This series explores phases at which gender data bias may be introduced or exacerbated in the simplified data lifecycle. The series contains four parts: requirements gathering, data collection, data cleansing, and analysis and use. This is part one: requirements gathering.

Healthcare’s appointment-level data has the potential to do a lot of good. A row of an appointment-level dataset usually includes provider detail, name and npi, patient detail, and appointment detail, length of time and exact type, down to the specific procedure code. When collected at the region, state, or national…

Third party data models are popular right now and its no surprise why. Using machine learning (ML), organizations can cut down on time-consuming processes and, inherently, the number of employees required to do a related task. However, the foundation organizations need to start building in-house ML models comes with many…

In any particular business domain, organizations find value in using maturity curves to both benchmark their behavior against peers and self-assess against an idealized future state. Although these curves are a useful positioning tool, they often place organizations at a neutral starting point — the origin of the coordinate plane…