We spend a lot of time helping fundraising organizations use data to make better decisions. After more than 20 years in this work, we have learned that the biggest challenge is not usually getting more data.  

Most organizations already have plenty of it. They have dashboards, models, reports, wealth screening, research, CRM data, prospect scores and information sitting in more places than anyone probably wants to admit.  

The harder part is figuring out what all of it means and how to turn it into something people can actually use. That is why we think about data as a living asset.  

A model is not finished when it is delivered. A dashboard is not finished when it goes live. The real test comes when a fundraiser looks at a recommendation and says, “That does not match what I know about this prospect.” 

That is not necessarily a problem. It is often where the most useful conversation starts. 

Sometimes the Data Is Not the Problem 

We have worked on projects where analytics teams and frontline fundraisers spent a lot of time debating whether recommendations were “right.” 

At first, it looked like a disagreement about the model. It was not. The analytics team was thinking about what the model had been designed to predict. The fundraisers were thinking about what they were seeing in their relationships. Everyone was using the same words, but they were not necessarily asking the same question. 

Once we talked through what they actually wanted the analysis to help them accomplish, the conversation changed. The model did not change. The data did not change. We just had a much better understanding of what we were trying to accomplish. 

We have seen this enough times to know that asking, “Who is right?” is usually not the most useful place to start. Fundraisers know things that will never show up in a model.  

They know the history of a relationship. They know what happened in a recent meeting. They may know that a prospect just joined a board, had a significant change in their business or said something in a conversation that completely changed the opportunity. 

Analytics brings something different to the table. We can look across thousands of records and identify patterns that are almost impossible to see, one relationship at a time. 

Neither perspective tells the whole story. The real value comes from putting the fundraisers' knowledge and the analytical patterns and suggestions together. 

Trust Matters 

When someone does not trust a model, the natural response from an analytics team can be to explain the methodology. 

We understand why. We built the thing. But that is not always what the person needs. 

We have had fundraisers tell us that a prospect should be a much higher priority than the model suggests. Instead of immediately defending the score, we can learn a lot by asking why the fundraiser feels that way about the prospect. 

Sometimes the answer is something the data does not know yet. There may have been a recent conversation. The prospect’s interests may have changed. The fundraiser may know something about the family or the relationship that is not captured anywhere in the CRM. 

And sometimes it is even simpler than that: the prospect may be a great prospect on paper, but not a good prospect for the organization right now. Maybe there is a sensitive situation within the family. Maybe another organization currently has the stronger relationship. Maybe the timing is not right. Maybe there is an important piece of institutional history that would never show up in a dataset. 

This is where institutional knowledge matters. Nothing substitutes for the person who has been working with an organization for years and knows its donors, its history, its relationships and sometimes the context behind a name in the CRM that the data simply cannot explain. 

A model can tell us that someone looks like a strong prospect. It cannot always tell us whether this is the right person to approach today. 

That does not mean we should ignore the model. It means we should put the model in the hands of the people who have the context to use it well. That distinction matters. 

The purpose of analytics is not to win an argument with a fundraiser. It is to help the organization make better decisions. 

Sometimes the model will challenge what we thought we knew. Sometimes the people closest to the relationships will give us context that changes how we interpret the model. 

That is exactly how this should work. 

The Handoff Is Not the End 

Nonprofits need to rethink how we define the end of an analytics project. Too often, the process looks like this: someone makes a request, analytics builds something, the deliverable is presented and the project gets closed. 

That may be how we track the work. It is not necessarily how the value gets created. Some of the most useful questions come after the model or dashboard has been delivered. 

A fundraiser may ask why a particular prospect ranked where they did. A development operations team may realize that an important field is not being captured consistently. Leadership may realize that the original business question has changed. 

Those are not failures of the project. That is feedback, and we should pay attention to it. 

The strongest analytics work we have been part of has involved a lot of interaction between the people building the analysis and the people who are going to use it. We ask questions early. We test assumptions. We listen when someone says something does not look right. We come back to the business question when the conversation starts getting too focused on the technical details. 

It may take a little more time up front. We think it is worth it. People are also much more likely to use something when they understand why it was built and feel like they had a role in shaping it. 

What Happens After the Insight? 

This is really what we mean by data being a living asset. The value is not sitting in the model. It is in what happens after the model gives us something to think about. A prospect gets prioritized. A fundraiser makes a call. A relationship changes. New information gets added to the CRM. The organization learns something. That new information should eventually make its way back into the process. 

This is where we think analytics can become much more valuable to fundraising organizations. Instead of treating a model as a one-time answer, we can use what we learn from the people using it to improve how we approach the problem. 

That also means being comfortable hearing that something is not working. If a fundraiser tells us, “The model is wrong,” we do not necessarily need to prove them wrong. A better question is: 

What are you seeing that the model is not? 

Sometimes that uncovers a data issue. Sometimes it exposes an assumption that needs to be reconsidered. And sometimes it tells us that we are trying to answer the wrong question. All three are useful. 

In our experience, this is one of the most important parts of analytics work that does not get talked about enough. The value is not only in building the model. It is in creating the conversation around the model and learning from what happens once people start using it. 

We Do Not Always Need More Technology 

There is a tendency to think that improving data-driven decision-making requires another platform, another dashboard or another sophisticated model. 

Sometimes it does. Often, it does not. Sometimes we need to involve the people who will use the analysis earlier in the process. Sometimes we need to spend more time defining the question before we start building. Sometimes we simply need to create a better way for fundraisers and analytics teams to talk to each other. 

And sometimes we need to acknowledge that the data does not tell us everything. We have seen technically impressive models that never really get used. Not because the analysis was bad, but because the people expected to use it did not understand how it connected to their work or did not trust the recommendations. 

That is a data problem, but it is also a people problem. 

We think this is especially important in fundraising, where so much of what matters is difficult to capture in a database. Relationships are complicated. Circumstances change. A conversation that happens today can completely change how we think about a prospect tomorrow. 

Our data needs to keep up with that reality. The goal is not to build something impressive and put it on a shelf. The goal is to create something people actually use. Something they can question, learn from and improve. 

That is what makes data a living asset. And that is what turns insight into action.