If your organization stopped collecting a particular data point tomorrow, who would notice, and what decision would become harder to make?
Over time, “we’ve always collected this” starts to function like a justification, even when nobody can explain what decision it informs, or whether anyone would notice if it disappeared. A question can remain on a survey for years. A reporting template still asks the same metrics long after a program’s activities have changed. Staff may spend hours entering information into a database simply because the organization has built a routine around collecting it. Eventually, the process itself can make the data feel necessary.
For staff responsible for overseeing data collection, questioning those routines feels risky. You may inherit reporting systems that existed long before you joined the organization, manage requirements established by funders or leadership, or oversee information that several departments use differently. Still, regularly asking why your organization collects something is important. Data should have an identifiable purpose, whether that purpose involves learning, accountability, decision-making, or some combination of the three.
Why Are We Collecting It?
Every data point should have a job, and the intended user and purpose should shape what gets collected.
Some information helps an organization understand whether participants are benefiting from a program. Other measures allow a funder to understand how grantees are using funding. Board members may need certain indicators to fulfill governance responsibilities, while program teams may need entirely different information to adjust implementation.
That sounds straightforward, yet data collection can easily become disconnected from its original purpose. Is your funder requesting a metric from several grant cycles ago, and staff continued tracking it after the requirement ended? Or maybe you created a survey question to answer a specific programmatic concern, yet the concern was resolved and the question was never removed. Did leadership ask for a dashboard filled with indicators without identifying which decisions those indicators will support?
Periodically return to a few basic questions: Why do we need this information? Who uses it? What are they using it for? The answers may confirm that the metric still matters and also reveal that your organization has been spending staff time collecting information with no clear audience or use.
Learning and accountability also require different kinds of attention. Accountability data demonstrates that an organization fulfilled a commitment, such as serving a particular number of people or completing specific activities. Learning data helps staff understand why results differed across locations, what participants experienced, which strategies worked well, or where an approach needs adjustment. Neither purpose automatically makes a measure more valuable. However, knowing the purpose prevents staff from expecting one type of data to answer questions it cannot answer.
Has Our Organization Created the Necessary Conditions for Using the Data?
Clear purpose alone does not guarantee useful data. Organizations also need conditions that allow people to review, interpret, discuss, and act on what they collect.
Consider time. Staff can spend weeks compiling quarterly data and then move immediately into the next reporting cycle without ever discussing what the findings mean. A beautifully designed data dashboard provides little value if time isn’t set aside for staff to examine trends or ask why something changed. Collecting participant feedback does not support learning if the people designing and delivering the program never see it.
Ownership matters too. Someone may oversee data collection without having responsibility for facilitating conversations about what the data means. Program staff may assume the evaluation team will interpret it. Leadership may expect program directors to identify implications. Development staff may use the same information for funder reports while remaining disconnected from internal learning conversations. When everyone touches the data but no one owns the process of turning it into insight, action can stall.
Access creates another challenge. Staff need information in forms they can understand and use. A complicated database or lengthy report may technically make the data available while creating practical barriers to engagement. The people closest to the work should not need specialized statistical training to understand basic findings about their programs. Depending on the audience, a short summary, recurring staff discussion, simple visualization, or focused set of indicators may support better use than a comprehensive report.
Decision-making authority also affects whether learning leads anywhere. Staff can identify patterns and recommend changes, but those insights will not improve a program if they lack the authority or support to act. Conversely, leadership can ask teams to become more data driven while unintentionally discouraging honest interpretation when disappointing results trigger blame. A learning culture requires room to say, “This didn’t work the way we expected,” and then investigate why.
Feedback loops matter for accountability as well. If a foundation requires grantees to submit extensive reports, what happens to that information afterward? Do program officers review trends across grants? Does the foundation share what it learns with grantees? Do reporting requirements change as strategies evolve? Accountability loses much of its usefulness when information travels in one direction and disappears into a filing system.
For anyone overseeing data collection, these organizational conditions deserve as much attention as the metrics themselves. A stronger survey or cleaner dashboard cannot compensate for a culture that never creates time, ownership, access, or authority for using what the data reveals.
What Can We Stop Collecting?
Organizations often respond to uncertainty by adding another question, indicator, spreadsheet column, or reporting requirement. Over time, the accumulation becomes overwhelming. Staff may collect dozens of measures while regularly discussing only a handful of them.
More data can actually make decision-making harder. When everything receives equal attention, staff have to sort through more information to identify what matters. Excessive reporting can also consume time that could otherwise support analysis, reflection, relationship-building, or program improvement. For grantees, duplicative reporting requirements can divert staff capacity away from the work that funders want to support.
Collecting less data doesn’t mean lowering expectations for evidence. It means becoming more deliberate about what deserves staff time and attention.
Start by examining the data your organization rarely uses. Ask whether anyone references it after collection. Look at old reports and identify which measures consistently inform conversations and which ones simply appear because the template includes them. Review surveys for questions that no longer connect to current program goals. If your organization manages grant reporting, consider whether every requested metric helps your team understand performance, fulfill an accountability need, or make a decision.
Then determine what to keep, revise, combine, or retire. Some measures may still serve a legitimate purpose but require less frequent collection. Others may need clearer definitions or better analysis. A metric that provides little value on its own might become useful when paired with another source of information. Finally, some data points may simply need to go.
Removing a metric can feel uncomfortable, particularly when your organization has collected it for years. However, history alone does not make information useful. If nobody can identify who needs the data, what question it answers, or what decision depends on it, continuing to collect it deserves scrutiny.
Reducing unnecessary collection can also improve the quality of the data you keep. Staff have more capacity to maintain accurate records, examine trends, investigate unexpected findings, and talk with colleagues about what the information means. Instead of managing an ever-growing inventory of metrics, your organization can concentrate on the data that supports its priorities.
Key Takeaway
Data collection should connect to a clear purpose and a real audience. Whether information supports learning, accountability, or both, organizations need systems that allow people to use it. That means creating time to review findings, clarifying ownership, making information accessible, supporting staff in acting on what they learn, and closing the feedback loop with the people who provide or use the data.
Question the data system itself. You don’t have to preserve every metric simply because it appears on an old spreadsheet or reporting template. Periodically examining what your organization collects can uncover information that no longer serves a meaningful purpose and create space to focus more deeply on what does.
Better decision-making requires fewer measures, clearer questions, and greater intention about what happens after the data arrives.
Raise Your Voice: What’s one piece of data your organization collects that you would question if you were designing the process today? Share your thoughts in the comments section below.
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