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Degrees of Separation from Value

 

When starting any type of program, those who fund the work typically want to be reassured that they will receive value for their investment. Here is the challenge for those of us who run or participate in Data Governance programs: we rarely deliver direct Return on Investment (ROI). Instead, we are positioned several degrees of separation from ultimate value. Our challenge, then, is to demonstrate that we are on the critical path for delivering that value. In other words, that our contribution is a “need to have” and not a “nice to have.”

How do we do this? Our message is likely to be lost if it is delivered only in paragraph form, where we lead our readers down the path from our contribution to ultimate value. The path simply has too many steps. So instead, it helps to deliver the same information in a form that the visual processing part of our brains can understand.

I like this approach: We describe our value using a series of IF-THEN equations. They take the form of if A, then B, therefore C, where A is an action, B is the result of that action, and C is the impact.

Here’s an example.  I’m currently serving as the Corporate Data Advocate for IFC, the private sector arm of the World Bank Group. IFC lends money to clients for projects that should advance the World Bank Group’s dual objectives of ending extreme poverty within 30 years and promoting shared prosperity.

 

Consider the situation of farmers who are living on the edge of poverty. In some cases, they could gain income if they could get their crops to expanded markets. We can express their situation this way:

If A (the farmers can get their crops to expanded markets), then
B (they can increase sales), and therefore
C (reduce poverty for those farmers, their families, and their local economies).

 

Company XYZ is willing to build a transportation system, but they need funding. They ask IFC to provide some of that funding.

If A (IFC or another source) provides funding, then
B (XYZ will build a means of transportation), and therefore
C (farmers will have a means of getting their crops to and extended market).

 

But here’s a big question: Should IFC provide this funding? To answer “yes,” IFC leadership needs answers to many, many question about the proposed project and also about their  potential partner (XYZ), the country and region in which the project will take place, other efforts under way in XYZ’s industry sector, the stability of the currency involved, and other factors that will help IFC consider the proposed project within a global context.

IFC leaders expect to make these business decisions based on correct, consistent information. And here is where Data Governance enters the value chain. The IFC Data Governance team works fiercely to ensure the quality and consistency of the data sets that describe acceptable values for Currencies, Countries, Sector Codes, and other Corporate Reference Data.

 

Now, the Data Governance equation provides input into the other equations:

If A (Data Governance [working with IT and other groups] does its job correctly), then
B (Corporate Reference Data will be presented correctly in the information used by our corporate decision makers), and therefore
C (decision-makers can have confidence in these contextual aspects of their decisions).

 

So how many degrees of separation is my team’s work from the ultimate act of helping those farmers receive enough money to live better and maybe even provide schooling for their daughters? (Did you see what I did there, assuming another A-B-C where A is income and C is education?)

The answer depends on how you count. Are we three chains away from value? Seven steps across three chains? Some other measurement?

I don’t know. But here’s what I do know. First, when I look up from my work, I can draw a direct line between what I do and some very needed changes in the world. How cool is that? And second, despite our best efforts, sometimes a piece of “bad” data slips through. It shows up in a report or dashboard. A project is momentarily inconvenienced, as leadership questions the data, we investigate it, and then it gets corrected.

And this, my colleagues, becomes an opportunity to spell out the Data Governance value chain, and to remind all of our stakeholders how the work we do in the background keeps such “moments of inconvenience” to a minimum.

It’s our chance to remind them what their valuable work would look like if we weren’t constantly toiling away, several degrees of separation from value.

 

Image courtesy of Stuart Miles / FreeDigitalPhotos.net

 

About Gwen Thomas

Currently the Corporate Data Advocate at the World Bank Group's private sector arm (IFC, The International Finance Corporation), Gwen Thomas is the Founder of The Data Governance Institute and primary author of the DGI Data Governance Framework. Gwen has personally helped build Data Governance programs at the Federal Reserve System, Sallie Mae, Disney World, NDCHealth/Wolters Kluwer, American Express, Washington Mutual Bank (WaMu), Minnesota Pollution Control Agency, Wachovia Bank, Coors, and others. Gwen frequently presents at industry events and contributes to IT and business publications. She is the author of the book Alpha Males and Data Disasters: The Case for Data Governance.