Homeless Management Information
Systems (HMIS) Data Quality:
Practical Strategies and Tips
for Improving Data Quality
OVERVIEW
• What Is Data Quality?
• Why Is Data Quality Important?
• How Do You Know if Your Data are Good?
• Playing Your Part to Enhance Data Quality: Tips for Every
Level
• Common Data Quality Issues for Universal Data Elements
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
2
What Is Data Quality?
• Data quality refers to the accuracy and completeness of
information collected and reported in HMIS.
• Quality data allows programs, agencies, and CoCs to
make accurate statements and findings about persons
served or about program outcomes.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
3
Why Is Data Quality Important?
• Missing identifiers make unduplicated counts unreliable
– Inaccurate counts may under or over represent the
population and may impact funding
• Misrepresenting client characteristics can lead to:
– Misdirected resources
– Priority given to certain types of programming.
• Incomplete entry and exit data cannot reveal:
– How people move in and out of the homeless system; or
– What combinations of services are most effective in moving
persons out of homelessness.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
4
How Do You Know If Your Data Are Good?
• Run Reports to See if Data Are:
– Complete
– Timely
– Accurate
– Consistent
• Reports can be:
– Aggregate level, to spot overall issues with CoC, Program
or User
– Client-level, to find records with problems
• You can use built-in reports designed to check data
quality, or you can also use other everyday standard
reports or custom queries.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
5
Are Your Data Complete?
• Are All Clients Entered?
– Compare client counts to bed capacity or on-site check
– Compare records in recent period vs. previous periods
– Are there family shelter guests with only one client?
– Check against number of paper records, if applicable
• Are All the Required Fields Filled In?
– Check the completion rates of each required field. e.g.,
“Gender: 96% Complete”
– Client level, e.g., “List All Clients Where Race Is Null”
See Handouts 1 and 2 for sample reports
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
6
Special Note on Exit Dates
• Exit dates are critical for reporting:
– How many people are in the system;
– Demographics of people still in the system;
– Length of stay; and
– Program Outcomes and Effectiveness.
• Without exit dates, very few other data elements are
useful or reliable.
• All programs should have a clear process for recording
exit dates and for monitoring this field.
• Consider exiting out today any clients that are not in
program.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
7
What if Client Counts Don’t Match Expectations?
• If client count is too low:
– Not all clients entered?
– Many clients entered with the name “John Doe”?
• If client count is too high?
– Clients not exited?
– Records not properly de-duplicated?
– IDs not entered consistently?
• Or, maybe, expectations are wrong and data are right!
HMIS sometimes reveals unexpected information.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
8
Are Your Data Accurate?
• Are staff collecting true information and properly recording
responses?
– Check for valid data (e.g., no veterans are minors)
See Handout 3 for sample data validity checks
– Check for aggregate data in expected range for target
population:
• <5% of clients older than 75 years,
• <2% Native Hawaiian/Pacific Islander
– Compare random sample of paper files
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
9
Are Your Data Timely?
• Are data entered soon after collected?
– Compare dates of program entry to the date the record was
created
– Run reports on data from yesterday or two days ago.
• Are changing data kept up to date?
– Check for clients still in shelter with “Last Updated” dates
more than a month ago.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
10
Are Your Data Consistent?
• Does Everyone Understand the Questions and Answers in
the Same Way?
– Compare data from different users/programs serving the
same general population. e.g., 95% of Bob’s clients have a
disability; 60% of Mary’s clients have a disability.
– Take advantage of duplicate records. Spot check to see
whether you’re getting the same data (e.g. first name, race)
for the same client in different programs or service
episodes.
– Survey users and see if they record the same responses
with sample clients.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
11
Does Everyone Know What “Good” Is?
• Set Common Standards for your Community
• Your CoC and Program should have common
expectations for
– What gets collected and entered
– Who enters data
– Who and when the data are checked and how errors get
fixed
– When data get entered
– What happens to data after they are entered
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
12
Does Everyone Know What “Good” Is?
• Formalize Expectations in a Data Quality Plan
– A set of written policies that set common standards and
procedures for ensuring data quality
• A plan should include:
– Principles
– Benchmarks
– Monitoring Procedures
– Incentives
– Contractual Agreements/Buy-In




Completeness
Accuracy
Timeliness
Consistency
See Handouts 4 and 5 for a monitoring report
and associated DQ plan
and 6 for a worksheet on developing a plan
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
13
Playing Your Part to Enhance
Data Quality: Tips for Every Level
• Everyone Plays a Role in Enhancing Data Quality
– CoC-Level
• Technology
• Processes
– Program-Level
– User-Level
• Collection
• Entry
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
14
CoC Level:
HMIS Project Staff Roles
• Create and implement data quality plan
• Check data quality and provide feedback
• Provide training, support and documentation
• Hold regular user groups
• Convene data quality sub-committee
– This committee can spearhead plan implementation
• Release only good data and clarify limitations with every
aggregate data release
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
15
CoC Level:
Prevent User Errors with Your Technology
• Customize the software to minimize errors
– Screen Design:
• All required fields in logical flow
• Required fields marked, labels written clearly
• Use drop downs, not free text
– Validation:
Revisit Handout 3
• Rather than reporting on completeness and accuracy issues
after-the-fact, validations can occur during data entry.
• Tip: Allow “Don’t Know” options or use validate with warnings.
Systems that force data entry end up with “Joe Guest” and
“99999.”
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
16
CoC-Level: Training and Communication
Highlight strengths and pitfalls of software in trainings,
user groups and other communications. For example:
• “Take Advantage of These
• “Watch Out!”
Features!”
• This “intake date” is not the
• To clear up confusion about
question, click…
• Module for viewing
incomplete record, click…
• To view data quality report:
go under reports…
• The following information is
validated….
same as this “service begin
date”.
• Last name is listed first
here, but last here.
• In order to answer all the
required fields…
• This report shows missing
fields, but not fields that are
outdated.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
17
Program Level: Data Flow Process
• Create Program-Level Processes for:
– Flow of data collection and data entry
• Whose job is HMIS anyway?
– Is there one “HMIS person” or is everyone responsible for
entering their own clients?
– Must all data be on paper? Is paper allowed?
• Many providers lack clear processes for entering exit data or
updating information
• If data are updated on paper form, how will the data entry staff
spot the new information?
• If an issue is found by data entry staff, what is the protocol for
getting resolution to their question?
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
18
Program Level: Monitoring & Using Data
• Regularly Monitor Data Quality
– Review quality assurance and standard reports
– Provide feedback to staff and facilitate conversation
between those collecting and entering data
– Tip: Tie data collection/entry to job duties and performance
and allow necessary time for training
• Integrate Use of HMIS Into Daily Operations, Including
Use of the Data
– Tip: When staff knows directors are relying on HMIS data to
report to the board or to funders, quality is bound to be
high.
See Handout 7 for sample program communication
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
19
User Level: Data Collection
• Intake or front line staff are often the first point of data
collection for clients in need of service
• Intake or front line staff need to understand and be able to
communicate to every client served why information is
being captured and how the information will be used
including:
– Purpose of data collection;
– Importance of HMIS at the local level; and
– Privacy policies and consent protocols.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
20
User Level: Data Collection
Paper
– Feels more natural; may be
less intimidating; may be
similar to “old” process
– Tip: Use a data collection
form that resembles the
computer screen; use block
lettering; have response
values, not free text on
form.
Vs.
Computer
– Timely; Avoids extra
time/persons required for
data entry; can use scan
cards for large shelter
registration
– Tip: Allow clients to sit and
view the screen while data
is entered; print out a report
for the client to have.
See Handout 8 for sample paper form
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
21
User Level: Data Entry
• Data Entry Staff, including volunteers must be trained to:
– Search the HMIS for an existing client record by all
methods, if applicable
– Enter all the information provided
– Enter accurate information
• Proofread for common errors:
– Accidentally picking the wrong response category
– Typing Data in the wrong field
– Misspellings
– Tip: False data are usually worse than no data; e.g. “Baby
Boy” instead of first name.
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
22
User Level: Data Entry
• Data entry staff should also:
– Communicate regularly with front-line staff
• Clarify shorthand (What does “SA” mean?)
– Not independently change or ignore suspicious data
• Record issues in a data quality log
See Handout 9 for sample data quality log
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
23
User Level: Understand Required Data Elements
• Front-line and data entry staff should understand:
– What needs to be collected and definitions of each
– When to collect each element
– Where to enter each element
– Tips to ensure each element is entered properly. E.g.,
• If you only have the last 4 digits of SSN, enter 5 spaces first
and mark quality code as “Partial SSN Recorded”
• Use “01/01” for Month/Day if only year of Birth is known
• “Last Permanent Address” refers to the last place the client
lived for 90 days or more
See Handout 10 for notes and tips on each
universal data element
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
24
Feeling Overwhelmed?
• There are many ways to check data quality.
– Start with data completeness checks
• Key issues to address first:
– All Clients Entered
– Exit Dates! Exit Dates! Exit Dates!
MANTRA:
Enter
and Exit
• Use this training, the handouts and the Data Quality white
paper to implement a more comprehensive data quality
plan as you feel comfortable with these first steps.
• For more information, see “Enhancing HMIS Data Quality”
available at
• http://www.hmis.info/ta_resources_data.asp?topic_id=9
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
25
Summary of Key Points on Data Quality
• Data should be accurate, complete, consistent and timely
• All members of the CoC play a role in promoting data
quality
• A Data Quality plan sets standards and procedures to
ensure data quality
• Built-in data quality reports operationalize the goals in the
data quality plan
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
26
Additional Resources
• “Enhancing HMIS Data Quality”:
http://hmis.info/documents/Enhancing%20HMIS%20Data%20Quality%20Final.pdf
• Additional resources are available at:
www.hmis.info
Click on “Resource Library” to search for documents
Prepared by Center for Social Policy, UMass Boston and Abt Associates for the U.S. Department of
Housing and Urban Development
27