On a recent Southwest flight I was reminded how easy it is for important business processes to fail. The flight attendant came on the speakerphone and politely asked if Eve Adamson (pseudonym) was aboard the aircraft, and if so could ring the attendant call button. If you fly much you know this is a very common occurrence. But if you think about it, this breaks all the rules of common sense and the TSA boarding procedures.
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.
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.
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.
"For the data industry, standardized definitions of data quality dimensions are needed, just as other professions, such as accounting have agreed-upon concepts and terms like “chart of accounts”." --Danette McGilvray, Granite Falls Consulting
