“Business data inventory” is a somewhat challenging concept, so people often use analogies to talk about it. Two of my favorite analogies have to do with the economy and the stock market.
I’ll share these, and then tie them together in a way that can help your organization increase the value it derives from its data.
Data supply and demand
The first analogy is all about supply and demand. Just as the marketplace has producers and consumers, your company has data producers and consumers. Think of data as a commodity, constantly being created on the “supply side” as new data is added to your system; on the “demand side” are all the individuals and departmental users of that data. These users can range from a customer service rep looking up a customer’s account number to a CEO reviewing metrics to evaluate a new strategic direction for the company.
Depending on what insights your data consumers need to get from your data, your business data inventory becomes increasingly important. And that means it can be especially important to invest in improving the supply side of your data marketplace — specifically, improving the quality of the data by reducing errors and redundancies that are all too common.
A lesson from the stock market
Now for my second analogy. To understand the real value of fundamentally sound data that’s well curated and managed, consider the stock market.
Suppose there was a stock that traded under two different stock tickers — acronyms that were very close, but different. Let’s say that Smith Plastics, Inc. traded under both “SPI” and “SMP.” Even though it’s the same company selling the same product, people would be buying the stock at different prices. There would be no way to arrive at an accurate market value.
The classic example of this problem occurring in an organization’s data involves “customer name.” Throughout your company, various departments and individuals have built different data repositories that contain this information, also known as a business data element (BDE) — but they often refer to it by slightly different terms. For example, it may appear under “customer name” in a spreadsheet maintained by your marketing team, while in a database used by accounting, it is tracked under a field labeled “name.” In yet another repository, the same information might be divided into “customer first name” and “customer last name.” These discrepancies might seem trivial — but just as in the Smith Plastics analogy, the result is that your organization can’t really aggregate the information stored across the enterprise and arrive at an accurate number of customers.
That’s why one of the first and most important steps a company can take in data improvement is to develop a business data inventory that maps all of the different occurrences of a given piece of data throughout the enterprise, no matter what it’s called in each different repository. This resource helps you identify and resolve discrepancies that could otherwise make your data essentially useless.
Avoid the temptation to rush to the demand side
One word of caution: there are products on the market that promise to build a business data inventory for you. It sounds great: you just push the button and presto, you’re done. The problem is what these tools don’t do — and that is the additional analysis needed to identify matches, redundancies, and errors that can cause your data to be misleading. For that, there’s really only one solution: a methodical, face-to-face process that gets information from the heads of the experts in your organization who know how data is used, where it’s stored, and under what names.
There may also be a temptation to go right to the demand-side and buy the latest business intelligence or data science tools to help your people derive greater insights from your data. But a more fundamental and strategic approach is to start by focusing on the supply side, and ensure that the data is as accurate and logically structured as possible. That way, the insights you gain are reliable, and based on the latest, most accurate data available.
The best approach to balancing the needs of the supply and demand sides by establishing an Office of Data (led by a Chief Data Officer, or CDO). Representing a new type of function within your organization, the Office serves an essential, strategic role as the “broker” of data between its creators and its users.
The Office should be tasked with data governance, metadata management, data quality and data architecture —responsibilities that today might reside across multiple functions within your organization.