Hornstein Jewish Professional Leadership Program

In order to help ensure that strategies are effective at achieving their intended impact within a community or content area, we need to rely on both qualitative and quantitative data. Data helps us make strategic decisions about whether to modify, grow or revamp an approach to different problems or opportunities we seek to address. In nonprofits, making trade-off decisions in an environment of limited funds and competing or changing goals becomes difficult. Data creates the potential for more informed decision-making based on evidence of successes and need for change within programs.

I think most non-profits use data ‘light’ in their decision making. Program managers have a general feel for the data but may not have a formal data collection, analysis and review process. Rigorous impact measurement can be prohibitively expensive. Most organizations collect output data (how many people they serve, for example), but it is much more difficult to collect impact data especially in fields where the impact may be much more anecdotal than measurable. There is a definite trend toward an increase in outcome rather than output measurement and more rigorous data collection in general; however, the simultaneous trend of a decrease in support for overhead and unrestricted funds can make these two trends stand in opposition.

Nonprofits often cite operational reasons such as cost, skill and staff time as well as reasons such as staff's impressions of what they know to be true through working directly within their programs (interviews, anecdotes, relationships, qualitative information) which, nonetheless, can be difficult to turn into the quantitative data, as reasons they don't use data. Data can be difficult to collect due to legal constraints surrounding ages of participants, people are hesitant to share information, people are not willing to take the time to provide information.

In terms of analyzing data, "data out" is only as good as "data in." The value of analysis is very much dependent upon the quality of the data provided. If you do not have rigorous data collection and entry (deduplicated, objective, consistent etc.), it is incredibly hard to have an accurate analysis. In terms of pitfalls with collection, it can be time-consuming especially within nonprofits that are not staffed for such activities and have employees already spread thin with multiple job functions. A pitfall for data analysis is that often staff are not trained in data analysis, and so while there may be great data collection, interpreting the results becomes more difficult.

I find the best way to determine the metrics you need is to build a logic model. You need to define the problem or opportunity you seek to address as well as the impact you hope to have—the‘what.’ You can then identify your inputs (resources including financial, materials and staff etc.) and your interventions (programs, activities etc.) — the ‘how.’ Once you know the ‘what’ and the ‘how’ you can determine why you chose those activities. What result(s) are you hoping they will achieve? You can then start to identify how you can measure for the desired results and define what you will do if you are not achieving those results. What do you hope to learn and what decisions will you make with the information you receive? This includes identifying output, short, medium and long-term outcomes so that you have set metrics for yourself at different points in time.

This is not a hypothetical example but a real study. The city of Somerville commissioned a cataloging of every single tree in Somerville. The data-set includes information such as location, type, health, age, etc. of the trees. That data-set can be used to determine safety issues as well as sociodemographic issues. Is tree health impacted by the socio-demographics of different areas and if so, why? Is there a greater diversity of trees in some areas and if so, why? Do any trees pose a risk, and are there benefits to having more/less trees?

Data visualization is a key element in reporting, and we increasingly try to make it a part of telling our data stories. Visualization of data is important because it can present complex information in much more digestible ways both for donors and internally for our own purposes. In deciding how to present data there are several factors that we consider:

• What is the story we are trying to tell?

• Who is the audience?

• Is it meant to inform, to call to action, to make a request, to organize thoughts?

I do not think intricate math skills are necessary for data manipulation. What's more important, rather, is being able to define what you seek to understand. People see numbers and get immediately overwhelmed. Often, taking the time (and it does take time) to read slowly and let yourself absorb the information and think about what story you are trying to tell or what someone is trying to convey helps immensely.

Use the people around you, whether it is for math or any skill. Chances are you work with others who complement your weaknesses and vice-versa. I have also found that within the nonprofit world there are many opportunities for professional development (often free). You just have to pursue them (they are also great networking opportunities).

I have a love-hate relationship with data. Given limited funds within a nonprofit, it is always a hard decision to spend money on data when those dollars could be spent elsewhere in programming that would potentially help more people. Then again, without being able to demonstrate the impact and value of a program to donors and managers, you run the risk of losing funding or having a less effective program.

I was hired initially as a project specialist working on measurement and reporting as CJP continued down the path of impact measurement. The large majority of the time, I do not work directly with the data itself. As a project manager one of my tasks is to work across the planning department to help create logic models which include measurement plans for different content areas. I also help think about how to communicate the information effectively, as well as the strategic or programmatic implications data may have.

Data is a necessary evil. People love to hate it and groan about it — the cost, the time, the necessary skills, it takes away from precious content time —  at the end of the day however, data provides valuable information from which necessary decisions are made. Numbers, graphs, charts and the like can look overwhelming but if you take the time to read, nothing is as scary nor unapproachable as it looks.

I do not think that measurement or specifically data-related fields are my Jewish nonprofit "home." With that said, I find that it is so immensely useful to understand how to develop a theory of change, measure your impact and work comfortably with data. I am grateful for how much I have learned with measurement as a significant portion of my work. There is strength in knowledge and I find that those who take the time to learn the data behind their work always have the strongest voice and understand how to advocate for and make the best strategic decisions.