Charles Duhigg wrote the book Smarter Faster Better, in which he discusses the eight secrets of being productive in life and business. One of the eight principles is how to absorb data in a world plagued by information overload.
In this post we’ll discuss methods to improve our understanding of data. We’ll also run through the engineering design process before finishing with a public school case study.
How Do We Absorb Information?
So how can we break down data in a world of information overload? There are two main ways we absorb information.
First, we can organise data in mental structures. But we also absorb information by creating disfluency.
Organise Data with Scaffolding
Humans are good at absorbing information when we break data into series of smaller pieces. Most data can be broken down into countless categories and subcategories. Structuring data in this way is known as scaffolding.
Wine connoisseurs might use this technique to quickly choose wine from a long list at a restaurant. Whereas an amateur may have trouble considering each wine, connoisseurs might simply start with a good year.
With this mental structure, connoisseurs exclude most of the menu instantly and then choose between remaining options.
Having clear structures in our minds makes it easier to absorb new information. The new data fits neatly within our existing structure.
Create Disfluency
The other trick to absorbing data is creating disfluency. Disfluency is some sort of disruption in our communication.
We need to manipulate information in front of us to absorb it fully. And we do so by taking a mass of information and forcing it through a procedure that makes it easier to digest.
For example, using a new word in a sentence helps us remember it. And writing down the sentence helps us use the word in more conversations.
Instructions in hard-to-read font are easier to remember because we’ve had to grapple with it longer to understand it.
The difference between typing and hand-writing notes is the degree of disfluency. It’s far easier to type. And that means we can’t remember something we type as much as if we’d written it by hand.
To create disfluency and absorb data we need to manually grapple with the information.
A great way to create disfluency and absorb data is to ask yourself questions about the data and stipulate hypotheses.
That’s what one team at Chase Bank did to become the company’s most successful debt collectors.
Data Absorption for Debt Collection
Debt collectors have one of the most difficult jobs in finance. They call up people who are behind on payments and try to convince them to pay off their debt. Most people struggle with this task. That’s why one team stuck out from the herd.
This team had developed a system to understand their data better than anyone else.
Each morning they would start with a new hypothesis. They would use the data available to them to call relevant people to test out the hypotheses. By the end of the day, they would have a series of data points and could determine whether their initial hypothesis was correct.
They would then organise the data into folders to make their insights more manageable. Scaffolding helped them refer to insights from previous experiments.
Actionable Insights
The insights that resulted from months of this scientific method resulted in a streamlined daily schedule. They knew their debtors better than anyone.
One insight taught them to call home in the morning to speak to the woman of the house. The team found that women are more likely than men to pay back debts.
Around noon, they called the man’s office and say something along the lines of “I’m so glad I caught you before lunch”. This sentiment made the man feel important. He would then want to keep up the appearance and pay back their debt.
They learned to call single people around dinner, as they are more lonely and willing to talk then.
And after dinner they’d call people whose debts had dropped and ballooned over time. After a glass of wine, it’d be easy to remind them how good it feels to pay off debt.
Tricks like these developed over time by testing a new hypothesis everyday with relevant data.
They used the data they had available to become the most effective debt collectors at Chase Bank. We can employ the same idea in our lives by tracking data and keeping notes on our own experiments over time.
The Engineering Design Process
The engineering design process is a methodical approach to problem solving that comes down to five steps.
- First, we define the dilemma.
- Then, we start collecting data.
- Using the data, we can start brainstorming solutions.
- With a list of potential solutions, we can then debate the best approach.
- Finally, we experiment to find the best solution.
This is a system for making choices that helps us slow down and think.
We use it to learn from our experience and see alternatives from different perspectives. After all, every choice we’ve ever made is a data point.
Formal decision-making systems teach us to make questions look unfamiliar. These systems include flowcharts, series of questions and the engineering process. By forcing us to see alternatives, they allow us to take control of what’s going on inside our heads.
South Avondale Elementary – Case Study
South Avondale Elementary School collected data about their students’ performance for six years. The school even hired a team of experienced data visualisation experts to build dashboards. But the problem was that teachers never looked at the data.
There was simply too much information to take in.
As a result, South Avondale remained one of the worst performing schools in Cincinnati. And Cincinatti was one of worst performing cities in Ohio.
That’s when they started the Elementary Initiative. This programme aimed to reform how teachers made decisions. Educators would transform spreadsheets and statistics into insights and plans.
Each teacher would spend two afternoons a month in the data room, reviewing the class’s performance. While it’s much easier to see the class as one unit, the data room was a space to dive into individual kid’s performance.
The teachers ran impromptu experiments to test how various factors impacted grades. For example, if switching teachers for a module or using smaller reading groups helped test grades. Then they recorded their results on index cards and visualised them on a whiteboard.
In the Elementary Initiative, teachers had to engage with the data, instead of passively absorbing it. It was harder to process the data, but that’s what made the information stick. They were finally using all the data they had available.
This example shows the difference between finding an answer and knowing what it means.
Actionable Insights
The Elementary Initiative produced actionable insights by encouraging educators to dig into the data.
Beyond the initial data, teachers started collecting data about which questions different classes missed most. They realised different educators were more effective at teaching certain topics. That’s when the teachers traded their curricula.
Each grade started giving students living near each other similar assignments, even if they were in different classes. That way they could help each other on the bus ride home.
The initiative also contributed to the creation of hot pencil drills. These are multiplication quizzes held over the PA to combat poor math grades. The whole school would compete, and the quickest names were read over the PA as the reward. Math grades shot up after this initiative.
These insights resulted in the school’s average test score doubling within a year.
Data Absorption in a Nutshell
In a world of information overload, it can be difficult to fully absorb data. The problem is that there’s so much of it. The solution is to organise and manually dive into the data and create disfluency.
We can dig into the data by running experiments, like the debt collectors at Chase Bank or teachers at South Avondale. The goal is to produce knowledge with actionable insights that help optimise our performance.
With frameworks like the engineering design process, we can optimise our choices to learn from our experience. After all, every choice we’ve ever made is a data point.
Learn to absorb data better with disfluency, decision-making systems and Mind & Practice today.