Organizations encounter data in many forms.
Research findings, program participant feedback, interviews, community stories, website analytics, service records, donor information, observations from frontline staff, and conversations with partners can all help an organization understand what is happening and what deserves its attention.
Because data takes so many forms, no single person or department can hold responsibility for everything an organization learns. Program staff gather information directly from participants. Researchers or evaluators examine patterns and help make sense of the findings. Development staff use that information to answer funder questions or strengthen a funding proposal. Communications staff translate a finding into an infographic, social media post, annual report, or story. Leadership rely on the same information to make decisions about strategy, staffing, partnerships, or resources.
Each person interacts with the data differently, and those differences matter. The strength of your organization’s data practice depends on how well people connect these roles.
Data Doesn’t Travel in a Straight Line
Organizations sometimes treat data like a relay race. One team collects it, another team analyzes it, and someone else receives the findings and shares them with the world. That sequence sounds efficient, but it leaves out many of the conversations that make data useful.
Consider a program team preparing to collect information from participants. Development staff may already know that several funders have started asking questions about a particular outcome. Communications staff may regularly hear questions from community members about who the organization serves or what happens after someone participates in a program. Leadership may need information to decide whether the organization should expand an initiative. Those questions can help program staff determine what information will actually prove useful before they create another survey, intake form, or tracking system.
The conversation also needs to move in the other direction. Frontline staff often notice things that a spreadsheet can’t explain. They may hear the same concern from several participants, observe an unexpected barrier, or recognize that people interpret a survey question differently than the organization intended. When staff have a way to share those observations, researchers, evaluators, and organizational leaders gain context that helps them interpret the numbers more accurately. Data works best as a conversation, not as a package that one department drops on another department’s desk.
Strong Hand-Offs Start Before Anyone Collects the Data
A good hand-off begins long before someone sends a report or shares a spreadsheet. Teams need to understand why the organization wants particular information in the first place.
For example, imagine that your program staff asks participants to complete a lengthy survey every six months. Program staff diligently distribute it, participants take the time to answer it, and someone eventually enters the responses into a database. Yet nobody has talked recently about which questions the organization still needs, who uses the responses, or what decisions the survey informs.
Now imagine that development staff need evidence about an outcome that the survey never addresses. Meanwhile, communications staff want to answer a recurring question from community members, but the organization doesn’t collect the information they need. Staff may spend significant time gathering data while the organization still struggles to answer some of its most important questions.
Collaboration at the beginning can prevent that disconnect. Program staff can explain what they can realistically collect without burdening participants. Development staff can share the questions they hear from funders. Communications staff can identify what audiences want to understand. Researchers and evaluators can help determine what information can answer those questions and which methods make sense. Leadership can connect those conversations to organizational priorities.
Nobody needs to dictate another team’s work. Instead, everyone contributes information that helps the organization make thoughtful choices about what it collects and why.
Analysis Needs Context, Too
Once an organization has collected information, analysis can reveal patterns, trends, differences, and unexpected findings. Numbers rarely explain themselves, however, and qualitative information also requires thoughtful interpretation.
Suppose participation in a program suddenly drops. The data shows the decline, but program staff may know that the organization changed its schedule at the same time. Perhaps transportation became harder for participants. Maybe a community partner stopped providing referrals. A new registration process could have created an unexpected barrier. Without that context, someone reviewing the numbers might reach the wrong conclusion about the program.
Researchers and evaluators bring valuable skills to this stage. They can examine patterns, test assumptions, compare information across sources, identify limitations, and distinguish between what the evidence supports and what still requires further exploration. At the same time, they benefit from conversations with the people who understand how the organization collected the information and what happened while the work took place.
That exchange strengthens the analysis. Rather than asking one person to interpret everything alone, organizations can bring different perspectives together and ask, “What do we see here, what might explain it, and what else do we need to understand?”
Sharing Data Requires More Than a Good Statistic
The next hand-off often happens when an organization needs to communicate what it has learned. At this point, development and communications teams play especially important roles.
A communications team may receive a finding such as, “82% of participants reported increased confidence.” The number looks perfect for an infographic. Before publishing it, though, the team needs context. Who participated in the survey? What did “increased confidence” mean in this study? How many people responded? Did participants report the change immediately after an activity or several months later? What conclusions can the organization reasonably draw from that result?
Development staff face similar questions. A strong statistic can help demonstrate progress to a funder, but the statistic alone may not tell the full story. Recommendations, qualitative findings, implementation challenges, and limitations can all influence how the organization should describe its results. When development staff understand that context, they can make a compelling case for support without overstating what the evidence shows.
Communications and development teams also contribute knowledge that should travel back through the organization. They often know which findings audiences understand easily, which questions keep surfacing, what funders want to know, and where the organization struggles to explain its work. Those insights can influence future research questions, evaluation plans, and data collection efforts.
Shared Responsibility Doesn’t Mean Everyone Has the Same Job
An organization can be data-driven without expecting everyone swapping roles.
People need enough understanding to recognize how their work connects to the next person’s work. Frontline staff should know why the organization asks them to collect particular information. Researchers and evaluators should understand the conditions surrounding the data they analyze. Development staff should know what findings actually support before placing them in a proposal. Communications staff should have enough context to translate findings accurately for broader audiences. Leadership should understand both the possibilities and limitations within the information they use to make decisions.
Clear roles still matter. In fact, collaboration works better when people understand where their expertise begins, where someone else’s expertise adds value, and when they need to bring another person into the conversation. Organizations can start by asking a few simple questions: Who needs this information? Who has context that could help us understand it? Who will use the findings? Who will communicate them? What does the next person need from us before we hand the information over?
These questions can reveal gaps that no new dashboard or database will solve on its own.
Key Takeaway
Data rarely belongs to just one team. People across an organization help determine what questions matter, collect information, provide context, analyze findings, make decisions, tell stories, communicate results, and identify the next questions worth exploring. Strong collaboration helps each person understand both what they hold and what someone else needs from them.
Your data is only as strong as the hand-offs between those people. When organizations strengthen those connections, they give themselves a better chance of collecting information with purpose, interpreting it thoughtfully, and sharing it in ways that remain useful and accurate.
Raise Your Voice: Where does data change hands in your organization, and what could make that hand-off stronger? Share in the comments section below.
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