We hear this a lot from our colleagues working in the non-profit world: “I know managing data is important, but I have so many questions.” To get you started, CCNY evaluators and data analysts compiled the most Frequently Asked Questions (FAQs) from our current and prospective clients. Data analytics is important for organizations because it helps you optimize performance and determine the most effective way to allocate scarce resources to maximize social impact.
Question 1: I have data in all sorts of places (online databases, various spreadsheets, and even on paper). How can I use my data in order to inform what we’re doing?
Start with a data inventory analysis. Review what you want to measure without thinking about the data you currently have, and then you compare that list to the data that you have. This will identify what you currently can and cannot measure. Now you can measure metrics that your data supports, and begin to collect data for the areas you want to measure but don’t have data to support yet.
Question 2: Everyone’s talking about using data to inform our work. Can you help us figure out where to start?
Yes, we have trained over a thousand people on how to collect and measure their data to make data-driven decisions. We can’t stress how important it is to do a logic model and a data inventory analysis before you start building your dashboards and data visualization. These two tools directly inform you on how to proceed. If you use Excel, PowerBi, Tableau, or some other tool without a plan, you will spend a lot of time making pretty charts that probably won’t tell the story you want to tell.
Question 3: We already measure many things, how can we turn our data into useful dashboards that our team can use without having to update them by hand?
All dashboards are fundamentally the same. You have data, and you have charts that aggregate the data. You can do this in Excel where you have one worksheet in your workbook called “data” and another called “charts.” You can build your charts in such a way that when you add another row of data in Excel, the chart(s) will update automatically. Here is how the StackExchange user dav describes this process:
“Convert your “data” to an Excel Table by going to Insert > Table. Then, create your chart from the Table column. As you add data to your table (by simply typing in the next row), your chart will automatically expand.
- As a bonus, as you sort or filter your table, the chart will automatically update with the Table’s data
- As an additional bonus, you can use Excel’s ability to reference Table elements in your formulas, and you can do some basic summary and formatting work”
Question 4: We’ve always measured what funders want us to measure, but we think it might be important to track some other metrics as well. How should we decide what to measure so that it won’t negatively impact our capacity to do our work?
Do some research on what other groups like you measure that is meaningful to understanding the population you are working with or the program/intervention that you have. Then choose a few of those metrics. You got into this work to improve people’s lives. If you measure that improvement, funders will probably be interested in the data, even if they didn’t ask for it.
Question 5: How can we most efficiently track funder-identified metrics so that we still have the capacity to measure other metrics that we know are important too?
If you want to make sure that you track the information in such a way that you can use it for many purposes in the future, you should strive for what is called “clean” or “tidy” data. This means having your data organized in a meaningful way. This idea has been identified and established for years, and is best described in the book, “R for Data Science,” by Hadley Wickham and Garrett Grolemund. Chapter 12 describes three interrelated rules which make a dataset tidy:
- Each variable must have its own column.
- Each observation must have its own row.
- Each value must have its own cell.
With an eye towards assuring the data is tidy, you want the first column to contain all of the variables you need to measure (gender, school district, grade, meeting outcomes as a yes/no field, household income, etc.). Each row should be an observation for each participant in your program, and every cell should be a value regarding that observation. Now you can do math and statistics on that data anytime you want because the key variables are organized.
Making sure that all data is organized this way will allow staff to quickly pick up and work with it. This data organization technique saves valuable time allowing you to measure what funders are looking for and measure what your organization wants to measure.
Question 6: What data is most useful for a given audience?
There are usually a few basic measures that we present to all audiences, for example:
- The number of participants
- General qualitative feedback about satisfaction with a program
- Aggregate program outcomes
When designing data dashboards and individual data visualizations, we often intentionally tailor those visuals to a specific audience or a specific level of an organization’s hierarchy. For example, nonprofit board members, funders, and directors are often looking for what we call “strategic” information. This information provides the big picture of the program, allowing them to create strategies for moving forward.
On the other hand, front line staff and their managers are often interested in the quality and efficiency of services at the ground level, which we call “operational” information. An example is the length of time it takes an intake coordinator to see clients. With this information, their manager can target areas for improvement or areas of proficiency.
Question 7: How can we present our data so that program participants, front line staff, managers, funders, and other stakeholders can all understand our impact?
There is much truth to the adage, “A picture speaks a thousand words.” We know that most people understand data visualizations better than tables with rows of numbers. We recommend:
- Creating visual representations of numbers
- Combining different types of data visualizations: pie graphs, linear graphs, heat maps, bar charts, etc.
- Creating simple infographics
- Using images to make messages more memorable
- Using a simple and consistent color palette
- Writing the conclusion in the title of data visualizations, to provide a clear summary
As you can see, with the right planning and setting of expectations, data doesn’t have to be scary. Approach it as you would anything else: step by step. Click to view more in-depth insights from our staff on program evaluation and data visualization. To find out how CCNY can help your organization use data to improve outcomes, schedule a consultation today by calling (716) 855-0007, ext. 317 or e-mail email@example.com.