Fictional Case Study
Sulingituk Government recognizes the impact that safe, suitable, and accessible housing has on the well-being of a community. They have committed to creating a 20-year plan for housing development. As part of the plan, Sulingituk has identified the need to conduct a housing needs assessment; however, the poor quality of their existing housing data makes it challenging to conduct the assessment.
In every step of the data lifecycle, from collection to reporting, there is the potential for error. If your data has many errors, it will not give you an accurate picture of what’s going on. Ensuring that you are collecting good quality, accurate data is vital to helping your government make informed decisions.
This downloadable resource covers:
- Key factors in the quality of a dataset (i.e., accuracy, consistency, completeness, uniqueness, timeliness, validity).
- Documentation of metadata (i.e., information about a dataset).
- Data system quality (i.e., quality throughout your government).
- Quality assurance (i.e., the process of preventing errors in your data).
- Data entry best practices (i.e., inputting your data in ways that minimize error).
The webinar below is a presentation by Rebecca Wortzman and Lela Draganic (Big River Analytics) on data management, including data quality. For more webinars covering content in this toolkit click here.