What is by-name data management? 

By-name data management includes the practices, protocol, and policies a community adopts to give confidence that the data they’re producing reflects the reality of people experiencing homelessness in their region. Some fundamental principles of data management include developing and socializing written protocols that identify roles and responsibilities to ensure data is accurate, timely, and complete. 

By-name data management plays a crucial role in improving services and understanding the needs of people experiencing homelessness. It is more than just collecting numbers — it’s about ensuring that those numbers help us understand and respond better to the challenges people face, and ultimately, help communities design systems that are more effective and compassionate.

Why does by-name data management matter?

Effective by-name data management builds trust within the community by ensuring transparency in how data is collected and used. Documenting processes and protocols helps providers, decision-makers, funders, and the public have confidence in the data’s integrity. This transparency also ensures resources are allocated effectively, improving outcomes for those experiencing homelessness. A well-managed dataset helps communities identify areas for improvement and highlight successes. Continuous monitoring allows for real-time adjustments to better meet the needs of those served.

By-name data management directly impacts the lives of people experiencing homelessness. Strong data practices help track outcomes, identify trends, and ensure services reach those in need. When services are tailored to the real needs of a community and informed by well-managed data rather than assumptions, people experiencing homelessness are likely to have a greater trust in the system.

Who is responsible for data management? 

Data management is a community-wide effort that depends on an ongoing feedback loop. Its success relies on how data is collected, tracked, and analyzed. For these processes to work well, it’s important that service providers, front-line staff, and people with lived expertise are involved in designing the data systems.

Complex systems can be hard to manage, so it’s crucial to have a clear and easy-to-use data entry process to maintain the integrity of the data. While detailed protocols and reporting capabilities are necessary, their effectiveness depends greatly on how clearly their importance and expectations around use are communicated to providers.

The best data management practices include a feedback loop where service providers, front-line staff, people with lived expertise, leadership, governing boards, and others work together to create and document the practices. These expectations are shared, training is provided, and workflows are regularly monitored. Over time, practices are adjusted as needed. In the ever-changing world of homeless services, data management practices are bound to shift. That’s why the process of developing, training, monitoring, and refining needs to be continuous — and it works best when the community is involved.

Examples of by-name data management practices

Defining by-name data for your community: With a large number of people entering data and interacting with the community’s data, it is important to have a shared understanding of what the by-name dataset is and what data is included. 

Managing duplicate profiles: For effective collaboration, each person in your by-name dataset should have a Unique ID (like an HMIS ID) that is assigned only to them. Regularly auditing for and merging duplicate profiles ensures that the dataset accurately represents unique individuals. 

Closing outdated enrollments: When a program enrollment is left open long after a person is no longer receiving services, it can misrepresent who is experiencing homelessness in your system and inflate actively homeless numbers. Oversight is needed so these enrollments don’t linger in the system, creating inaccuracies that compromise the overall dataset. 

Monitoring consistent data entry: Sometimes an enrollment is open for an individual, but the data entry to record interactions with that person is not happening. In these cases, a lack of recent activity visible on a client profile could result in that person falling off the active list. Regularly monitoring the active list to ensure that data entry is up to date is essential to ensure data represents the reality of people experiencing homelessness.

Monitoring inactive records: Related to the above example, it is also necessary to monitor records in accordance with your communities inactive policy to ensure that individuals who are no longer active in your system are removed from the active list. Learn more about tracking inactivity here.

Documenting by-name data management practices can look different for every community. See the tools and resources section for ideas and more information.