Determining the Business Value of Data
Every executive has heard countless vendors observe that data is a valuable asset. But exactly how can one know the actual business value of data? If you question salespeople on this point, more than likely they will end up admitting that it is simply a rhetorical statement; of course one can’t really put a dollar value on an organization’s data.
Still, it raises an interesting question, doesn’t it? Most people tasked with overseeing their organization’s data know intuitively that data has value. But can that value be expressed in financial terms — and if so, what is the mathematical formula one could use to arrive at it? After all, accounting firms long ago developed reliable methods for conducting business valuation analyses for various types of corporate assets, so why couldn’t the same be done to find the business value of data?
Fortunately, the answer is that such a valuation can be done. The process is somewhat complicated, but here’s how it works at a high level:
It starts by establishing an Office of Data, led by a Chief Data Officer and tasked with data governance, metadata management, data quality and data architecture — responsibilities that are typically spread across various corporate functions. Because data is a valuable commodity within the organization, the organization needs an Office of Data to serve an essential, strategic role as the “broker” of data between its creators and its users.
A critical step is to develop a standard method for quantifying how the data elements of the organization exist within the organization. This metric must measure the level of organizational or tribal knowledge about a data element, the frequency of use or demand on the data element, and the overall quality of the data element. We call this metric the Data Certification Score, which is a holistic indicator that measures all the things described above.
Next you need to create an inventory of the hundreds of business data elements (BDEs) throughout the organization, and start acquiring the metadata, i.e., the information about how each type of data is created and used. Then you need to select the best individuals to serve as data stewards, and manage their efforts to monitor and improve the quality of the data under their purview, based on a handful of key metrics.
By analyzing the Data Certification Score for each of the physical instances of the data element, you can create a holistic enterprise-level view of how the BDE lives in the organization. The Data Certification Scores, in turn, can be rolled up into a central view that quantifies the data element’s existence and the pulse that is associated with each element by the community of creators, consumers, stewards and custodians across the organization.
Once the data certification scores are established for the most important BDEs of an organization, it is important to establish monitoring mechanisms that will recalculate data certification on some time frequency. Typically, the data community will establish that time frequency, and it will be closely related to the demand they are placing on the data. For instance, if a data element is a critical input to a monthly financial process, then the data certification score might be re-calculated monthly or weekly.
When the Data Certification Scores are calculated in this way, they provide the ability to execute correlation studies analyzing all of the Data Certification Scores to other key operational or financial KPIs or metrics of the organization. It is the correlation output that provides us with the empirical evidence of value. This final step is captured as metadata and is used to create the balance sheet of data, which provides visibility into the data elements that are worth the most — either from a revenue, cost or efficiency perspective, depending on what other metrics the scores show high correlation to.
Finding correlations between data and value
This is where the concept gets very interesting. By tracking the Data Certification Scores on a monthly, bi-monthly or quarterly basis, you can start to directly correlate individual data elements with your Profit & Loss statement or other key management reports used to run the business.
This means that you can uncover distinct, reliable relationships between specific data elements and essential indicators and metrics such as revenue and costs. By conducting some basic analyses, you can see in real time exactly where and how your data is adding to (or in other cases, hurting) your bottom line — thus determining the business value of data.
How will this insight help you? Imagine that you implement the system described here, and in the process determine that five specific data elements have a very strong correlation with revenue. Now you know exactly where to invest in improving data quality for optimal ROI.
Keep in mind, this is a new way of looking at the data that you’re already using to support your operational model. But this approach allows you to see that if you improve the Data Certification Scores for specific BDEs, you can forecast that profitability should go up by a defined amount over a given time period. It takes the traditional accounting process of business valuation, and applies it to a new kind of corporate asset that until now has essentially been a black box.
Best of all, this is not a theoretical model. It’s one that actually works, and is helping organizations gain greater insight into which components of their data actually drive business value, and to make reliable forecasts based on the relationship between data quality and other essential business metrics.