Data quality
The degree to which data is fit for a specific use, usually judged across dimensions such as accuracy, completeness, consistency, timeliness, validity and uniqueness. Quality is always relative to a purpose: data that is adequate for a monthly trend may be unfit for an automated decision.
Why it matters
Poor data quality rarely announces itself. It shows up as reports that do not reconcile, models that behave unpredictably and staff who keep private spreadsheets because they no longer trust the system of record. Each of these carries cost and risk that grows as more automation depends on the same data.
Effective quality management defines expectations for the data that matters most, measures them continuously and assigns someone to act when they are breached. Trying to make all data perfect is neither possible nor necessary.
In practice
For example, a regional healthcare provider preparing an appointment-demand model might find that cancellation reasons are free text and inconsistently completed. Rather than cleaning history by hand, it could introduce a controlled list at the point of entry, measure completeness weekly and exclude low-quality periods from training until coverage improves.
Where Rodan fits
Rodan assesses and remediates data quality as part of Analytics and Intelligence and AI delivery. See also how to get your data ready for AI in six practical steps.