1. Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt

2. Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Learning Objectives
Understanding SNAPS Data Strategy content related to Data Quality
Understand all components of a Data Quality Management Plan 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 Management Plan

3. Session Overview/Agenda
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Session Overview/Agenda
SNAPS Data Strategy and Data Quality
Purpose and Intent of a Data Quality Program
Review each of the four components of a Data Quality Program
Discuss roles, responsibilities and potential next steps for your community

4. Data Quality 101: What is Data Quality Mike Lindsay, ICF
Natalie Matthews, Abt
To view the full strategy, go to the HUD Exchange (session on strategy was completed at Spring 2018 NHSDC conference)

5. SNAPS Data Strategy and Data Quality
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
SNAPS Data Strategy and Data Quality

6. SNAPS Data Strategy to Improve Data And Performance
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
SNAPS Data Strategy to Improve Data And Performance
SNAPS strategy is intended to be aspirational
These are not standards that HUD intends to monitor projects for compliance
Focus on ensuring CoCs have data driven local planning and work towards ending homelessness
The Office of Special Needs Assistance Programs (SNAPS) has defined a set of goals it believes represent where the field and Federal government can be in 3 – 5 years.

7. SNAPS Data Strategy to Improve Data And Performance
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
SNAPS Data Strategy to Improve Data And Performance
CoCs should work with HMIS lead and agencies to
Review the Strategy
Set local goals and performance indicators
Identify what changes, if any, they need to make to their work to move closer to implementing HUD’s vision
The Office of Special Needs Assistance Programs (SNAPS) has defined a set of goals it believes represent where the field and Federal government can be in 3 – 5 years.

8. SNAPS Data Strategy to Improve Data And Performance
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
SNAPS Data Strategy to Improve Data And Performance
There are three unique strategies
For the purposes of today’s session we will just highlight strategy 2, since it focuses on data quality
The Office of Special Needs Assistance Programs (SNAPS) has defined a set of goals it believes represent where the field and Federal government can be in 3 – 5 years.

9. Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
SNAPS Strategy #2
Communities operate data systems that allow for accurate, comprehensive and timely data collection, usage and reporting

10. SNAPS Strategy #2 Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
SNAPS Strategy #2

11. Data Quality Data Quality 101: What is Data Quality Mike Lindsay, ICF
Natalie Matthews, Abt
Data Quality

12. Definition of Data Quality
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Definition of Data Quality
Data quality refers to the reliability and comprehensiveness of your community’s data
Components of data quality include
Timeliness
Completeness
Accuracy
Consistency
Do you have sufficient data to accurately reflect the demographics, needs and outcomes of persons experiencing homelessness?

13. Current Requirements for DQ
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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.”

14. Proposed and Forthcoming Guidance
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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.”

15. Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Data Quality Plans
In anticipation of the HMIS Final Rule, increasing need for data informed decision making 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

16. So why a DQ Monitoring Program?
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
So why a DQ Monitoring 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

17. Elements of a Data Quality Program
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Elements of a Data Quality Program
CoC HMIS Data Quality Plan
Enforceable agreements
Monitoring and reporting
Compliance processes

18. Preparing for the DQ Program
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Preparing for the DQ Program

19. Identifying Your Baseline
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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

20. Step 1: Ensure CoC’s Commitment
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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

21. Key Considerations in Step 1
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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?

22. Step 2: Data Quality Plan and Enforceable Agreements
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Step 2: Data Quality Plan and 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

23. Step 2: Data Quality Plan and Enforceable Agreements
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Step 2: Data Quality Plan and 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

24. Key Considerations in Step 2
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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?

25. Step 3: Monitoring, Reporting & Compliance Processes
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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

26. Key Considerations in Step 3
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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?

27. Step 4: CoC, Agency and HMIS Leadership Efforts
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Step 4: CoC, Agency and 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.

28. Key Considerations in Step 4
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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?

29. Step 5: HMIS Lead’s Administration of HMIS
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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

30. Key Considerations in Step 5
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
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?

31. Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Common Pitfalls
Ignoring data quality until reports are due or data is published
Emphasizing data quality for some staff and not others
Failing to keep agency management informed
Failing to understand the role/importance of end users
Not taking advantage of the potential for quality data reporting

32. Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
What’s Next?
Don’t wait!! The quality of your data now will impact your upcoming LSA reports
Review HUD’s Strategy
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

33. Resources and Guidance
Data Quality 101: What is Data Quality
Mike Lindsay, ICF
Natalie Matthews, Abt
Resources and Guidance
HUD Data Strategy (2018)
HUD Data Quality Brief (2017)
HUD Data Quality Toolkit (2009)