How Data Analytics Can Help NGOs Fulfill Their Social Mission
NGOs, also known as 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. Vault is looking to apply the science and art of measurement and data analytics to help NGOs accomplish their various missions, and we believe that if applied correctly, analytics can make a huge difference in NGO effectiveness
We’ve broken down the process of how to use analytics for NGOs into three categories, summarized below. We feel that it presents a systematic and practical approach to foster performance management and measurement in these organizations.
The first hurdle that must be crossed is that of measurement, of taking the time and effort to measure work and progress and 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 a measurement strategy. First – it is important to not only measure the end goal, but also the incremental steps that lead up to that goal. 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 withing 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 – and you can find out which efforts are the most effective at getting rid of this problem.
Second – make sure and measure regression rates. Too often we stop the measurement once the problem is solved – once we have lowered the diabetes rate, in this case. But how many of those people, after we stopped working with them, have regressed into having diabetes? This is sometimes an alarmingly high number, and 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 through measurement that the regression level is high, it’s time to implement some strategies and efforts into keeping the solution in place – that 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, 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.
Once we have measurement strategies in place, now we have lots of data on our hands. Analytics 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 analytics 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.
One of the most important things to remember in doing analysis 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.
Not to be forgotten is the element of presentation. 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 analytics 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 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 it simple (showing too many variables on a graph, or just plain too many graphs, causes more confusion that it does clarity)
– Relate the 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)
Hopefully this small outline 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?