Collecting valuable data should be a top priority for any organization. Too often, however, organizations end up with a surplus of data that collects dust. To get the most out of your data, you should implement a plan and specific method to turn Excel sheets full of numbers into actionable data. “Actionable Data,” in contrast to raw data, is information that will give you real insights into your organization and provide you with the “right” information that can be acted upon to improve your efficiency and effectiveness. Below are four steps that you can take to turn an overload of data into actionable data so you can focus on what matters most — making measurable improvements in your community. 

1. Standardize Your Data Sets

Your data collection and analysis will be much easier and more efficient if you standardize your performance measurement system. Make a performance improvement plan with your data teams first and then make sure everyone is aligned with common systems and tools. What does standardization look like for complex data sets?

First, carefully choose your performance measures so that they reflect your goals accurately. Second, audit your list of measures and try your best to pare them down to a maximum of five measures per program. Third, prioritize “better off” measures (measures that speak to whether anyone is better off as a result of your programs and services. Finally, avoid changing your measures frequently.

Foundations and funders have a special responsibility to help their staff and grantees avoid data overload. Pew Research Center found that when grantors expect their grantees to use too much information to perform tasks, many feel too burdened to do their jobs effectively. In this study, 46% of Americans agreed with the following: “A lot of institutions I deal with – schools, banks or government agencies – expect me to do too much information gathering in order to deal with them.”

The key to avoiding data overload for yourself and your funded partners is to request no more than five performance measures per partner or program. You should require your grantees to report on the same or similar performance measures for like-programs. This will allow you to easily aggregate results to analyze your collective impact in the community. You can learn more about this here.

Key to Data Standardization:

1. Spend the time planning and strategizing your data collection strategy upon setup.
2. Audit your list of performance measures and weed out ones that you’re not using.
3. Focus on “better off” measures when paring down your list.

2. Always Clean “Dirty” Data

Data overload occurs when there is an excessive buildup of “dirty” data. Dirty data is incomplete, inaccurate, or inconsistent. The more efficient your data collection process is, the less dirty your data will be. Many data experts spend a lot of time thinking about this problem, referring to themselves as “data janitors.” To keep your stored data relevant and clean, you should use precautionary and retroactive data-cleaning techniques.  

Stopping Dirty Data Before it Shows Up

First, make your data collection process as smooth as possible. The easiest way to do this is to double-check all data collection fields before inputting data. If you collect program survey data, ensure that there are no spelling or punctuation errors that might lead to unreliable answers. You should also ensure that all units of measurement, decimal fields, and data properties are consistent and accurate. Here is a checklist to go through with your team for your program surveys or data collection efforts before they go live:

1. For each data field: carefully consider whether it is relevant to your goals.
2. Check spelling, punctuation, and grammar.
3. If applicable, check all included links on different browsers, using incognito windows or different profiles.
4. Ensure that all fields use the correct units of measurement, decimal information, or languages. 
5. Remove repeated or redundant questions.

Cleaning Up Dirty Data

Once you collect your data, you need to “clean up” any dirty data that might remain. You can do this by looking for outliers and ensuring accuracy. You may also want to look for numbers that appear suspiciously often. It may also be helpful to use data visualization tools for a different perspective.

Avoid the common mistake of including unreliable data. If you find that a question or input field has an error, you should not include that information in your report. Even if the mistake seems irrelevant to the data being presented, using misleading data can lead to the misappropriation of resources for your organization. Worse, it could be illegal if used in conversation with grantees, donors, volunteers, or partner organizations. Always ensure that your data is reliable.

3. Differentiate Between Different Types of Data

You’re likely collecting data for multiple purposes. If so, avoid running into organizational problems via careless data storage and organization. This means keeping data sets for 1) program performance, 2) population-level impact, and 3) client-level data separate within your data tracking system (s). When selecting your data management systems, ensure they allow you to quickly access the data you’re looking for immediately, whether it’s performance measures, community indicators, or client demographics. 

Why is it important to keep population and performance data separate? The Results-Based Accountability framework provides an excellent explanation of the differences between the two. In short, different groups of people are accountable for different types of data. Managers are accountable for the performance of the programs, agencies, and service systems they manage. They are not accountable for the well-being of whole populations. Population accountability lies with the whole community.

Client-level data is different from both population and performance-level data. Client-level data is data related to specific individuals (like demographics and personal program outcomes). Some client-level data, however, can be aggregated into performance-level data (eg., personal program outcomes into aggregate program outcomes).

4. Use the Right Tools for the Job

None of the tips in this blog are possible without the application of a system designed for organized and simple data collection and analysis. Traditional data collection and analysis systems like spreadsheets or paper are insufficient for a modern organization’s data demands. What you need is a system that allows you to:

• Quickly and easily input data
• Manage different types of data
• Share data with partners and the community
• Customize how you collect and organize your data

Using software that has these features will make your data management easier, faster, and more actionable. For example, data standardization is listed here as the critical first step in reducing data overload. So, you may want to look for a system that allows you to copy and reuse data collection forms.

Tools designed specifically for social and public sector performance management will work better for your data collection needs. Considering how valuable data is and how useful it can be for you and your organization’s goals, getting the right system in place is an essential step for you to take. For more information regarding data collection tools, read our blog on How Data Collection Software Can Help Your Organization Achieve Its Goals.