Quality, reliable data is achieved when a community completes the Youth and Young Adult Scorecard and has reported 4 months of accurate data - within a 15% data reliability threshold. The data reliability threshold is primarily a check to see if your data transformation process is working appropriately. The other layer to data reliability is whether or not a community is following best practices for data entry that are resulting in an accurate BNL in real-time.
Our communities’ By-Name Lists rely on a number of different elements. The communities with the most accurate and complete list and aggregate data possess/undertake the following:
Timely, accurate and complete data collection by providers (especially current living situation and SOGI data)
An accurate and timely raw report from HMIS with client level data
An accurate and uniform data transformation process (usually through a tool in Tableau or Python) that transforms a raw report from HMIS into monthly aggregate numbers (inflow, actively homeless, outflow)
Regular maintenance of the By Name List through regular use (ie. through case conferencing and list management)
Most Common Barriers to Data Quality and Reliability
Timeliness and Data Clean-up
Problem: Data is not being entered within the reporting period. When this happens, data is backdated and can throw off data reliability.
Solution: Document a timeliness and accuracy policy and procedure that outlines data entry expectations and ensure providers are entering, updating, and exiting clients within the reporting period. A reporting period is the first of the month through the last day of the month.
Problem: Data Clean-up happens outside of a reporting period. For example: Data clean-up happens every 3 or 6 months. This means data will be backdated, inaccurate, and no longer real-time.
Solution: Data Leads meet with agencies and establish a data clean-up protocol to be performed each month, at minimum.
Actions to consider:
Revisit your policy and procedures regularly with providers
During data team meetings, discuss any barriers providers are experiencing with timely data entry and clean-up
Engage with leadership to discuss solutions
Data Collection Gaps
Problem: Current Living Situation is not collected and/or updated. This can cause inaccurate disaggregated counts of actively homeless on your BNL and can affect the active status of a young person.
Solution: Understand which projects in your community require CLS collection according to HUD standards and that providers know how and when to complete a CLS update. CLS updates need to happen within a reporting period. Find more guidance on our Current Living Situation resource!
Problem: Poor demographic data quality, especially Sexual Orientation and Gender Identity (SOGI). Incomplete fields in data entry will result in “nulls” or unknowns.
Solution: Check if this field is included in your HMIS projects. Advocate to your local HMIS decision makers to include it.
Actions to consider:
Review intake forms and verify that all data collection fields are included
Meet with HMIS-participating agencies to talk about why Current Living Situation and SOGI data collection is important for your BNL
Find more ideas on our SOGI Improvement Guide!
Data Transformation Issues
Problem: There is not regular, timely access to raw data or HMIS exported reports. This can halt data submissions and the data workgroup’s ability to utilize the data.
Solution: Clarify roles and responsibilities for your Data Lead and ACI Coordinator as well as expected timelines. Whose responsibility is it to download the report each month? What day of the month will this happen? Are there steps that need to happen first?
Problem: There are knowledge gaps and/or delays in the transformation and submission process.
Solution: Clarify roles and responsibilities for your Data Lead and ACI Coordinator. Create a data transformation and submission process document that can be easily accessible.
Problem: Lack of capacity.
Solution: Establish a community data team that includes your Data Lead, ACI Coordinator, and other folks in your community who can be valuable members and data cats! Clearly outline roles and responsibilities.
Actions to consider:
Clear roles, responsibilities, processes, and procedures are crucial. Verify that all data team members are on the same page
Think about sustainability on your community data team. What if a member is unavailable? Do you have a back-up? Can you access what you need to? Are your processes dependent on one person? What steps can you take to create sustainability?
If extra steps are required to obtain monthly data reports, create a standardized, recurring process
Build a culture of “everyone is a data person!”
Above all, it is important that all pieces of your system are working together. This means that all your community policies are being implemented, folks are working on the same data timeline, and everyone understands their role and contribution to a quality, reliable, and real-time By-Name List.
It is recommended that communities create a Data Manual where you can house your data policies and procedures, data transformation processes, data definitions, and any relevant information you think is necessary to ensure the maintenance of a quality BNL. Feel free to use this template to get started. Have fun, Data Cats!