1. Data Quality in the State of New Hampshire
Donna Curley – HMIS Project Manager
Al Vitale - Data Analyst
Bowman Eastern User Summit July 19-20, 2011

2. Why Data Quality?
HMIS is HUD’s response to a Congressional Directive to capture better data on homelessness
Regular measurement of data quality and outcomes related to critical processes as well as mandated HUD reporting
Identify issues such as training needs

3. Why Data Quality? Essential for community partners planning
Understanding of the nature and scope of homelessness
Funding applications
Public awareness and education

4. Why Data Quality? Every CoC is required to implement an HMIS
Every CoC is scored on this obligation as part of the annual CoC Competition
National data on homelessness is vital for HUD reporting and informs key policy decisions

5. Data Quality Framework
Client Record Definition
Ensure HUD mandated data is captured
Accuracy
Data must be entered correctly
Timeliness
Data entered promptly for reporting requirements

6. Data Quality Framework
Completeness
Information is correctly entered on all clients
Consistency
Performance information is consistent across time
Incentives and enforcement
Public recognition for compliance
Funding impacts for non-compliance

7. Data Quality Factors Prioritized process in the Agency
Give staff the time to participate in training and to complete entry
The environment/equipment arranged to support entry
Is the data used?

8. Data Quality Factors Trust
Client honesty is based on establishing rapport and the quality and content of the interview process.
Staff to System
Staff will elect not to enter information if they don’t trust.
Client to Staff
Clients won’t tell the truth if they don’t trust.

9. Common Data Quality Errors
Issues with Training
Entering “no” when you mean “yes”
Failing to enter information on some Clients
Date Errors (DOB is 7/11/1972, entered 7/11/2011)

10. Common Data Quality Errors
Transposing numbers
Spelling errors in names such as Glen vs. Glenn
Accidentally selecting the wrong response from a drop down

11. Data Quality Training New User Site Administrators
Policies and Procedures
Data elements/definitions/entry guide
Help Desk/Technical assistance

12. Ensuring Data Quality Provide monthly reports to agencies for review
Clear direction for correcting data
Procedures for correcting are defined
Staff to look at data reliability and validity issues
Adjust & add data quality reports

13. The 3 data element categories
Program Descriptor Data Elements (PDDE)
Universal Data Elements (UDE)
Program-Specific Data Elements (PSDE)
The Data Standards define specific, acceptable
responses for each data element

14. Incongruity (Wacky Data) Reports
Entry Date Precedes Birth Date
Client records are displayed if s/he is entered into a program before being born.
Pregnant Female
Client records are displayed if she is pregnant and less then age 15 or more then age 55.
Pregnant Male
This report displays client data if he is pregnant.
Questionable Age
Client data is displayed if s/he is less then age 0 or more then age 100.

15. Incongruity (Wacky Data) Reports
Age and Disabilities
If a client is less then 18 years of age and has Alcohol or Drug Abuse disability types the record is displayed.
Age and Head of Household
This report displays client records if the client is designated as a “Head of Household” AND is less then 18 years of age.
Age and Veteran
If a client is designated as a Veteran and the client’s age is less then 17 the record is displayed.
Children Not in a Household
This program reports client data if the person is under age 18 and is not affiliated with a household.

26. New Hampshire BOS Data Quality
How are we doing? Before Data Quality (1/1/05 – 6/30/08)
Number of errors per Client (of 4414 clients)
How are we doing? After Data Quality (7/1/08 – 7/1/11)
Number of errors per Client .62 (of 6231 clients)
A significant improvement!

27. Lessons Learned Partner with Agencies, buy-in is crucial
Establish Goals such as 100% AHAR involvement
Identify mandated elements and be clear on the definitions
Ensure data entry requirements are attainable
Ensure agencies are accountable
Have a plan for training and assistance

28. Data Quality Reports
Use them to understand if data is entered timely, completely and accurately
Generate them regularly (monthly or more frequently)
Have a process to allow for data correction
We have an obligation to remember that every HMIS record is a survivor story so we must understand the client need, and to do that we must collect accurate data

29. Donna Curley, HMIS Project Manager d.curley@harborhomes.org
Thank you!
Donna Curley, HMIS Project Manager
Al Vitale, Data Analyst