Having a great theory of change, sound metrics, and lots of data is worthless if you don’t put that data to use. In this series, I’ve taken a look in the mirror at how Idealware figured out how to ask the right questions and hunt down the data. In this final post, I’ll tell you about the next step on our journey to becoming a data-driven organization: how we keep the data visible and use it make decisions.

Keeping Data Front and Center

All the experts tell you that you should review your data on a regular basis. Great, but how exactly do you do that? You do it by deliberately creating routines and triggers.

Here’s how it works at Idealware. We all share the work and responsibility for data collection and analysis, though I take ultimate responsibility for it. For example, Chris reports to me each month on audience metrics. I used to literally have a recurring monthly task in Todoist called “Get the audience numbers from Chris.” Then I modified it to “Confirm audience numbers received” and added another task a week prior called “Remind Chris to send the audience numbers.”

You can see a problem surfacing here…Chris is a very busy person. He never failed to send the numbers, but it wasn’t at a predictable date and time; it was when he could make time for it. In fact, both of us found it very difficult to squeeze this type of work into our meeting-heavy schedules.

Our solution was to batch it into an Administration Day. It happens the last working day of every month. No meetings are allowed, and we each have a list of administrative tasks to complete on Administration Day before moving on to project work or other items. That list includes compiling metrics data. So far it’s an effective routine.

We also look at the data on a quarterly basis with our board. It’s a standing item on the board meeting agenda. I fill in our most important metrics on a dashboard spreadsheet. Then we discuss progress, anomalies, and course corrections needed. We also provide more detailed data to the board every year before our annual board retreat. Board members and staff leaders together use this data to get a big-picture view of our current situation and trends, evaluate the programs, and hold the organization accountable to our mission and theory of change.

Chart showing number of enews subscribers over time

Using Data to Make Everyday Decisions

Data not only gives us insight into our direction and effectiveness, it also helps us make day-to-day decisions. For example, when we meet to develop our editorial calendar, we have audience feedback spread out on the table and taped to the walls. Data on course registration and giving trends helps us forecast and guide our budgeting and cash management.

We had some pro bono help in building the Registration Calculator, a fantasic spreadsheet that predicts course registration numbers based on historical data.

Screen shot of registration calculator tool

Predicted registration is valuable, actionable information. If we see that a course’s predicted registration is lagging behind the goal, we can choose to send an additional email promotion featuring that course. On the other hand, if a course is ahead of its goal, we can dial back promotions and direct our energies elsewhere.

Using Data to Make Our Case

Finally, data helps us to tell our story and make our case with sponsors and funders. Take sponsors, for example. Idealware sponsors view it as a charitable contribution to make the nonprofit sector stronger, and they believe in our work. Yet they also have many other options to support knowledge resources and capacity building efforts.

If I can show a sponsor data about Idealware’s reach, how many people opt in to sponsor offers, and audience distribution, that’s persuasive.

Similarly, with grant makers, a few simple graphs or data points can do a lot to strengthen our case and give us credibility.

Your Turn

I’ve shared some detail (not all of it flattering) about Idealware’s data practices. Now it’s your turn. What is your organization doing to define goals, collect data, and put that data to use? What lessons have you learned on your journey along the data maturity spectrum?

Please share in the comments.