1. Methods and Techniques for Integration of Small Datasets September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development Deb Little, MISI Loren Hoffmann, Wisconsin Dept. of Commerce

2. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 2 The Landscape The Ideal All agencies in a continuum use the same HMIS The Reality Some agencies have invested substantial resources in their own system, which finally works “perfectly” Some agencies have multiple funders, some of which may require the use of another system or the reporting of data not collected by the HMIS of the CoC

3. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 3 Issues to Consider What are the Benefits? For the Community For the Participating Agencies For the Client What is the Goal of Integration? Unduplicated Count Service Coordination/Gaps Analysis Comprehensive Client Information

4. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 4 Where is the Problem? Why can’t I “just merge” the data?

5. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 5 Data Issues to Consider What data is included? Where is it to be stored? Who has access? Who controls the data? Who owns the data? Who maintains or keeps it in synch?

6. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 6 Data Issues What Data? Each field has its issues. Type - e.g., Name How many fields? Fname, Lname, Mname, suffix What about punctuation such as hyphens How is the data stored DOB vs. Age of client Picklist variations Education - 11 years of education vs. “some High School” How is it stored/coded Male vs. M vs. 1, etc

7. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 7 Where Is It Stored? Options for storing shared data: On the same server as the HMIS A neutral machine

8. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 8 Who Has Access? Who establishes the rules about the data? The integrated data has more value than the parts separately Who determines what is published? Who determines what is “cleaned” data vs. “raw” data? Who is able to release the data to another party?

9. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 9 Maintenance As the data needs/rules change over time, what effort is to be made to update historic data? Are all data elements collected to be maintained?

10. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 10 Options Centralized HMIS - used by everyone Dual Entry - agency enters data into multiple systems Those who have achieved either of these don’t need this session Integration Multiple systems maintained Data merged from multiple system (this session)

11. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 11 Issues to Consider Data elements necessary to achieve the goal? Only the Universal elements? Locally defined data elements? Frequency of data integration? Data Warehousing vs. Data Integration What are the political considerations? Identifying data vs. de-identified Who can view data? Who can release data?

12. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 12 Issues to Consider (Con't) Who will be involved in the planning and decision making process? Where will the data be merged and stored?

13. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 13 Resources to Consider Technical expertise Get them involved early Adequate budget Initial setup and on-going Available time Auditing tools How will we know how good the data are?

14. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 14 How to Make It Work Identify the appropriate model One way integration – independent agencies send data file for inclusion in central database Two way integration – agencies send data to and receive data from central system Data Warehousing *pros and cons to each method

15. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 15 Merge - Integration Options Real time Data transferred between two or more systems; each system is refreshed with data from the other Pro: Real time transfer between systems. Con: Very expensive; probably cost prohibitive for most CoCs.

16. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 16 Periodic Integration Periodic transfer of data between two or more systems Pro: Periodic updates. Con: Time consuming; not real time; could be cost prohibitive for many CoCs.

17. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 17 Periodic Merging for Analysis Only Aggregation of data from various system for analytical purposes only. Pro: Least expensive option; can generate unduplicated count. Con: No real time access to data; data is time limited.

18. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 18 Rules Needed Define needed “integration rules” File format required (CSV, XML, other) Data auditing procedures How to handle “bad data”

19. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 19 More Rules Value Mapping  Determining when values in the database mean the same thing.  Database A: Male=1, Female=2, Transgender=3.  Database B: Female=1, Male =2.  Database C: Female = F, Male = M

20. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 20 And More Rules Record matching Determine whether a record in two systems are the same Do I add a second record representing the same client? What is the “rule” for determining that the client in two different records is the same client? Is this service already there? Record matching approach will vary depending on reason for merging and what is merged.

21. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 21 Overall Process - Summary Data Conversion:  Create data standard: Per implementation.  Data mapping: Per system. Data Merging:  Export.  Validate.  Transport.  Validate Again  Import. Analysis:  As needed for reporting.

22. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 22 Analysis Integration is not complete when all the data is merged into a single database. Additional tasks:  “Unduplication.”  Use data from multiple agencies to fill in blanks  Handle conflicting information  Extract data.  Distribute data.

23. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 23 Baltimore Integration Currently: Large metropolitan implementation 90 agencies, 120 + programs using designated HMIS 7 agencies utilize the same third party software 1 agency utilizes product developed in-house 1 agency in development of integration “bridge” Issues: Avoid duplicate data entry Establish single reporting tool for “Homeless Stats”

24. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 24 Wisconsin Integration Currently: Statewide implementation 130 agencies 2-3 agencies need to participate due to funding requirements Issues: Don’t want to do double entry Client confidentiality

25. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 25 Baltimore (Con’t) Results: Data integration file defined; includes universal, program and continuum defined data elements One way integration model – agencies not using designated HMIS send data in regular intervals to central database. No data returned Data passing quality controls is processed against live database Auditing tools utilized regularly to detect and eliminate duplicate files

26. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 26 What Is Collected? Only HUD required data is currently collected. Agency provides one client record per client Agency provides one service record per service event (3 services = 3 records) - link on UniqueID field Unduplication based on HMIS algorithm

27. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 27 Wisconsin Integration Results: Data will be collected periodically for reporting purposes only Data will be housed in a separate database, accessibly only to state system staff System staff will generate reports, distributing only de-identified information

28. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 28 Summary Integration: Is complicated Will cost $$ Is an ongoing cost (time and money) Can be done If managed properly, can be very cost effective and beneficial

29. September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development 29 The Moral of the Story... Anything is Easy, Once You Know How!