Access to data is getting easier then ever for staff at every level from teacher to superintendent. However, access doesn’t necessarily lead to positive learning outcomes because many districts have an immature data culture. Here are the five data myths I’ve seen that can destroy a data initiative.
#1 Analyzing our data will solve all of our problems.
We must never confuse analyzing the data with solving a problem. Many times organizations use data to identify problems, but then never take the appropriate actions to solve the problem. We cannot analyze our way to learning.
#2 Data in the analytics system is the most important data we have.
It is human nature to over emphasize the value of data we have. This often leads us to frame our problems around the existing data without regard to whether or not it has a direct relationship to learning. We must always ask ourselves if the data we have is relevant to the problem we are trying to solve. If it is not then look for new data that would be more relevant.
#3 The more data I use, the stronger my analysis will be.
We often think that more is better. If we have a strong predictive model with three data points, then it will definitely be better with six data points. This isn’t the case. Adding additional data often creates a greater burden on the data collectors and complicates the data analysis. A quality data system will identify the smallest number of high impact data points that can be used.
#4 Our data looks horrible, we better not share this.
Many times data will reveal unfavorable details about a school or program. This often results in a gut reaction to hide the data or discredit the data system. Instead we must acknowledge the data and own it. We are responsible for the data in our systems and only we have the power to change what that data looks like in the future.
#5 The more disaggregated our data the better.
Most modern data system allow you to drill deep into your data. This can lead to data sets that are searching for a problem and “interesting but useless” insights that offer no actionable solution. Be wary of over analysis.