Big Data

Economic Value of Data Interview

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) June 19, 2017

During the recent Dell EMC World in Viva Las Vegas, Dell EMC TV interviewed me about my just-completed research project with the University of San Francisco, where I am an Executive Fellow and teach the School of Management course titled the “Big Data MBA.” Check out the video at the bottom of this post (need to find a way to get the TV to shed some of those extra pounds).

Here is a summary of the conversation that ensued during and after the interview.

Bill – some of your recent work, including blogs, speaking engagements and research, have honed in on the importance of calculating the economic value of data (EvD). What do you mean by economic value of data?

  • Economic value of data, or EvD, is a management framework for attaching financial value to an organization’s data assets. If data is truly an asset, then organizations need to find a way to represent the value of their data on their Balance Sheets.
  • Here’s the real challenge: if organizations do not know how to properly value their data assets, then they lack a basis or a framework for identifying, prioritizing and optimizing their investments in data and analytics
  • For some organizations, understanding and managing EvD is the heart of the digital transformation process. A properly constructed EvD methodology can become the management framework for setting, prioritizing and management Digital Transformation initiatives, with supporting predictive and actionable dashboards to proactively optimize EvD investments…but now I’m getting a bit ahead of myself.

What was the goal of the research paper? Are there any key insights that you can quickly share?

  • The goal of the research paper was to introduce a different Economic Value of Data (EvD) methodology that not only helps organizations to determine the economic value of their data, but also to put an economic valuation on the resulting analytics (think of the value of an individual’s FICO score, for example).
  • I think one of the aha moments occurred when we realized that using traditional accounting (GAAP) principles to calculate EvD have proven to be insufficient because accounting takes a retrospective or historical view on valuing assets; accounting values assets based on what you paid to acquire those assets. It assumes that depreciation follows.
  • So instead of taking a traditional yet faulty EvD approach based upon Accounting, we proposed a new approach based upon economics and data science to provide a future or predictive view of EvD.

So how can an organization take steps to incorporate this methodology into its business processes and what should it expect to gain?

  • Start with the organization’s key business use cases for data analytics; that is, what is the business trying to accomplish over the next 9 to 12 months. I highlight the word business because it is the financial value of these business use cases (e.g., customer attrition, predictive maintenance, population health, new product introductions) that is the anchor point for determining EvD.
  • Next, identify the decisions that make up these use cases. That is, the organization must make the optimal decisions about what, to reduce attrition, predict machine failures, or develop new products. It is around these decisions that we can determine the economic value of the organization’s data and supporting analytics.
  • By the way, it is also around these decisions that we can make the determination as to what data sources we need (and the relative value of the data sources to that use case) and what analytics we need to build.

Who should be taking the lead on doing this? The CFO? CIO? Chief Data Officer?

  • Ideally, the CDO should take the lead, but the CDO needs to think more like a “Chief Data Monetization Officer” or CDMO than a traditional IT-centric CDO. Ideally the CDO has more of an economics background than an IT background, because the CDO needs to be the leader in optimizing the digital sources of monetization.
  • A CIO can also take the lead, but the EvD methodology requires the close collaboration with the line-of-business leadership in order to 1) identify and prioritize the targeted business use cases and 2) determine a rough financial value of those business use cases; for example, how valuable is it to the organization to reduce maintenance costs on your wind turbines by 10%?

What are you expectations for the future on this topic? Will it become a widely accepted business practice with standard methods?

  • I think this Future View of EvD has the potential to change not only how organizations prioritize their data and analytic investments, but will likely change the way that organizations determine the valuation of their businesses.
  • In conversations with customers who have read the research and have had subsequent conversations, the results truly appear revolutionary in helping organizations to re-think, re-prioritize and increase/optimize their investments in their data assets and analytic capabilities.

 

DEW17: Bill Schmarzo, Dell EMC from Dell Broadcast on Vimeo.

And here is a link to the University of San Francisco research paper:

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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