This is the first of a 4-part series focusing on data quality. This first installment will provide an overview on what data quality is, and why it is important to the work we do.
Why Data is Important: Getting the Funding You Need to Do What You Do
The use of HMIS is helping policymakers, researchers, service agencies and other community stakeholders to more fully and precisely understand the size, scope, and dynamics of homeless related issues. Historically, agencies have relied on the use of anecdotal evidence – or stories – to convey the impact their programs have on the clients they serve. While those stories are still very important to tell, the significantly increasing competitiveness for funds at the federal, state, and local level has initiated a need for programs to produce concrete evidence and measurable outcome achievement data to demonstrate that they are truly making a difference to clients and in their communities. As a result, data is used to drive the decision-making process of who gets funded and who does not.
Components of Data Quality
Defining data quality is complex, but we will try to simplify it for you here. While there is no one single definition of data quality, there are four terms most frequently used to describe it:
- Completeness – Ensures that all the appropriate and relevant data that agencies or funders need is being collected and recorded, and that each CoC can accurately describe both its clients, and the full scope of services provided to those clients accessing services.
- Timeliness – Reducing the time between data collection and data entry will increase the accuracy and completeness of client data. If updated information is not recorded in the system, analysis is done on outdated and inaccurate information.
- Accuracy – Ensures that what is being recorded in a database is an accurate and true portrayal of what is happening in the real world. If inaccurate data is recorded for a client it could impact their eligibility for a program, or at a broader level it could impact an agency’s score on performance indicators.
- Consistency – It is crucial that all aspects of a client’s profile and assessment data “agree with” each other, and that there are no contradictions of data. It is also important that agencies and staff members utilize the same definition for capturing data. HMIS utilizes the Department of Housing and Urban Development (HUD)’s HMIS Data Standards, which defines each data element collected in ServicePoint.
Why Should You Care About Data Quality?
Data quality is a critical problem facing public and private organizations alike. As the use of data reporting continues to drive funding decision-making, data quality should become a hugely important aspect of your efforts to ensure that you can continue to provide services to the people you serve for years to come. Funders making decisions based on inaccurate information can have costly consequences – both economic and social – not only for organizations but for the continuum as well. In simple terms, inaccurate or poor data leads to poor decisions. A way to visually understand this concept is to think of the old adage: “Garbage In, Garbage Out.”
For actionable insight, there must be quality data. This is extremely important to keep in mind because without it, decisions could be made based on faulty information. Ensuring the implementation of high-quality statewide data allows service providers to more efficiently meet the needs of the clients, identifying what their needs are likely to be, and how well they responded to past interventions. By capturing relevant information on clients served, we can also better understand the extent and nature of homelessness in our community. Further guidance on data quality monitoring will be provided in future newsletters.
 HUD HMIS TA Initiative. (2009, October). From Intake to Analysis: Toolkit for Developing a CoC Level Data Quality Plan. Retrieved from: https://www.onecpd.info/resources/documents/huddataqualitytoolkit.pdf