HMIS 101: Data Quality Overview

HMIS data are used to produce an unduplicated count of persons experiencing homelessness for each Continuum of Care, to describe the extent and nature of homelessness locally, regionally, and nationally, to identify patterns of service use, and to and measure program effectiveness. This information guides homeless services policy and decision-making at the federal, state, and local levels, making it imperative that organizations ensure their HMIS data are as complete and accurate as possible. Additionally, some funders require providers to enter information in HMIS to document the work their funding is supporting.

Given the importance of HMIS data and reporting to tell us how we are doing at addressing and solving homelessness, HMIS data quality cannot be overstated. A lack of HMIS data quality means that the story the community is presenting about homelessness is not a true reflection of reality, whether that story is being told nationally, statewide, or locally.

The benefit of quality HMIS data goes beyond meeting funder requirements. System-wide HMIS data can be used to demonstrate a lack of needed services in a community, and to track the progress a community is making towards ending homelessness, both of which would be nearly impossible if organizations worked independently.

Accurate client-level data must be gathered about each client served in the data analysis period, for each data element relevant to the respective measure. The data must also be entered into the HMIS correctly and timely. Correct entry and exit dates, destination, income and sources, and residential/housing move-in dates are some of the most crucial data elements for these reports. Client identifier information used for deduplicating clients across project enrollments is also important for a provider to review. Without accurate deduplication results, the measures may not reflect the reality of system use. When considering data quality to improve the accuracy of the report, prioritize review of these data elements for the data analysis period.