On the road to becoming data-driven, collecting useful data starts with understanding why you need it.

Some data helps organizations demonstrate accountability to funders, boards, leadership, participants, and other stakeholders. Other data supports learning by helping staff understand what’s working, where challenges are emerging, and how programs might improve.

Often, the same data can contribute to both purposes. The important part is determine the role the data is playing.

Understanding the purpose of a metric is a first step. Programs staff may need to consider what the information actually means and what conclusions it can reasonably support. Attendance may tell you how many people showed up yet offers limited information about what participants learned or experienced. Likewise, collecting meaningful information has limited value if no one uses it to make decisions.

The Purpose-Meaning-Action Data Framework brings these considerations together so your team can examine whether the data you’re collecting is helping you understand and strengthen your work.

Try this exercise with your staff and let me know if it’s useful for you.

Objective:

This exercise is designed to help teams examine the purpose, meaning, and potential action behind the data they routinely collect.

This Activity Is For:

• Nonprofit program, evaluation, communications, development, and leadership staff who collect, review, or use organizational data
• Philanthropy staff who develop reporting requirements, review grantee data, or work directly with funded organizations
• Data and evaluation staff who want to engage colleagues in conversations about how information supports learning and decision-making

What You’ll Need:

Plan for approximately 50 to 60 minutes for this exercise. If your organization collects a significant amount of data, focus on one program, reporting process, survey, dashboard, or other manageable set of measures rather than trying to review everything at once.

For an in-person session, you’ll need:

• A current report, survey, data dashboard, tracking form, or list of metrics your team regularly collects
• Sticky notes
• Pens or markers
• Three sheets of flip-chart paper labeled Purpose, Meaning, and Action
• A timer

For a virtual session, you’ll need:

• A digital copy of the data collection tool or report you’re reviewing
• A shared virtual whiteboard, spreadsheet, or collaborative document
• Three sections labeled Purpose, Meaning, and Action
• A timer

Before the session begins, select approximately three to five data points for the group to review. Choosing a smaller number allows the team to examine each one closely instead of rushing through a long list.

The Steps

Step 1: Choose the Data You Want to Examine | 5 Minutes

Start by reviewing the data points your group selected. Make sure everyone understands what each measure represents and where the information comes from. Example can include participant attendance, referrals, survey responses, activities performed or services delivered, community partnerships, program completion rates, or another measure your organization routinely tracks.

Next, ask participants to work from the same definition of each metric. Clarifying this at the beginning can surface differences in how staff interpret seemingly straightforward measures. If one person defines “participant engagement” differently from another, for instance, the team needs to resolve that before discussing what the data means.

Step 2: PURPOSE | 10 Minutes

For each data point, begin with Purpose: Why are we collecting this?

Ask the group to consider:

• Why did we begin collecting this information?
• Who requested or needs this data?
• Who currently uses it?
• Does it primarily support learning, accountability, or both?
• What question were we hoping this data would answer?
• Is that question still relevant to our work?
• Would anything change if we stopped collecting this information?

Encourage participants to distinguish between information collected because someone genuinely uses it and information collected because it has simply become part of the routine.

Some measures may exist primarily for accountability, and that doesn’t make them unnecessary. A funder may need participation numbers to confirm that a program reached the population described in a grant agreement. Leadership may need financial or service-delivery data to oversee organizational performance. However, naming that purpose helps staff avoid expecting an accountability metric to answer questions it was never designed to address.

At the end of this step, label each measure Learning, Accountability, or Both.

Step 3: MEANING | 10 to 15 Minutes

Now, move from why you collect the data to Meaning: What does this information actually tell us?

For each metric, discuss:

• What can we confidently say based on this information?
• What can we not conclude from it?
• Does this measure tell us about activity, reach, experience, quality, change, or something else?
• What context would help us interpret the number or finding?
• Are we assigning more meaning to this data than it can support?
• What additional information would help us understand what is happening?

Suppose a program tracks workshop attendance. Attendance can tell staff how many people participated and, when examined over time, whether participation has increased or decreased. Those numbers could also help staff compare turnout across locations, topics, or recruitment strategies.

However, attendance alone cannot explain why people participated, what they learned, whether they found the workshop useful, or whether they did anything differently afterward.

That distinction matters. Instead of labeling a number “good” or “bad” immediately, teams can first identify what the measure actually represents. Doing so reduces the risk of confusing activity with impact or assuming that larger numbers automatically indicate stronger results.

Step 4: ACTION | 15 Minutes

Once you’ve identified the purpose and meaning of the data, ask Action: What will we do with what we know?

Discuss:

• What decisions can this information inform?
• Who needs to see or discuss these findings?
• What deserves further investigation?
• Does the data suggest a program adjustment?
• Should we continue collecting this measure as it currently exists?
• Could we change how we collect it to make it more useful?
• Do we need another source of information to understand the issue?
• When will we review this data again?

This portion of the exercise shifts the conversation from measurement to use. Perhaps a decline in participation leads staff to examine outreach strategies. Participant feedback might prompt a change to program delivery. Consistent results could confirm that a particular approach should continue. In other cases, the group may decide that a metric serves no clear purpose and recommend retiring it.

Not every finding needs to trigger an immediate program change. Sometimes the appropriate action is to keep watching a trend, gather additional information, or bring a question to participants or community partners. The goal is to identify an intentional response rather than collecting information that disappears into a report.

Step 5: Decide What to Keep, Change, Stop, or Add | 10 Minutes

Finish the exercise by reviewing what surfaced across all three parts of the framework. For each data point, choose one of four next steps:

• Keep: The data has a clear purpose, provides useful information, and supports an identifiable action or decision.
• Change: The measure still matters, but the way you collect, define, analyze, or use it needs improvement.
• Stop: The team cannot identify a meaningful purpose or use for continuing to collect it.
• Add: The existing data leaves an important question unanswered, and the team needs another source of information.

Be cautious with the Add category. Discovering a gap doesn’t automatically mean your organization needs another survey question, reporting requirement, or spreadsheet column. First consider whether the information already exists somewhere else or whether staff can answer the question through conversations, observations, existing records, or another source.

The goal of the framework is better data use, not more data collection.

Let’s Process

After completing the exercise, give the group time to reflect on what they noticed. Teams may discover that some long-standing measures have a clear accountability purpose but provide little information for program learning. Other metrics may generate useful findings that rarely make their way into planning conversations. You may also uncover data points that staff have collected for years even though no one can identify who uses them.

Pay attention to disagreements, too. If one department sees a metric as essential while another sees little value in it, explore why. Development staff, program staff, evaluators, senior leadership, and funders may interact with the same information differently. Those perspectives can help the organization clarify what needs to be collected, how findings should be communicated, and where opportunities for learning may have been overlooked.

Before ending the session, document the team’s Keep, Change, Stop, and Add decisions. Assign responsibility for any next steps and decide when the group will revisit them. If you identified a reporting requirement that your organization cannot change, consider whether you can supplement it with information that better supports staff learning. Likewise, if you identified valuable learning data that currently goes unused, determine where it could become part of regular program meetings, planning conversations, or decision-making processes.

Key Takeaway

The Purpose-Meaning-Action Data Framework gives teams a structured way to examine whether their data collection practices still serve them.

Purpose clarifies why the information exists and who needs it. Meaning helps staff understand what the data can and cannot tell them. Action connects those findings to decisions, questions, and improvements.

During the exercise, you may discover that some measures work exactly as intended, while others need refinement or no longer justify the time required to collect them. Either result provides useful information. From there, the goal is to follow through on what your team identified by adjusting data collection practices, creating opportunities to discuss findings, and making data use a regular part of how your organization learns.

Raise Your Voice: Which part of the Purpose-Meaning-Action Data Framework would reveal the most about your organization’s current data practices? Share below in the comments section.

Was this useful? Subscribe to the Raise Your Voice newsletter, and explore my consulting services.