Data Quality Practices and Measurements

Storage is cheap, processing is flexible, and data is a hidden trove of value increasingly used to drive business decisions and priorities. We generate it by the terabyte. We copy it from database to database, transform it from NoSQL to Relational data to graphs and charts.  We ingest it from customers and reflect it back to them with augmentations and additions our Sales departments have promised are just what is needed to propel them to the next level. More than ever data drives our customers' business decisions as well as our own. Poor quality data can mean poor decision or worse customer outrage. After some hard lessons in what poor data quality can cost, we determined to identify poor practices that were hurting data quality, encourage better practices to build quality in, and find ways to measure data quality in order to determine what was working and what wasn't.

Paper | Presentation

Nick Bonnichsen

Somehow I've acrued over 20 years experience in QA, starting in gaming, moving through device testing, semiconductor fabrication, automotive parts, and landing in the EdTech industry. I graduated from University of Oregon in 1999 with a CS degree. I enjoy board games, table tap RPGs, sporadic camping, and traveling all over the world with my wife.