What is data quality?
There are lots of ways of measuring data quality. A quick definition is: the degree to which
you can trust the data you are using for the purpose at hand.
Why does data quality matter?
Because problems with data quality can:
- Lead to inaccurate decisions or conclusions
- Increase costs (staff time, confusion, repetitive questions and issues)
- Create compliance or legal risk
Three steps to better data quality
So we designed a new guidebook, “How to Ensure Quality Data”, to support quality data collection in City and County of San Francisco. In our guidebook, we lay out the following steps to better data quality:
- Collect Needs and Requirements. Before you define your data, you need to know why you are collecting it and for what purposes. You also need to identify your user needs and what requirements the data faces.
- Define the Dataset. Once you have your requirements, you can define the data tables and fields you need.
- Define Policies and Processes. You will need to define a set of policies and processes to manage your data through its lifecycle
Check out the guidebook and our companion worksheet. Send any feedback via our help desk, support.datasf.org.