How do you cope with data overload and avoid data fatigue?

I am an “overplanalyzer,” so having a detailed plan before data collection is a must for me. You need to identify what you are looking for and why before you actually start gathering data. Measurable questions and quality hypotheses are essential to making sure you gather the right data. I also always recommend having someone else review the questions and hypotheses to get some feedback. Your biases can influence your research from the very beginning, so working with a partner or a diverse group can lead to more complete and accurate results.

It is really important to keep data points in perspective when determining quality. For example, a percentage could seem really high or low, but you need to consider the sample size when determining if the data point is truly significant. Triangulation greatly helps to avoid one data point being elevated too greatly or being used as the sole indicator for decision making. It is also important to chose the data you wish to share based on your purpose and audience. The quality of a data point is only as great as its ability to demonstrate impact (or there lack of). Some data are too weakly aligned to identifiable actions/processes to be considered as a meaningful reflection of impact on the work.

The great thing about the internet is that you can find anything and everything on it. The bad thing about the internet is that you can find ANYTHING and EVERYTHING on it. Keep a solid criteria for any data you come across and evaluate it thoroughly before using it. The criterion can be your purpose, the curriculum you are studying, or a window of relevant time, etc. The more airtight your evaluation methods, the less likely you are to lose your way.

Organize your data in such a way that it not only informs, but tells the story of the progress made. Simply and less are best.