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Economic Value of Data (EvD) Challenges | @BigDataExpo #BigData #Analytics
Data has a direct impact on an organization’s financial investments and monetization capabilities
By: William Schmarzo
Apr. 19, 2017 03:00 PM
Well, my recent University of San Francisco research paper “Applying Economic Concepts To Big Data To Determine The Financial Value Of The Organization’s Data And Analytics Research Paper” has fueled some very interesting conversations. Most excellent! That was one of its goals.
It is important for organizations to invest the time and effort to understand the economic value of their data because data has a direct impact on an organization’s financial investments and monetization capabilities. However, calculating economic value of data (EvD) is very difficult because:
And in light of those points, let me share some thoughts that I probably should have been made more evident in the research paper.
Factoid #1: Data is NOT a Commodity (So Data is NOT the New Oil)
And here’s the important factoid about a commodity: every barrel of Texas light sweet is exactly like any other barrel of Texas light sweet. One barrel of Texas light sweet is indistinguishable from any other barrel of Texas light sweet. Oil is truly a commodity.
However, data is not a commodity. Data does not have a fixed chemical composition, and pieces of data are NOT indistinguishable from any other piece of data. In fact, data may be more akin to genetic code, in so much as the genetic code defines who we are (see Figure 1).
Figure 1: Genetic Code
Every piece of personal data – every sales transactions, consumer comment, social media posts, phone calls, text messages, credit card transactions, fitness band readings, doctor visits, web browses, keyword searches, etc. – comprises another “strand” of one’s “behavioral genetic code” that indicates one’s inclinations, tendencies, propensities, interests, passions, associations and affiliations.
It’s not just the raw data that holds valuable strains of our “behavioral genetic code”, the metadata about our transactional and engagement data are a rich source of insights into our behavioral genetic code. For example, look at the metadata associated with a 140-character tweet. 140 characters wouldn’t seem to be much data. However, the richness of that 140-character tweet explodes when you start coupling the tweet with all the metadata necessary to understand the 140-characters in context of the conversation (see Figure 2).
Factoid #2: Can’t Use Accounting Techniques to Calculate Economic Value of Data
Instead of using the retrospective accounting perspective, we want to take a forward-looking, predictive perspective to determine the economic value of data. We want to apply data science concepts and techniques to determine the EvD by looking at how the data will be used to optimize key business processes, uncover new revenue opportunities, reduce compliance and security risks, and create a more compelling customer experience. Think determining the value of data based upon “value in use” (see Table 1).
Table 1: Accounting versus Data Science Perspectives
This “value in use” perspective traces its roots to Adam Smith, the pioneer of modern economics. In his book “Wealth of Nations,” Adam Smith defined capital as “that part of a man’s stock which provides him a revenue stream.” Adam Smith’s concept of “revenue streams” is consistent with the data science approach looking to leverage data and analytics to create “value in use”.
We have ready examples of how other organizations determine the economic value of assets based upon “value in use” starting with my favorite data science book – Moneyball. Moneyball describes a strategy of leveraging data and analytics (sabermetrics) to determine how valuable a player might be in the future. One of the biggest challenges for sports teams is to determine a player’s future value since player salaries and salary cap management are the biggest management challenges in sports management. Consequently, data science provides the necessary forward-looking, predictive perspective to make those “future value” decisions.
Sports organizations can not accurately make the economic determination of a player’s value based entirely on their past stats. To address this challenge, basketball created Real Plus-Minus (RPM). Real Plus-Minus is a predictive metric (score) that is designed to predict how well a player will perform in the future.
Economic Value of Data Summary
To exploit the economic value of data, organizations need to transition the conversation from an accounting perspective (of what has happened) to a data science perspective (on what is likely to happen) on their data assets. Once you reframe the conversation, the EvD calculation becomes more manageable, more understandable and ultimately more actionable.
 Edited by Seth Miller User:arapacana, Original file designed and produced by: Kosi Gramatikoff User:Kosigrim, courtesy of Abgent, also available in print (commercial offset one-page: original version of the image) by Abgent – Original file: en:File:GeneticCode21.svg, Public Domain, https://commons.wikimedia.org/w/index.php?curid=4574024
 “Wealth of Nations”, http://geolib.com/smith.adam/won1-04.html
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