Josh Pack, PhD
Principal Consultant, Data Strategy Group

Let’s use an anecdotal story to illustrate how hidden data correction processes create a lot of non-value-added costs at your company.  Our story is adapted from “Getting in Front on Data,” by Thomas C. Redman, PhD, a title we highly recommend.

A bright up-and-coming executive named “Samantha” is making her final preparations for a Board presentation, when she and her analyst, “Steve” see some sales figures that don’t seem right.  She asks Steve to double-check the data.  Steve, ever diligent and resourceful, soon identifies and corrects some errors that were made by the operations staff.  They update Samantha’s presentation and deliver to the Board with rave reviews.  Samantha’s a star, and Steve further solidifies his already great reputation.  And to ensure more great performances in the future, Samantha asks Steve to check for sales data errors every month.

Sounds wonderful, right?

Wrong.

Samantha and Steve have just created a hidden data factory.  This creates problems and adds cost on multiple levels:

1. They’re now making custom manual accommodations for bad data. (wasteful workflow)
2. Their corrections are not being standardized or implemented into operations. (missed opportunity to correct data at the source)
3. They have not actually validated that their corrections are appropriate with subject matter experts in operations. (potential to unknowingly exacerbate inaccuracies)
4. They have unwittingly made themselves responsible for the quality of the data in the eyes of the Board. (ambiguous accountabilities and audit trails)
5. Their custom version of the truth attracts rivals who seek to refute their data. (reinforced silos and erosion of relationships)
6. They have indirectly frustrated IT, who is typically blamed for data discrepancies. (adds unnecessary burden on IT resources and decreases their level of trust)
7. Worse yet, Board level strategy may be impacted by data that hasn’t been properly vetted, or worst of all, far-reaching decisions are made based on inaccurate information. (false sense of security, misguided company, angry shareholders)

Stories like these are all too common.  The tragedy is that hidden data factories can all be avoided.  The challenge is that organizations are not usually structured or staffed to address the quality of their data as they would with their actual products or services.  But the methods and platforms for implementing Data Lifecycle Management are not complicated; rather, they require commitment and plain old hard work.

Keller Schroeder’s Data Strategy Group is here to help you diagnose, plan, and implement strong data culture and processes.  Give us a call, and we’ll roll up our sleeves together and get going.