In this contributed article for insideBIGDATA, Kyle McAllister, Practice Director, Data Analytics for Pivot Point Consulting, discusses with how many healthcare organizations are willing to invest in new technologies like AI/ML with no plan for what they will do with it once acquired. One of the most pervasive examples of this phenomenon in the big data era is often referred to as “lazy data.” This is data gathered for the sake of gathering data, with no real use case behind it.
Prematurely transforming data during the ingestion process, rather than maintaining it in its raw form until its use case is known, results in the loss of the original details and schema of the raw format. This can limit future uses and value of the data. It would be like Ray Kinsella deciding the voices in his cornfield were telling him to build a soccer field instead of a baseball diamond. Shoeless Joe would have been disappointed when he stepped onto the field with his bat and mitt.
The best approach to avoiding lazy data is simple but not necessarily easy. Put your data to work!
Read the full article here to learn best practices for avoiding lazy data.
About the Author
Kyle McAllister, Practice Director, Data Analytics for Pivot Point Consulting, is a healthcare IT, analytics and population health strategist with experience leading organizations and teams through complex data, analytics, and other IT projects from visioning to execution to optimization.
