Do you value data? Of course you do, otherwise you probably wouldn't be subscribed to this blog. So my guess is that you appreciate data without bias. In this age of "Fake News" and other obstructions to our desired level of information quality (think broader than data quality) we have to be weary of how information is interpreted and whether the data we use, to draw a conclusions, is without bias.

If you are anything like me, you love a good cup of coffee/tea and your contribution to Starbucks’ last quarter (Q1FY17) report of $4 Billion USD net revenues¹ is more than you want to tell your spouse. So let me give you a few excuses to visit your local Starbucks in order to study some examples of data quality.

*Text in bold has additional explanation of the term which can be seen if you place your cursor over the text.

The IT department has just migrated 400,000 accounts from the legacy ERP system onto a new, bright and shiny, system sold by a large software vendor, but the Sales team is mortified. The Sales team has discovered that all of the Sales Notes fields (in addition to others) have been Truncated to 255 characters. The sales agents use the end of this text field to record all of the "juicy" (or current) leads and now this valuable information is no longer accessible...

When faced with a number of data quality issues, and urgent stakeholder requests to improve quality, it can be tempting to dive right in and clean the data. This however doesn't get to the root of the problem and one finds that many of the same types of problems have to be resolved over and over again. This can be not only ineffective, but also demoralizing when staff have to spend so much time to get back to where they started.