What gets measured, gets funded
How big data can be used to secure funding for community development. As Peter Drucker, the famous management expert, said, “What gets measured, gets done” or “What gets measured, gets funded.” It isn’t merely enough anymore for recipients of government aid programs and foundation grants to show anecdotal evidence of progress. They are now being required to demonstrate proof that their programs are not only beneficial for their constituents but are also providing a positive return on investment for the program funders.
In the area of community development, it has often been challenging to measure success. Especially, as there are many intangible benefits to community programs such as improvement in the quality of life for residents. The main obstacle is not in actually getting access to data sources but rather in the analysis of the information. For example, cities already collect a huge amount of data such as tax and financial records, rates of small business ownership, school enrollment, public transportation usage etc. The key for community development professionals is to determine how they can use this data to prove that their programs are working.
As Paul Mattessich, an executive director at Wilder Research, explained “in order to measure the success of community development, it is important to understand the difference between measuring the process of development and measuring the outcomes of development.” In other words, there is a distinction between simply monitoring activity versus tracking the end results of those activities. For instance, it is not difficult to tally the total number of teens that are participating in an after-school program. However, data points (beyond pure attendance) need to be analyzed in order to determine whether the program resulted in a positive impact for the community as a whole. Did attendance rates at local schools go up? Did cases of vandalism go down? Did overall crime decrease? Did those students have higher graduation rates and did a higher percentage go on to college? These statistics show a much more holistic view of how a program impacted a community at large and not just the primary participants.
One example of how data was used to get project funding was conducted by the Pratt Institute in Brooklyn, NY. They used data visualization techniques to show the disproportionate amount of time that low income workers spent commuting to and from work compared to wealthier residents. They were able to illustrate that lower income residents had the longest commutes, the fewest transit options and the least access to jobs as a result. Working in conjunction with the New York City Department of Transportation as well as The MTA, they were able to get a plan approved for new transportation routes to provide better commuting options for lower income workers as well as help bridge the gap between community interests and government agencies. Additional data points can and should continue to be collected to measure the long term effectiveness of the funding efforts such as increased levels of employment for people residing in areas where transportation access was improved. However, by starting with information that was already available and turning that into a visual diagram of the problem, they were able to make a clear and concise case to increase the budget for new transit lines.
There is no doubt that new sources for information will continue to increase and community leaders are going to need to embrace this and use it to their advantage. They will need to develop an overall strategy as well as hire analysts or outsource to experts that can collect and interpret the data in a meaningful way. As these strategies are being put in place, there are also some key best practices that professionals should utilize. First of all, determine in advance what criteria will be used to define success and keep the number of measured variables small enough to be manageable. Secondly, invite all stakeholders into the process early and get consensus on the evaluation techniques that will be used to determine a favorable outcome. And, try to find data sources that are also readily available. Finally, identify a control group or baseline for comparison purposes. The more that community organizers can embrace the use of big data and prove successful outcomes, the more likely they are to get projects funded in the future.