Data-Driven Compassion: Combining Human Judgment and Data Analytics for Better CoC Outcomes

Numbers tell compelling stories, but they rarely tell the whole story. 

While many Continuums of Care (CoCs) rely on sophisticated data systems and reports, the most successful communities have discovered that blending quantitative insights with human judgment leads to better outcomes. Data points, even those that have been carefully collected and maintained for accuracy, paint a partial picture—one that’s open to interpretation. And it’s up to your community to interpret it with the care and inclusivity it deserves.

This article explores how to combine the precision of data analytics with the invaluable perspectives of service providers and people with lived experience of homelessness, ensuring the best possible outcomes for the folks in your CoC.

Thank you for joining us!

The Role of Human Judgment in CoCs

Human judgment brings nuance and context to homeless services that data alone cannot capture. This qualitative insight helps CoCs understand the “why” behind their numbers and the “how” of improving their systems.

Incorporating Lived Experience

CoCs increasingly recognize the importance of including people with lived experience in their decision-making processes.

One of our outstanding partner CoCs, the Southern Nevada Homelessness CoC, has transformed its governance structure to ensure that every meeting—from CoC Board sessions to HMIS Steering Committee discussions—includes participants with lived experience of homelessness. This integration ensures that policies and programs are shaped by those who understand the challenges firsthand.

These CoCs have acknowledged the “human contributions” of those with lived experience by:

• Offering gift cards for participation in meetings and focus groups
• Facilitating community clean-up programs that provide both compensation and engagement
• Creating paid consulting positions for individuals with lived experience 

As you work to draw diverse voices into the room, be sure to openly give credit for every contribution—your CoC’s strategy will be stronger for it.

Local Context and Community Expertise

Human judgment is particularly important in understanding local nuances that national data standards might miss. Service providers who have worked within their communities for years know the ins and outs of:

• Local challenges and resources
• Cultural considerations that impact service delivery
• Historical context that shapes current challenges
• Informal support networks and community assets

This ground-level understanding helps CoCs interpret their data within the appropriate context and design more effective interventions.

For example, while quantitative data might show low utilization of a particular service, local providers might understand that transportation barriers or cultural factors are the root cause — insights that wouldn't be apparent from the numbers alone.

The Power of Data Analytics in Prioritizing Resources

Today's CoCs use analytics tools to transform raw data into actionable insights. These tools help communities better understand and serve their populations.

Identifying and Supporting Specific Populations

Communities use multiple data sources to get a comprehensive view of community needs.

Sources like Longitudinal Systems Analysis (LSA), Point-in-Time (PIT) count, and System Performance Measures (SPMs) reveal how veterans, youth, families, and seniors interact with homeless services.

Through careful analysis of these sources, CoCs can evaluate how different interventions work for specific populations, spot gaps in their service offerings, and develop programs to address the distinct needs of each group. This detailed understanding helps ensure resources are directed where they can have the greatest impact.

Predictive Analytics for Proactive Solutions

Many data tools now offer predictive analysis to help communities proactively support their clients. For example, CoCs can use predictive analytics to:

• Identify clients at the highest risk of returning to homelessness
• Forecast seasonal shelter capacity needs
• Project which housing programs might work best for different populations
• Anticipate areas where homelessness might increase due to economic indicators.

Data-Driven Decision-Making in Action

To get a better idea of how CoCs can leverage their data to improve client outcomes, let’s take a look at two hypothetical scenarios.

Scenario 1: Addressing Youth Homelessness. Imagine a CoC discovers through LSA data that youth aged 18 to 24 are returning to homelessness at higher rates than other groups. Further analysis of their data shows that while youth are getting housed, many lose housing within six months.

If SPM employment data points to youth not being able to get a job without a permanent address, this combined insight could lead to developing an integrated housing and employment program for young adults.

Scenario 2: Prevention Through Pattern Recognition. Consider a CoC that uses their data to identify a pattern: families experiencing homelessness often come from the same three zip codes, and many faced eviction in the previous year.

Using this insight, they could partner with local eviction courts to create an early intervention program, connecting at-risk families with resources before they lose housing.

Finding Balance: Integrating Data and Human Insights

Success in addressing homelessness must merge the benefits of both human judgment and data analytics. But how?

Communities that excel at this balanced approach often implement multiple strategies for gathering and using both types of information, such as:

• Incorporating qualitative feedback into HMIS data collection
• Engaging people with lived experience in data interpretation
• Using human insights to explain statistical anomalies
• Adding context to numerical trends through provider feedback
• And more

The Role of Technology

While technology drives data collection, it can also support the integration of human insights. Modern HMIS platforms like Clarity Human Services are built to serve as holistic tools to understand homelessness by:

• Adding qualitative data fields to existing databases
• Creating systems for regular feedback collection
• Developing more nuanced measurement tools that capture both numbers and narratives
• And more

If you want to go beyond seeing your HMIS as a one-dimensional tool, start by collecting discrete qualitative data to enter into your HMIS. For example, begin capturing feedback on cultural or language barriers encountered or comfort level with different service locations.

As you gather qualitative data like this, your HMIS will quickly become what it was always meant to be—an active, care-empowering partner in the fight against homelessness.

Reducing Biases Through Data Analytics

Data analytics can help identify where bias may exist in homeless response systems. While data alone cannot eliminate bias, it provides evidence to guide system improvements.

Tools That Highlight Biases and Differences

By carefully examining patterns, CoCs can identify disparate outcomes across demographic groups and understand who is—and isn't—receiving available services. Your community can also monitor how different populations move through the system and evaluate whether interventions work equally well for all groups.

This systematic approach to tracking outcomes highlights areas where services need to be adjusted to serve everyone in the community better.

For example, the CoC Analysis Tool: Race and Ethnicity from HUD Exchange shows how data can drive equity. The tool uses PIT with American Community Survey (ACS) data to help communities analyze racial disparities in the homeless population.

The Stella Performance Module (Stella P) turns LSA data into visualizations. This visual representation helps communities understand if their homeless system serves all populations equitably. Through various filters, the visualizations show:

• Demographic breakdowns of who uses the system
• How long different groups remain homeless
• The exit rates of different populations
• The likelihood of different populations returning to homelessness

By comparing these measures across different racial and ethnic groups, communities can spot potential inequities in their system and take steps to address them.

Uncovering Local Biases Through Analysis

Many CoCs have run equity analyses to uncover biases in their processes. The Montgomery County, PA CoC discovered BIPOC clients consistently scored lower on their VI-SPDAT Coordinated Entry assessment. They dug into the “why” behind this difference, and they soon learned that it wasn’t the assessment itself but rather the assessment process that created barriers: the questions asked, who asked them, and how they were posed all contributed to scoring disparities.

This discovery led to restructuring their call center operations and revising their assessment procedures. (Side note, if you’re thinking about creating a custom assessment for your CoC, check out our guidance on making it effective and equitable!)

While data alone can’t identify all forms of bias, it serves as a promising starting point for further investigation.

Tips for Leveraging Data and Human Judgment

We've covered the power of data analytics, the importance of human insight, and ways to reduce bias in decision-making. Let's combine the key strategies we've discussed into actionable tips for CoCs looking to strengthen their approach to integrating data and human judgment.

Tip 1: Start with Simple, Consistent Data Collection

Begin by collecting discrete qualitative data points in your HMIS that provide context to your numbers. Gather exit interview feedback about program experiences and document housing journey details, including neighborhood preferences and barriers.

When tracking service access challenges and successes, pay attention to cultural and language considerations that affect outcomes.

Tip 2: Create Regular Feedback Loops

Build methods to gather insights across your community. Frontline staff can spot emerging trends. Program participants moving through different services offer valuable perspectives, and community partners often see broader patterns that impact housing stability.

Tip 3: Make Data Accessible and Meaningful

Clear visualizations of data help staff and other stakeholders better analyze hard numbers. To make the data even more relevant, share success stories alongside statistics that connect numbers to real-world impacts.

Tip 4: Invest in Both Types of Knowledge

Create time for both quantitative and qualitative analysis. For example, training staff on data interpretation and compensating people with lived experience for their expertise. Use a built-for-humans HMIS like Clarity Human Services to capture both types of information to paint a holistic picture of your community's needs and successes.

Integrating Human Insights with Data Analytics: Ready to Get Started?

Effective homeless response systems rely on a combination of rigorous data analysis and deep human understanding.

While federal reports and data analytics provide the foundation for measuring outcomes, it's the integration of human insight—from service providers, people with lived experience, and community partners—that transforms these numbers into meaningful action.

The most successful CoCs have discovered that this balanced approach leads to more equitable service delivery, better-targeted interventions, and stronger program outcomes.

Ready to strengthen your community's approach to data and human insight? The Bitfocus team can help you build systems that capture both the numbers and the narratives that drive positive change.

Give us a shout to learn more, or check out Clarity HMIS today.