas

Photographer. My dad and I built a darkroom; I shot entire yearbooks.

🎙️ Gilbert

Advice to your 18-year-old self?

🗣 Douglas

Break up sooner. And invest in Microsoft. 😉

🎙️ Gilbert

What job would you be terrible at?

🗣 Douglas

Pro tennis player—shoulder surgery. Maybe a good tennis coach, though.

Resources & How to Connect

🎙️ Gilbert

Where can people find you and your books?

🗣 Douglas

Infonomics and Data Juice are on major booksellers. Connect on LinkedIn or visit DouglasBLaney.com.

Avoiding Excessive Detail and Technical Language

🎙️ Gilbert

Final takeaway?

🗣 Douglas

Data is a real asset. Don’t let accountants, lawyers, or insurers convince you otherwise. Measure it, manage it, monetize it.