Introduction

Is your team accomplishing what it is trying to accomplish? The role of data is to support you in answering this question.

Quantitative numbers data, as well as enriching qualitative data, is vital for you to collect, manage, and use in order know where your team is and is not accomplishing its mission.

For many community-based nonprofit organizations, data management refers to straightforward ways to collect at least three types of data and then safely store and organize that date:
1 |  Customer service feedback data shows your team how well the delivery and processes of your programs and services are connecting with the people are the center of your mission.
2 |  Growth or learning data shows that people’s lives are better after engaging with your programs and services.
3 |  Long-term benefits and/or advocacy impact data shows that the mission of your work is changing factors that can prevent the need for your programs and services (i.e. put yourself out of business).

In this month’s 2 Tips, we provide you with small actions that can support big growth among your team’s data skills and know-how.

Tip 1

Using the Data Management Muscles guide, decide which actions your team will begin implementing in order to build their know-how and confidence.

 

Tip 2

Schedule short reflection meetings to assess how well the team is growing their strengths and which supports work best for the team.

 

Data Management Muscles Guide

Description

Managing the collection, storage, and use of data benefits your team’s important work. The actions listed below are all important to get to at some point in the lifespan of your and your team’s work. However, strong teams always start somewhere; choose any of the actions that are most doable right now.

Do no wait until you can find the “best timing” — that timing may never come. Starting small is smart: your team’s confidence will grow by getting successes established and by you illustrating that data management is easier than previously envisioned.

 

Action Purpose Timeframe
1 |  Review current data storage practices. This action will focus your attention on whether the right data is being collected, whether different types of data should be collecting going forward, and whether there are appropriate guidelines and policies in existence to support your team on data storage. Annual
2 |  Time your team on how fast goals data can be accessed. In order to track progress on goals and plan strategy for ongoing success with goals, your team must access and present data on a monthly and/or quarterly basis. Data summaries should be easily accessed. Based on how much time your team spends on accessing and summarizing data, you may need to improve data collection, storage, and reporting procedures. Monthly and Quarterly
3 |  Connect data to discussions in clear and coherent ways. All planning and strategy meetings should have precise data points connected to major questions, proposed ideas, and decisions. The questions, ideas, and decisions may come from the organizational strategic plan, program-specific goals, or from emerging/unexpected  factors. Quarterly
4 |  Write a user guide for accessing the stored data. The steps outlined in such a guide should be decipherable to anyone inside and outside of the team. How coherently and simply the guide is written will reflect back your team’s current strengths and confidence with data management and data communication. Annually
5 |  Write a user guide for sampling from stored data to ensure the quality of the stored data. The steps included in such a guide should be decipherable to anyone inside and outside of the team. How coherently and simply the guide is written will reflect back your team’s current strengths and confidence with assessing the quality of the data (i.e. How to find gaps, inaccurate, and missing data). Annually
6 |  Sample from stored data to ensure the quality of the stored data. Once your team has a rhythm of data management actions from carrying-out the best-practice presented in this tool, start sampling from the program and service data. Actual sampling will rely on the guide described above in action five. Expect to find gaps, inaccuracies, and missing data as this is an expected part of the world of running programs and managing program data. Once these errors are found, then addressing the errors will follow. Quarterly