To Improve Data Quality, Start at the Source

Correct data leads to correct analysis and outcomes intended for collection. I cannot emphasise more. However, when you throw the disordered and unstructured data and expect the AI to figure out the “heuristics” and start “predicting”, it is a surefire recipe for disaster.

Sadly, most hospitals can’t see anything beyond the Excel sheets. Their digital operations are a mess and is usually offloaded to IT who is battling with the random blackouts of the computer screens. It is a good write up that encourages users to break through the organisational silos.

Rather than fixing data quality by finding and correcting errors, managers and teams must adopt a new mentality — one that focuses on creating data correctly the first time to ensure quality throughout the process. This new approach — and the changes needed to make it happen — must be step one for any leader that is serious about cultivating a data-driven mindset across the company, implementing data science, monetizing its data, or even simply striving to become more efficient. It requires seeing yourself and the role you play in data in a new way, all the while identifying and ruthlessly attacking the root causes of errors, making them disappear once and for all.

To Improve Data Quality, Start at the Source