Why use nonprofit data analysis, you ask? NGOs, or non-profits here in the U.S., fulfill a very important role as they seek to accomplish social good. They are in a unique position that allows them to see social need and react to it in ways that often times have more impact than other organization’s efforts could.

However, until now, nonprofit data analysis is still not widespread in the nonprofit sector. In fact, a 2019 study reported that while 90% of nonprofits gather data, only 5% of them use it for daily decision making.

Data Crunch Corp is here to help NGOs apply the science and art of nonprofit data analysis. Ultimately we aim to help accomplish their various missions. Moreover, we believe that if applied correctly, nonprofit data analysis can make a huge difference in NGO effectiveness.

Simple Guide to Nonprofit Data Analysis

 

We’ve broken down the process of how to perform nonprofit data analysis into three categories, summarized below. These presents a systematic and practical approach to foster performance management and measurement in these organizations.

Measurement of Data in Nonprofit Data Analysis

The first hurdle that must be crossed is that of measurement of data analytics for nonprofits. We must first take the time and effort to measure work and progress. Then, collect it in a database for further analysis and presentation. There are several reasons why it is important for an NGO to measure its efforts:

  • Make sure time, effort, and money are being used where they need to be
  • Gain ability to prove that you are accomplishing and fulfilling your social mission
  • Gain ability to show that donor and sponsor funding is being used effectively

There are a few things to keep in mind when implementing nonprofit data analysis measurement strategy.

Measurement of the Incremental Steps to Reach Goal

It is important to not only measure the end goal, but also the incremental steps that lead up to that goal. This steps lays out the foundations of nonprofit data analysis. Let’s say your organization’s mission is to decrease the number of diabetics within a specific demographic in your community. Measuring the % decrease in diabetes within this population over a given time period is great, but it doesn’t tell the whole story.

Ask yourself, what are the incremental steps leading up to the lowered diabetes rates? Perhaps one is the amount of exercise the average person in the demographic is getting on a daily basis. Perhaps another is the amount of sweets or fatty foods the average person is consuming per day.

As you attack these issues that lead to diabetes, measure the improvement in these areas. Then people get the whole story of where your efforts have helped reduce each aspect of the larger problem. You can find out which efforts are the most effective at getting rid of this problem. That is how you start your nonprofit data analysis. But, it doesn’t end here.

Measurement of Regression Rates

Ensure to measure regression rates. Too often we stop the measurement once the problem is solved. However, this is not the way to do your nonprofit data analysis. In this case, once we have lowered the diabetes rate, we stop. But how many of those people, after we stopped working with them, have regressed into having diabetes? This is sometimes an alarmingly high number. When regression rates are high, that means all the work we performed to lower the diabetes rate in the first place has gone to waste.

If you see that the regression level is high, it’s time to implement some strategies. Then, you will know that efforts into keeping the solution in place is imperative.

A key tip in performing nonprofit data analysis is not losing ground once you’ve attained it. It’s often a lot easier to keep the problem gone than to go back and fix it again. This allows you to really fulfill your mission though data analytics for non profits, in a lasting sense. It wastes less resources because you retain the ground you’ve gained. And donors and sponsors will be excited by the fact that you can show that your solution is a long lasting one.

Extraction of Useful Data Analytics for Nonprofits

Once we have measurement strategies in place, now we have lots of data on our hands. Data analytics for nonprofits is the process by which we extract useful intelligence from this data.

There are many methods of doing this, whether it be through visual analysis techniques, statistics, predictive models, etc. (specific ways on how to do these types of analysis will be the topic of subsequent posts).

Many people think that data analytics for nonprofits is a task that is beyond their abilities, but many times even simple analysis will result in sufficient intelligence that you can use to do your work smarter.

Principle of Segmentation

One of the most important things to remember in doing the analysis of data analytics for nonprofits is the principle of segmentation. This means looking at the data in smaller pieces, rather than in aggregate. For instance, if you want to know who your most effective workers are, break down the data to show you the hours each worker put in, and the changes in the incremental metrics we discussed above that occurred as a result of their work. Maybe you want to know which types of donors consistently give high sums to support your work – break them down by demographics, or by income, or by age, or any other variables to get a view of what your ideal donor looks like. Then you can target more of these kinds of people in your donation campaigns.

Presentation of Data Analytics for Nonprofits

Not to be forgotten is the element of presentation of data analytics for nonprofits. Once you have the data and all the analysis, you need to be able to present the intelligence you’ve found to others in a way that they understand, and in a way that will cause a change in their behavior. The intelligence from the data analytics for nonprofits is there so that you can be more effective in your work; however, if no one understands it, nothing will change and it will be useless. There are a few easy guidelines to follow in presenting analytical information so that it sticks:

  • Relate the numbers of the data analytics for nonprofits to something people understand (Just saying the number 416 can be somewhat abstract, but if you say instead “the number of people that fit in a Boeing 747” the number becomes real and concrete)
  • Only show the necessary elements of analysis to get your point across (many times you’ll have to go through a lot of analysis to get a few golden nuggets of intelligence, and our tendency is to want to show off all of the work we did to get there. The problem is, the process is not important to the people you are talking to. What’s important is the results and intelligence, so just focus on that.)
  • Keep the data analytics for nonprofit simple (showing too many variables on a graph, or just plain too many graphs, causes more confusion that it does clarity)
  • Relate the data analytics for nonprofit analysis back to what concerns your constituents (Your focus should always be on solving the problem, and the analysis is only important insofar as it helps you to do that. Focus on what solves the problem for the constituents)

Final words…

Hopefully this small outline on data analytics for nonprofits gives you a framework that you can use in thinking about how to implement analytics into your organization. In the coming posts we’ll be discussing more in depth how to do each of these three points. What do you think? Is this information helpful?