Data-informed decision making is a hot topic across almost every college campus.
Your student success initiatives, strategic decisions, and even your academic program decisions are all expected to be data-informed, writes Michel Koppenheffer, a vice president at EAB.
But drawing insights from a mountain of data is a complicated task, write Jennifer Garvey Berger and Wendy Bittner for Google's re:Work blog. When tackling a large and complex data set becomes overwhelming, Garvey Berger and Bittner offer three questions to refocus your analysis.
1: Are you taking the right approach?
The data analysis approach should depend on whether the challenge is predictable or unpredictable, write Garvey Berger and Bittner, partners at Cultivating Leadership.
Unpredictable challenges are heavily context-dependent, so it's more difficult to identify the likely outcome. While a predictable challenge can be approached with previously used methods, an unpredictable situation will require you to re-evaluate your approach or try something new, they add.
2: Are you trying to confirm your hypothesis?
If you set out to prove your hypothesis, you may not learn anything, Garvey Berger and Bittner write. Cherry-picking data to prove a point can lead to poorly informed decisions, the authors warn. Instead, evaluate all of the possibilities, rather than the few pieces that confirm your initial assumptions, they recommend.
3: Are you asking for help?
To approach a data set from multiple perspectives, ask your colleagues for input, Garvey Berger and Bittner write. Specifically, they recommend discussing the trends in the data that surprised you or that you can't currently explain. By focusing on the surprises, you may uncover new hypotheses to test and learn from, they add (Garvey Berger/Bittner, re:Work, 11/14).
Related: 3 reasons why collaboration is critical for data-informed decision making
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