Hey there, Deven here to chat with you about the importance of interrogating your data! In our experience this is a crucial prerequisite to data analysis and sense making but is often forgotten. The considerations below are a starting place as you build in strong data practices. After reading this, we hope you’ll consider building in a data interrogation checklist of your own.
An analysis of secondary data (i.e. archival/existing data) is a component of almost all of our projects. In fact, sometimes our team is brought on solely to analyze and make sense of data already collected. We work with everything from survey results and performance data to ethnographic data from interviews and focus groups. Regardless of the type/format of data, it’s always important to spend time getting to know it (and all of the assumptions that were made when it was collected). For our team, this interrogatory process is a necessary first step as we work toward ultimately making sense of a data set, and it is a big part of our data management process.
When you hear, interrogate your data, you might be thinking about traditional analysis — descriptives, t-tests, thematic analysis…and although that is true to a certain extent, what we mean is everything prior to hitting run in SPSS, R, NVivo or whatever other statistical tool you’re using! We’re talking about spending the time to find out the background of the data by asking questions like these:
- When was it collected?
- What was going on at the time it was collected (i.e. contextual considerations)
- Who collected the data?
- Why did they decide to collect the data?
- What’s their standpoint (i.e. perspective/position)?
It’s also important to solicit insights from data experts. You might be thinking, wait, isn’t that supposed to be me? Sure, as a data analyst, you add a level of technical expertise — but really, you need to find the context experts of the data in question. Those who collected the data, provided data, requested the data be collected. What do they have to say about the data? You might be surprised at what they say.
So, why spend time doing this?
Because all of these aspects impact what we see (or don’t) in the data. For example, the motivation to collect the data often frames the entire process — regardless of whether it’s a quantitative or qualitative project. Furthermore, who collects your data influences the data at a variety of levels (especially if they aren’t aware of their own bias and impact on the data they’re collecting). By engaging in a thorough interrogation (sometimes called a data biography), you’ll gain insights into the overall quality of the data, identify new questions or trends you might not otherwise arrive at, and likely find opportunities to improve data collection practices going forward.
In short, our role as data fanatics isn’t just to ensure data is used but also to ensure it’s used consciously and with intention. We have a responsibility to ethically collect and use data. Without time dedicated to learning about the origins of data (even if you’re the one collecting it), we can find ourselves making assumptions or missing a valuable perspective. A thorough data interrogation checklist is a must-have for anyone working with data. And, by the way, you should also be interrogating the data you collect, too! Not sure where to start? Let’s chat.