Does your RTO look fat with all that data? It’s not alone. Globally, we are suffering an infobesity epidemic. With so much data at our fingertips, it’s hard to hold back and think about the consequences before jumping right in and devouring all those data points.
Just like a business that develops too quickly without enough resources to support its growth, so too has our abundant access to data resulted in an inability to process insights.
Data saturation has opened us up to the never-ending possibilities of answers we could discover; our potential seems unbounded. When analysing a question we find solutions that lead to more questions, which lead to even more answers—resulting in a question rabbit hole.
“What were we even originally trying to find out?”
A joint study run by psychologists at Princeton and Harvard explored how our minds decipher answers from different sources of information.
Participants were split into two groups. Group One read the following paragraph:
Imagine that you are a loan officer at a bank reviewing the mortgage application of a recent college graduate with a stable, well-paying job and a solid credit history. The applicant seems qualified, but during the routine credit check you discover that for the last three months the applicant has not paid a $5,000 debt to his charge card account.
Do you approve or reject the mortgage application?
Group Two were given the same paragraph but with a difference—they were advised that there were conflicting reports on the size of the debt, that it was either $5,000 or $25,000. They were given the option to approve or reject the application straight away, or wait until more information was available. Most students waited until they could have all the facts, which revealed that the debt was the same as Group One’s: $5,000.
Despite having access to the same data, 71% of Group One chose to reject the applicants, whereas only 21% of Group Two made the same decision.
Participants in Group Two were originally exposed to two different sets of data (a debt of either $5,000 or $25,000) and the alluring potential of more information. When they were provided with the same information as Group One, their minds had placed weight on the number rather than the original question—is approving this mortgage application a good business decision?
When we try to fill the gaps in our data, there’s the possibility of finding less meaning or value. There will always be more information, more analysis, and more reports that can be run, but we need to differentiate between questions worth exploring and questions that should be left for another time.
It’s human to want to know everything before making a decision; we’re neurologically inclined to want to fill in the gaps. When we do, we run the risk of analysis paralysis: losing our validity and making poor decisions.
Create meaning by incorporating structure to your data, and follow the steps below to begin analysing with intention.
It’s tempting to collect data just because you can, so begin with a meaningful question.
Identify the measures that will answer your question, and filter out the rest.
If your data is sourced from multiple departments, map out a complete view of which data connects with which department and who has access to it. Spend time in the situation where the data is collected to understand what the measures truly mean—this way you lose the chance of disconnected data. After all, it’s hard to tell a meaningful story when the reader doesn’t have access to the characters’ backgrounds.
Don’t get lost here—once you have answered your question, make sure that your interpretation checks the following boxes:
Think of it as a data diet plan for your organisation, cutting out the excess and keeping the nutritious data that will leverage your RTO’s decisions.
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