While nonprofit leaders recognize the importance of measurement, many have not developed the skills they need to use data to answer strategic questions and determine whether their organizations are making progress in achieving their visions and missions. In this 7 Steps to Data Driven Decision Making Series, we share our tips for how you can do just that.

In the last few articles, we showed you how to identify the issues you would like to address and develop clear, measurable hypotheses. Now it is time for Step 3: Data Collection.

Data Collection

Collecting the right data is much easier when you know what you are trying to test. That is why Step 1: Framing the Issue and Step 2: Hypothesis Development are essential preparation for effective data collection.

Consider Sarah, our Program Manager for a capacity-building foundation who wants to increase volunteer retention. Sarah had access to a database with hundreds of fields of data. She was tempted to skip the first steps and jump right into her data. If she had, she would have spent a great deal of time sorting through her database and would still ultimately end up with GIGO or “Garbage in, garbage out”.

Instead, her ground work enabled her to identify which of her many fields of data were relevant and she saved time by only examining the data points that would help her test her hypotheses.

Tips for Data Collection

As you plan to collect your data, avoid the temptation to gather only the data that is easiest to collect. Data collection can tax your resources, so it is critical to collect the right data the first time.

Consider multiple methods of data collection; each one has strengths and weaknesses. Self-reported data, such as you will obtain from a survey, typically contain lots of errant data, and need to be carefully combed for submission errors. Incongruous data can alter the conclusions you draw. Tracking systems, like CRMs and other databases, can provide valuable data but the staff must be committed to using the system consistently every day.

Examples of Data Collection Methods

Case Study 1: Foundation

Sarah knew that some of her grantees were more successful with volunteer engagement than others. However, she did not understand what factors drove increases or decreases in retention. Sarah’s team developed more than 15 hypotheses through their logic model and brainstorming session. Of those, she chose the following three to prioritize and test, based on her mission and potential impact. Sarah’s chose to collect her data exclusively from an internal database that housed historical data on the organization’s grants.

Case study 2: Private school

John, the principle of an independent school wants to investigate the perceptions and performance of his middle school math program. He prioritized three hypotheses and explored several methods of data collection to test them. In order to fully understand his parents’ opinions, he decided to survey all of them about the strengths and weaknesses of his school. He also gathered test scores, staff evaluation forms and data on staff training to help him evaluate his remaining hypotheses.

The time you take to collect the right data from the right sources will strengthen your analysis and interpretation so that you can feel confident about your results.

Check out next month’s newsletter for Step 4: Data Analysis.