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Ensuring Data Quality

I work with a data-savvy client. She leads a team that helps people leaving hospitals to transition back into the community. Anytime my team fixes a bug, refreshes data, adds new measures — I provide a visual summary of how updates impact her stakeholders.
Even if clients is not as data centric, it is still a good way to share that my team is running quality checks and understands downstream impacts.

Bar chart showing measure change by data load

This visual illustrates how a bug fix brought a health care provider’s most recent performance back up to the historical average.

Table for drilling down into data anomalies

The data grid is part of an automated system that I built to spot data anomalies. The grid captures how a metric changed between the current and prior data load. The numerators and denominators are valuable as a tiny change in either can make a big difference for some metrics. I use the grid to guide granular conversations with my team or the client.

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