Welcome back to the Mind Your Data Series, the training series focused on improving data quality one element at a time. | | |
As part of LAHSA’s ongoing commitment to strengthening data quality across the Continuum of Care, this edition of Mind Your Data focuses on Universal Data Elements (UDEs) - the foundational data points collected for every participant enrolled in HMIS.
These elements play a critical role in ensuring consistent, accurate, and reliable data across agencies, programs, and reporting efforts. High-quality UDE data supports everything from client identification and coordinated care to federal reporting and system-wide analysis. When Universal Data Elements are entered completely and accurately, they help create a stronger, more trustworthy HMIS for providers, stakeholders, and the communities we serve.
Think of UDEs as the foundation of your data. If these are incomplete or inconsistent, everything built on top of them is impacted.
*UDE's are defined by the HUD HMIS Data Manual.
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- Name
- Social Security Number
- Date of Birth
- Race and Ethnicity
- Veteran Status
- Disabling Condition
- Project Start Date
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- Project Exit Date
- Destination
- Relationship to Head of Household
- Enrollment CoC
- Housing Move-in Date
- Prior Living Situation
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Strong UDE data allows LAHSA and providers to:
- Understand who is being served
- Measure system performance
- Reduce duplicate client records
- Ensure accurate and equitable outcomes to inform funding decisions
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When UDEs are missing or incorrect, it can:
- Lower your Data Quality Score
- Impact federal reporting (APR, LSA, PIT/HIC alignment)
- Create gaps in the client story
- Lead to duplicates in client records and services
| | Common UDE Data Quality Issues | | |
Across HMIS, we often see:
- Missing required fields at project entry
- Inconsistent or incorrect response selections
- Data not updated when new information becomes available
- Use of non-standard placeholders or free text
Because UDEs are required for every client, even small gaps can quickly become system-wide issues.
| | UDE Spotlight: Social Security Number (SSN) | | |
Why SSN Is a Critical UDE: The Social Security Number (SSN) plays a key role in:
- Deduplicating client records
- Supporting accurate reporting and data matching
- Strengthening data integrity across the system
However, SSN is also a highly sensitive data element, requiring careful handling to protect client privacy.
Standard Approach to SSN Data Entry: To balance data quality and privacy, SSN must be entered using a consistent and standardized method.
When Full SSN is Available:
- Enter the full, accurate SSN
- Double-check for accuracy before saving
- In the Quality of SSN field, select “Full SSN Reported”
When Partial SSN is Available:
- Enter the partial SSN, using “X” for any unknown numbers (e.g. XXXX-XX-1234)
- Do not enter 0s, 9s, or any other placeholder values for unknown numbers
- In the Quality of SSN field, select “Approximate or partial SSN reported”
When SSN is NOT Available:
- Enter: XXX-XX-XXXX
- Do not enter 0s, 9s, or any other placeholder values
- In the Quality of SSN field, select the appropriate option of either “Client doesn’t know” or “Client prefers not to answer”
This approach ensures:
- Consistency across all providers
- Clean and usable reporting
- Correct and reliable identifiers to allow proper identification and matching
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Work-Authorized SSNs: A Working Social Security Number (SSN) refers to a valid, complete SSN that a client uses for employment and income purposes.
- There is no difference in how SSNs are treated in HMIS
- Any valid SSN should be entered as a complete SSN
The focus is on accuracy and consistency, not categorization.
What to Avoid:
- Leaving SSN fields blank
- Using inconsistent placeholders (e.g., 000-00-0000, 999-99-9999)
- Selecting incorrect response options for quality fields
These practices can:
- Create duplicate client records
- Trigger data quality issues
- Reduce overall data reliability
Quick Check:
- Are all required UDE fields completed for every client?
- Are standardized responses being used consistently?
- Do all clients have either a valid SSN or the correct placeholder?
- Are updates being made when participant information changes or is received after the initial entry?
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Best Practices for All UDE's:
- Standardize your approach: Always follow HMIS data entry guidelines.
- Ask clearly and respectfully: Use consistent language when collecting sensitive information from participants.
- Enter data in real time: Avoid delays that lead to missing or inaccurate records.
- Update when new information is available: Data quality is ongoing, not one-time.
- Train your team regularly: Ensure all staff understand both what to collect and how to enter it correctly.
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FINAL THOUGHT: UDEs are the foundation of every report, dashboard, and decision. When entered correctly, they:
- Strengthen the integrity of the entire system
- Protect client identity
- Ensure every client is accurately represented
| | Mind your data now, because strong foundations lead to stronger outcomes. | |
Join our weekly HMIS office hours:
Every Thursday from 2 PM - 3 PM
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| Data, Explained: LAHSA's Data Quality Monitoring Plan | | |
LAHSA’s Data Quality Monitoring Plan (DQMP) ensures HMIS data is accurate, reliable, and actionable so providers and policymakers can understand needs, track progress, and improve outcomes. Check out our website to take a deeper dive into LAHSA's data quality framework:
| | LAHSA is a joint powers authority of the City and County of Los Angeles, created in 1993 to address the problem of homelessness in Los Angeles County. LAHSA is the lead agency in the HUD-funded Los Angeles Continuum of Care, and coordinates and manages federal, state, county, and city funds for programs providing shelter, housing, and services to people experiencing homelessness. | | | | |
