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 leaderTo Improve Data Quality, Start at the Source
that isserious about cultivating a data-driven mindset across the company, implementing data science, monetizing its data, or even simplystriving 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.