Similar to my initial thoughts on my Spreadsheet Nation post, I jumped into this topic of Data Quality without really testing the waters.  In this case, I thought I could just jump in, rhyme off some platitudes about Garbage In, Garbage Out (GIGO), and go on my merry way.  Instead, what opened up to me was a vast sea and I was a fish out of water.  I was standing on shore clueless about what lay beneath the surface.

Malaspina Strait, British Columbia, Canada

Data Quality really is one of those topics that tends to lurk under the surface – elusive to capture.  We are talking about “the state of completeness, validity, consistency, timeliness, and accuracy that makes data appropriate for a specific use” (definition courtesy of the Government of British Columbia).  Or if you prefer, there’s the Dragnet definition: “Just the facts”.  For accountants, we are talking about all that stuff we enter into our systems (or gets generated by other systems) that we need to access later for producing reports and analysis.  The Data Quality refers to how effectively we can gain access to and generate meaning from these volumes.

A great deal of energy tends to go into our design of ways for inputting data.  How much thought has gone into the processes designed for getting the data back out?

According to IDC, a leading technology research firm, very few companies have systems in place to make use of their data, and [they] often struggle to classify data in order to find it again.  There’s a great quote on the V3 blog from Benjamin Woo at IDC:

“The key is to take the data and make money from it”

I think that this frames the issue in language we can understand.  We incur costs for gathering, processing, and storing data.  We may even incur further costs cleansing, reworking, and managing the stores of data.  What does the data do for us?  Are we developing an asset that creates future value?  Or, are we plugging an expense?