1. Designing and Implementing a Data Quality Assurance Program for your CoC’s HMIS
Michael Lindsay, ICF International
Natalie Matthews, Abt Associates Inc.

2. Learning Objectives
Understand all components of a Data Quality Assurance Program and how this work fits into the overall efforts of the CoC
Discuss the roles that CoCs, HMIS Leads, and agencies play in implementing a Data Quality Assurance Program
Learn from the challenges faced by other communities implementing a Data Quality Assurance Program

3. Session Overview/Agenda
Purpose and Intent of a Data Quality Program
Review each of the 4 components of a DQ Program
Discuss roles, responsibilities and potential next steps for your community

4. Definition of Data Quality
Data quality refers to the reliability and comprehensiveness of your community’s data (as collected in HMIS)
Do you have sufficient data to accurately reflect the demographics, needs and outcomes of persons experiencing homelessness?
Components of data quality include
Timeliness
Completeness
Accuracy
Consistency

5. Current Requirements for DQ
Section of the HMIS Technical Standards (2004)
“PPI collected by a CHO must be relevant to the purpose for which it is to be used. To the extent necessary for those purposes, PPI should be accurate, complete and timely.”

6. Current Reporting on DQ
Traditionally, HUD reporting on data quality has focused on the rates of completeness (by data element).

7. Proposed and Forthcoming Guidance
Section of the HMIS Proposed Rule (2011)
“..HMIS Leads must set data quality benchmarks for CHOs, including bed coverage rates and service-volume coverage rates.”
With the emergence of System-level reporting and the maturity of HMIS implementations across the country, both funders and communities have started to place a greater emphasis on data quality, with many CoCs integrating data quality into their project ranking and scoring process and developing their own, more detailed data quality plans that extend beyond

8. Data Quality Plans
In anticipation of the HMIS Final Rule, and in response to NOFA scoring criteria for the CoC Program, many CoCs have created data quality plans
Plans often consist of
Baseline expectations for completeness, timeliness
Monitoring protocols for reviewing accuracy
Who has a DQ plan?
How do you monitor for compliance with DQ plan?
Who monitors agencies on DQ?

9. So why a DQ Program?
We argue that while having a good data quality plan in place is key, it is only the beginning and does not ensure that your CoC will have more accurate and reliable data
None of us want that DQ Plan to become just another stack of paper’s that sits on someone’s desk. That’s where having a clearly defined and active Data Quality Pro

10. Elements of Data Quality Program
CoC HMIS Data Quality Plan
Enforceable agreements
Monitoring and reporting
Compliance processes

11. Connection to CoC’s Overall Efforts
Data Quality Program
1. CoC commitment to improving DQ
2. CoC’s HMIS DQ Plan & Enforceable Agreements
3. Monitoring & Reporting
Compliance Processes
A lot of the work of the CoC (including coordinated entry and system performance measures), has started to be framed with the practice of collective impact.
We’ll spend a few moments now talking about what collective impact is, and how it translates into the work we’re discussing here on the development of a data quality program.
The first condition that must be in place for collective impact is a Common Agenda. In the context of HMIS Data Quality, that common agenda is the commitment of the CoC to have the most accurate and meaningful data as possible.
Next is a set of Shared Measures to engender shared accountability. This is he written document that you create to outline how you will achieve that—your CoC’s HMIS Data Quality Plan.
DISCUSS MORE W/MIKE to think through how and if to fold this in. Is it helpful?
4. CoC, Agency and HMIS Leadership Efforts
5. HMIS Lead’s administration of HMIS

12. Preparing for the DQ Program

13. Identifying Your Baseline
Important to take stock of where you are now
Do you know how many of the homeless assistance and homelessness prevention projects in your CoC, are actively participating in HMIS? Baseline for bed coverage
Have you recently run data completeness reports for your full HMIS implementation? Baseline data completeness
When CoC leaders, project staff and HMIS Lead staff review reports, does the data seem accurate? Baseline for accuracy

14. Step 1: Ensure CoC’s Commitment
Important to clarify up front what the expectations are for the data quality program
CoC will need to review and approve the DQ Plan
CoC should also be heavily involved in determining expectations for monitoring and compliance
This work cannot and should not fall just on the shoulders of the HMIS Lead Agency

15. Key Considerations in Step 1
How will the CoC enforce expectations for data quality?
Will these expectations extend to all homeless assistance and homeless prevention programs in the community?
How frequently will the CoC leadership review data quality reports and data analysis?

16. Step 2: Data Quality Plan & Enforceable Agreements
DQ Plan should be focused on
Defining data quality expectations, by data element and by program type
Completeness
Timeliness
Accuracy
Consistency
Outlining how data quality will be monitored
Who will monitor and when
Who will results be reported to

17. Step 2: Data Quality Plan & Enforceable Agreements
Enforceable agreements are critical
Need to be completed by all agencies participating in HMIS
Should provide guidance on what the consequences are for failure to meet the standards in the DQ Plan
Identify the process for notification of failure to meet a standard
Lay out the responsibilities of BOTH the HMIS participating agency and the HMIS Lead and CoC

18. Key Considerations in Step 2
Are the expectations and responsibilities reasonable?
Have they been discussed in a public forum, to allow for feedback and to generate buy-in from the CoC?
How far back do you need to go in terms of data quality improvements? Are you looking at “old” data? How does poor data quality impact your reporting efforts?
PAUSE HERE FOR SOME QUESTIONS/AUDIENCE ENGAGEMENT

19. Step 3: Monitoring, Reporting & Compliance Processes
Once the HMIS Data Quality Plan has been reviewed and approved by the CoC and agreements are in place, it’s time to get out there and implement
Will need to train/communicate to agencies and users first, to ensure that all users understand the expectations
Encourage the CoC to allow for a grace period
Transparency with results is key

20. Key Considerations in Step 3
Can the HMIS Lead monitor each agency for HMIS data quality compliance on an at least annual basis?
Does their monitoring process integrate all 4 elements of data quality?
Completeness
Accuracy
Timeliness
Consistency
How will monitoring results be shared with the agency? With the CoC?

21. Step 4: CoC, Agency & HMIS Leadership Efforts
Important to celebrate successes and to allow room for growth
Make the connection between the HMIS DQ efforts and other CoC lead efforts
Impact of improved data quality on the accuracy of System Performance Measures and other local data analysis
Impact of improved data quality on the ability to generate a By-Name or Prioritization List, to use HMIS for coordinated entry, etc.

22. Key Considerations in Step 4
Is everyone at the CoC, agency and HMIS Lead level clear about the role that they play in ensuring data quality?
How has this been communicated?
How has data quality been integrated into CoC, agency and HMIS meetings?
What are the motivations/barriers for getting people on board? Is special outreach or help needed to work with agencies that do not get HUD funding?

23. Step 5: HMIS Lead’s Administration of HMIS
HMIS Lead should complete the monitoring on data quality
Will need to run regular data quality reports for agencies to track progress beyond the monitoring visit
HMIS Lead is at the center of this work and needs to make these connections to CoC efforts with the community

24. Key Considerations in Step 5
Is the HMIS Lead regularly communicating about progress and barriers with the data quality program?
Has this work become an ongoing effort and is it integrated into the regular operations of the CoC, agencies and HMIS Lead?

25. What’s Next?
Don’t wait!! The quality of your data now will impact your upcoming SPM reports
Map out your baseline
Discuss these steps with your CoC
Review sample HMIS Data Quality Plans (they’re on the web!)
Talk to other CoCs about how they’ve done this sort of work
Spend time thinking through monitoring and compliance

26. Resources and Guidance
HUD Data Quality Toolkit (2009)
HMIS Proposed Rule (2011)
HMIS Technical Standards (2004)

27. Additional Questions Natalie Matthews, natalie_matthews@abtassoc.com
Mike Lindsay,