Part 2 of a 2-part series exploring the concepts, methodology, and process that any organization can use to determine the economic value of their data. Read Part 1.
Data curation and data governance is like going to the dentist; everyone knows it is good for you, but no one actually wants to do it. In the data warehouse era, probably one of the most difficult (and most often rejected requests) was getting the end users to own the governance of their own data sets. Why? Because these end users never saw nor understood the value of the data. But those days are a changin’.
My blog “Determining the Economic Value of Data” introduced several new concepts to help organizations quantify the economic value of their data. That blog highlighted some key concepts about the economic value of data, including:
- The frame for determining the value of data is not a “value in exchange” or accounting frame, but instead a “value in use” or economics frame. That is, the value of the data is not determined by what someone is willing to pay you for that data. Instead, the value of the data is determined by the value that one can create using that data to optimize key operational and business processes and uncover net-new monetization opportunities.
- One must start the data-valuation process by understanding the business and operational value of the organization’s key business initiatives. That is, what is the business trying to accomplish over the next 12 months and what is the business and operational value of achieving that goal? This establishes the value against which we will focus our “economic value of data” attribution process.
- Then one needs to go through a process (we call it “Thinking Like A Data Scientist”) to identify, validate, vet, value, and prioritize the use cases that support the organization’s key business initiatives. This process requires close collaboration with the key stakeholders and constituents to complete this identification-to-prioritization process.
Finally, apply data science to quantify the attribution of the use case’s business and operational value to each of the contributing data sources.
See the very long, even-more-boring University of San Francisco research paper titled “Applying Economic Concepts to Determine the Financial Value of Your Data” that details the concepts, methodology, and process that any organization can use to determine the economic value of its data.
And for those folks who need a refresher on some economic basics, check out my blog “Data and Economics 101,” because you’ll probably have a hard time digging up your college econ book buried in your parents’ garage.
Weaving raw data into gold
The story of Rumpelstiltskin was about a weird little man with the ability to weave raw hay into gold. Well, there may be a bit of truth in that old story, as leading organizations today are learning to weave raw data into business gold.
We understand that when raw oil is refined into high-octane fuel, the refined high-octane fuel is 16.9x more valuable than the raw oil. But how much more valuable would that barrel of high-octane fuel be if that barrel was never depleted, never wore out, and could be used over and over again across an unlimited number of use cases?
Obviously, the value of that barrel of high-octane fuel would be worth more than the 16.9x the value of the raw oil. In fact, that barrel of high-octane fuel that never depletes, never wears out, and can be used over and over again across an unlimited number of use cases would likely have infinite value.
That is what makes data a unique asset – an asset like we have never seen before. And you don’t need a weird little man (other than a data scientist) to weave raw data into business gold.
This article originally appeared on LinkedIn and is republished by permission. Hitachi Vantara is an SAP global technology partner.