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Mastering the Data Value Curve

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Mastering the Data Value Curve

It’s springtime. And in the U.S. that means the start of baseball season. Full confession: I’m a Brit and I like baseball films a bit better than the actual game (it’s just not cricket!), especially the improbably delightful Field of Dreams, where Kevin Costner plays a hardworking Iowa farmer who hears a voice whispering from his cornfield: “If you build it, they will come.” So of course he builds a baseball diamond in his backyard, chats up the ghost of Shoeless Joe Jackson, and saves the family farm.

The film was released in 1989, but the exhortation, “If you build it, they will come,” lives on today in many an internet meme. Its persistent half-life has a little bit of truth to it. Success in the world of data can also be measured by “those who come”—that is, by the number of data users who actually have access to—and use—the data they need to do their jobs.

If we plot this idea onto a simple graph, it looks something like this:

data value curve

We can see that data value relative to the degree of involvement by business users falls along a pretty standard curve. Data management activities—an important foundation to driving value—are at the lower end of the curve. Establishing a data dictionary, documenting your data, and understanding its lineage all help clarify the data to be used, but if no one can find this metadata, it’s not adding value. Too often this documentation sits in spreadsheets, on Sharepoint sites, or in hard-to-access applications. And without ongoing automated maintenance processes, it’s outdated as soon as its completed.

As we travel up the curve, we see more business users becoming involved in data governance activities, working collaboratively together and with IT to define standards for data so that everyone across the organization has a solid level of trust in the data they’ll be using. Those standards are typically defined by data consumers – and are often unique to each user’s context. For example, marketing’s definition of customer may be different from sales which is also different from finance’s. Which is OK—as long as everyone understands when they look at data, a report, or a metric, whose definition of customer they’re looking at.

But it’s not until the higher end of the curve, where data is discoverable by people across the organization, and used to support decision making aligned to business priorities, that we realize true business value: achieving strategic objectives –be they significant growth and better earnings, increased market share, or optimized risk management.

Sounds like a data field of dreams, doesn’t it? So, how do you get there?

Here’s the good news: you don’t have to start clearing the back forty. We’ve done the work for you, by building the Collibra Catalog, a single source of intelligence for business users who need quick access to enterprise data. Collibra Catalog isn’t another business intelligence platform, it’s more like a “data portal” that allows your users to easily find data from across the organization—that might mean reports built with a variety of business intelligence tools or the data used to build those reports that’s been sitting fallow and siloed for far too long. (Okay, I’m pushing the metaphor, but you take my point.)

With semantic search tools, business users can employ business terms that are meaningful to them, and convenient “shopping cart” functionality lets them pick and choose the data they need. Once trusted data becomes easy to find and use, your organization can say goodbye to foul balls and take a lap around the field after every home run.

 

 

 

Simon is the Collibra Global Financial Services Market Lead. During his 20+ years of management consulting services experience at leading consultancies EY and PwC, Simon helped financial services institutions design and implement information and data governance processes to support internal, external, and regulatory reporting including BCBS 239 and CCAR.