Like kids on Christmas morning, almost immediately after receiving and installing our favorite data quality tool at our shop we went wild and started cleansing data like crazy. After the indulgence of all this data cleansing (a data cleansing hangover?) we decided we’d take a breather and spend some cycles building metrics about our data. Yes, we should have done this first, but we needed to get some data cleaned up and we needed to do it fast. Once our framework for capturing data about our clients’ “data quality” was designed and implemented, we decided we’d represent these metrics as numeric values called “data quality scores.”
After some deep thought and significant effort, we were finally ready to communicate these “data quality scores” for clients to our senior management. We put together some reports and graphics, met with our senior managers and sure enough we were completely surprised when the fruits of our labor were met with disdain.
What did we do wrong? Something so simple it’s almost embarrassing. We didn’t know our audience. Our audience was comprised of senior executives and the senior executives at our shop like to hear good news and don’t like to hear bad news. The delivery of our message and associated “data quality scores” was completely backwards because we focused only on the negative.
We walked into the meeting with reports saying things like, “9% of client X’s data has issues” while we should have been saying “91% of client X’s data is error-free.” Our graphs should have indicated that our data quality scores went up when things were better, but they didn’t, they went down. Although mathematically our graphs were correct, the executives at our company like to think positive and graphically speaking they think “UP is good” – so our message (and our graphs) were all going the wrong direction.
Now, I’m not saying that we couldn’t explain our way out of this, nor am I saying that all “metrics” or “measurements” should always go up, but as data quality professionals we tend to focus on the negative way too often. Had we changed our communication strategy a little bit our hard work and cool graphs would have been much better received. In your data quality efforts, I’d strongly recommend focusing on the positive and when in doubt – remember – “UP is good.”
Happy holidays to all…Rich
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