Big Data

3 Theorems on the Economic Value of Data

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) October 25, 2017

Since releasing the University of San Francisco research paper on “How to Determine the Economic Value of Your Data” (EvD), I have had numerous conversations with senior executives about the business and technology ramifications of EvD. Now with the release of Doug Laney’s “Infonomics” book that builds upon Doug’s EvD work at Gartner, I expect these conversations to intensify. In fact, I just traveled to Switzerland to discuss the potential business and technology ramifications of EvD with the management team of a leading European Telecommunications company.

From these conversations, I am starting to form some “theorems” to guide organizations regarding how EvD could impact their business and technology investments. A theorem is defined as “a general proposition not self-evident but proved by a chain of reasoning; a truth established by means of accepted truths.” Well, it might be a stretch to call these “theorems” at this point, but I hope over the next several months to turn these “observations” into “theorems.”

Also, I fully expect the number of theorems to grow as the EvD concepts mature, especially as organizations look for data and analytics to the fuel their digital transformation initiatives.

Economic Value of Data Theorem #1:

It isn’t the data that’s valuable; it’s the relationships and patterns (insights) gleaned from the data that are valuable.

We highlight the difference between monetizing data versus monetizing insights when we discuss the Big Data Business Model Maturity Index (see Figure 1).

Figure 1: Insights Monetization Phase of Big Data Business Model Maturity Index

Figure 1: Insights Monetization Phase of Big Data Business Model Maturity Index

Phase 4 of the Big Data Business Model Maturity Index is the “Monetization” phase. However, organizations should not focus on the monetization of their data, especially selling their data. Selling data is a business model decision, not a business transaction. And there are significant liabilities that await an organization that moves into the business of selling data (see Equifax…).

Instead, organizations should focus upon the monetization of the insights derived from the data. The monetization value isn’t in the data; the monetization value is in the unique customer, product, service, operational and market insights that are gleaned from the data. It is from these insights that organizations will be able to identify new services, new products, new customers, new markets, new audiences, new channels and new partnerships.

Economic Value of Data Theorem #2:

It is from the quantification of the relationships and patterns that we can make predictions about what is likely to happen.

It is the quantification of the relationships and patterns around customers, products, services, operations and markets that drive operational, management and strategic predictions. And it is the value of these predictions (in support of business use cases) that ultimately determines the economic value of your data. We want to quantify the relationships, patterns, propensities, tendencies, biases, preferences, associations and affiliations at the level of the individual customer, product, service, operational process and markets (see Figure 2).

Figure 2: Uncovering Relationships and Patterns in the Data

Figure 2: Uncovering Relationships and Patterns in the Data

From these detailed insights, organizations can make predictions about their customers, products, services, operations and markets: what products and services customers are likely to buy, when customers will likely have a life stage change, what products are likely in need of servicing or retirement, what operations are likely candidates for operational optimization, what markets are likely ripe for new products or services, etc.

These predictions, though never 100% accurate, give organizations an “edge” in their operations, management and strategic decisions and use cases. For example, having better predictions about which customers are likely to attrite and the predicted lifetime value of those customers gives you an edge over the competition. It may not be much, but sometimes it is the smallest of edges that can separate the winners from the losers.

Economic Value of Data Theorem #3

Predictions drive monetization opportunities through improved (optimized) strategic and operational use cases.

It is application of predictions against business use cases (i.e., clusters of decisions) that determines the economic value of the data. For example, it is neither sufficient nor actionable to know that there is an increase in head injuries, lacerations and broken bones during and immediately after a local professional football game. That’s interesting, but not actionable.

3_Theorum-Patriots vs Falcons Game Prediction

Figure 3: Patriots vs Falcons Game Prediction

However, if you can predict a 37% increase in head injuries, lacerations and broken bones during and immediately after the professional football game, then that is actionable! With that prediction, I can now make recommendations (prescriptive analytics) about extra nurses, doctors and supplies one might need at the hospitals nearest the stadium.

The Future of Economic Value of Data Theorems

I can see the potential for more theorems as the EVD discussions mature. I can see, for example, a theorem on “variable predictability” and its importance in attributing financial value to the appropriate data sources.

We will continue to explore, test, fail and learn as we seek to perfect the methodology and formulas that can help organizations determine economic value of their data sources. I believe that this will become a business mandate as organizations look for a management framework to help them optimize the business and technology investments that are driving digital transformations.

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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2 thoughts on “3 Theorems on the Economic Value of Data

  1. As always, you make the powerful distinction between the interesting and the actionable (here, via the football injuries). *Interesting* data analysis is a quicksand that so many organizations struggle to escape and have such a challenge recognizing. Your clarity on this issue has been inspirational!

    I wonder whether a fourth EvD Theorem relates to the power of rich, specific data sets to vet human intuitions about their business processes and customers. You can have a hunch that different shades of blue might impact CTR, but without the data to validate/nullify the hypothesis, an organization is flying blind. The data on their own might never get you there, but they can take a human insight all the way to an ROI payoff. Curious on your thoughts, agreement, pushback!

    • Hey thanks Adam. I think your point on the potential fourth EvD Theorem relates to the importance of human instinct and judgment to think “outside of the model.” Here’s what I mean by that. Machine Learning and Artificial Intelligence models learning is entirely dependent upon the data set on which these models are trained. If these data sets are biased, then the models will be biased. If the data sets are incomplete, then the models will be incomplete.

      And since it’s not possible to capture training data sets for all situations (especially those situations that have not yet occurred), then we must rely upon human intuition and judgement to help build models that think outside of the data sets upon which the training was conducted.

      Does that make sense? I think you’ve hit on something here!