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

Data Quality 101: What is Data Quality Mike Lindsay, ICF Natalie Matthews, Abt. Data Quality 101: What is Data Quality Mike Lindsay, ICF Natalie Matthews, Abt. Learning Objectives Understanding SNAPS Data Strategy content related to Data Quality

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

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  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. 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

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

  6. 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

  7. 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

  8. 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

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

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

  12. 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

  13. Data Quality 101: What is Data Quality Mike Lindsay, ICF Natalie Matthews, Abt Current Requirements for DQ Section 4.2.2 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. Data Quality 101: What is Data Quality Mike Lindsay, ICF Natalie Matthews, Abt Proposed and Forthcoming Guidance Section 580.37 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. Data Quality 101: What is Data Quality Mike Lindsay, ICF Natalie Matthews, Abt So why a DQ Monitoring Program?

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

  19. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Data Quality 101: What is Data Quality Mike Lindsay, ICF Natalie Matthews, Abt Resources and Guidance • HUD Data Strategy (2018) • https://www.hudexchange.info/resource/5748/snaps-data-ta-strategy-to-improve-data-and-performance/ HUD Data Quality Brief (2017) • https://www.hudexchange.info/resources/documents/coc-data-quality-brief.pdf HUD Data Quality Toolkit (2009) • https://www.hudexchange.info/resources/documents/huddataqualitytoolkit.pdf

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