Two people were talking about the same data represented in a chart, but you wouldn’t know it. The data showed that the organization had a 96% satisfaction rating and that thrilled one professional. The other professional focused on how dissatisfied the remaining 4% were. 130 disappointed people is an unacceptable amount of dissatisfaction, she thought. Data, or rather our interpretation of it, is not as black and white as we think it is.
Even the most statistically accurate quantitative data is subject to the viewer’s interpretation. Each and every one of us looks at the data through a different lens, and this impacts our analysis. Sometimes our perspectives can be quite diverse. Other times our views are nearly identical but for a few nuances. Our previous experiences color our worldview, and from this worldview, we make assumptions about other people’s experiences, feelings, hopes, fears based on our experiences.
Our previous experiences color our worldview, and from this worldview, we make assumptions about other people’s experiences, feelings, hopes, fears based on our experiences. This worldview can make us wildly inaccurate in predicting the feelings of others, even those closest to us. Imagine then, how wrong we can be with our guesses about someone who is a stranger, someone whose life experience is very different than our own, someone who is excelling in a very different profession than our own. Our assumptions about our members can be wrong.
We see the data footprints members leave in their wake, the renewals, the ways they engaged, the content they read. We know what they do, and we assume how they feel. Interpreting this data we unconsciously make assumptions about their behaviors. We imagine why they join, why they renew, and why they engage. We then use these assumptions to inform our plans moving forward. It’s not the data but our assumptions that drive our decisions and actions. But our assumptions can be wrong.
Data we assume the is scientific. We assume it is infallible. We believe the data represents fact. And this might be true, but we don’t act on the data alone. We act on our interpretation of the data.
The only way to overcome this bias is to find out what is driving our member’s actions, goals, fears, and challenges. It is not enough to look at the data and see what our members are doing. We need to find out why. Why do they think this? Why are they acting this way? Why aren’t they acting? We need to learn the answers to these questions directly from them.
If you feel you don’t have unbias critical answers to inform significant changes, it is time to look beyond the data. It is time to discover your members’ stories.