Building Effective Data Management Practices
Pinned butterfly specimens with corresponding labels.
If there’s one thing I’ve learned in my time as an informal STEM education evaluator, it’s that you must label your work! Effective data management practices are a cornerstone of good evaluation practices.
At Improved Insights, we set up SOPs (standard operating procedures), style guides, and other tools to keep us organized. When working with others on evaluations, defining procedure and establishing a common language for the project helps us stay on the same page. These tools can also serve as effective guideposts when revisiting an evaluation project after some time (e.g., if you’re working on quarterly reports, a multi-year project where you revisit data analysis, etc.). They’re also important if you’re working on multiple projects at once. For us, this means multiple client projects, each with its own needs, design specs, and data. For you, this might mean working on evaluations for education programs under the same organizational umbrella, but with more partners (e.g., educators, program coordinators). In either case, staying organized and on the same page is crucial to a successful program evaluation.
Well, are you now convinced and looking to improve your data management practices? Look no further! We’ll outline a few actionable tips to get you started.
Tip #1: Set up standard practices for data cleaning. One of the most important steps when receiving raw data is to carefully clean it and prepare it for analysis. To establish guidelines for the data cleaning process, we develop an SOP. The SOP outlines the steps we need to follow, from checking for duplicate entries, to fixing clear typos, to flagging potential “bad” data for review and removal. Processes for data cleaning should reflect the type, or types, of instrument used to collect the data. Many of our clients, for example, employ surveys to gather feedback from participants. Survey data cleaning will look pretty different from cleaning interview data. The questions included in different instruments will also dictate what data cleaning looks like, as will the data collection approach. Online surveys, for example, may allow for accidental duplicate entries. Paper surveys have a lower chance of this occurring (though never say never!). You can find plenty of checklists online for data cleaning, but every project is unique. You might consider adapting one or more of these checklists to suit your specific data needs.
Tip #2: Standardize your file labeling and organization. Have you ever tried to find a file on your company’s drive and been left wading through a sea of duplicates and outdated document versions? Establishing file organization methods and naming conventions are two great solutions to this issue! We’ve standardized our approach to setting up folders for new projects. Each project folder contains subfolders that outline the various stages of the evaluation process (e.g., instruments, data collection, analysis, reporting), along with administrative and meeting files. The subfolders house only files related to their project stage. That way, we always know where to look to find what we need. We also label files in a standard format, including the client or project name, the file name, and the date (e.g., “Improved Insights_Data Management Practices_2025 11 12”). This helps us when globally searching our drive, and makes it easy to know at a glance which file version is the most updated. You might consider adopting a similar approach and creating a document to onboard new employees and volunteers to these conventions.
Tip #3: Pay attention to issues of anonymity, confidentiality, and data privacy. As evaluators, we pride ourselves on following best practices for protecting the data of participants. We carefully consider the need for confidentiality or anonymity in data collection and analysis, and develop comprehensive consent and assent procedures to ensure participants are aware of our work and opt in to the process. We also consider the storage and management of this data once collected. Often, our clients are “too close” to the program participants to offer this themselves, so we ensure that data can be de-identified or collected anonymously. While these types of protections are not always needed, it is important to know when they are appropriate and how to enforce them. You might consider setting up a procedure for identifying when these protections are needed, for ensuring data is collected safely and consent policies are enforced, and that data is stored securely by de-identifying or setting up private drives to house raw data.
We hope these tips help you get started on building your data management practices! Adopting even just one of these data management practices will set you ahead. If you adopt any of these practices, make sure to track and reflect on your “wins.” Science shows that making progress towards a goal is more satisfying than achieving a goal. You can use this to your advantage. Maybe you choose to up-level your evaluation practices each month by picking one area to improve and reflect on. This progress will keep you motivated to continue to improve your work. Feel free to raid our Insights for more ideas.
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