I've never been a big TEDtalks fan, but recently I've been exploring some of the episodes, partially based on peer pressure.
— Glenda Morgan (@morganmundum) January 15, 2016
In the process I ran across a talk from Sebastian Wernicke, who has a bioinformatics background but now seems to specialize in giving talks. The talk in question is "How to use data to make a hit TV show", which starts by looking at two data approaches to binge TV production - Amazon's use of data analysis to choose a new show concept, leading to Alpha House, and Netflix's use of data to look at lots of show components but then to let humans make conclusions and "take a leap of faith", leading to House of Cards. The anecdotes set up his description of where data fits and where it doesn't, and this mirrors what Michael and I are seeing in the use the broad application of personalized learning.
We have described in our most recent EdSurge article:
Bottom Line: Personalized learning is not a product you can buy. It is a strategy that good teachers can implement.
While Wernicke is not addressing education, he describes the same underlying issue in memorable way (starting at 8:18 in particular).
Now, personally I've seen a lot of this struggle with data myself, because I work in computational genetics, which is also a field where lots of very smart people are using unimaginable amounts of data to make pretty serious decisions like deciding on a cancer therapy or developing a drug. And over the years, I've noticed a sort of pattern or kind of rule, if you will, about the difference between successful decision-making with data and unsuccessful decision-making, and I find this a pattern worth sharing, and it goes something like this.
So whenever you're solving a complex problem, you're doing essentially two things. The first one is, you take that problem apart into its bits and pieces so that you can deeply analyze those bits and pieces, and then of course you do the second part. You put all of these bits and pieces back together again to come to your conclusion. And sometimes you have to do it over again, but it's always those two things: taking apart and putting back together again.
And now the crucial thing is that data and data analysis is only good for the first part. Data and data analysis, no matter how powerful, can only help you taking a problem apart and understanding its pieces. It's not suited to put those pieces back together again and then to come to a conclusion. There's another tool that can do that, and we all have it, and that tool is the brain. If there's one thing a brain is good at, it's taking bits and pieces back together again, even when you have incomplete information, and coming to a good conclusion, especially if it's the brain of an expert.
And that's why I believe that Netflix was so successful, because they used data and brains where they belong in the process. They use data to first understand lots of pieces about their audience that they otherwise wouldn't have been able to understand at that depth, but then the decision to take all these bits and pieces and put them back together again and make a show like "House of Cards," that was nowhere in the data. Ted Sarandos and his team made that decision to license that show, which also meant, by the way, that they were taking a pretty big personal risk with that decision. And Amazon, on the other hand, they did it the wrong way around. They used data all the way to drive their decision-making, first when they held their competition of TV ideas, then when they selected "Alpha House" to make as a show. Which of course was a very safe decision for them, because they could always point at the data, saying, "This is what the data tells us." But it didn't lead to the exceptional results that they were hoping for.
So data is of course a massively useful tool to make better decisions, but I believe that things go wrong when data is starting to drive those decisions. No matter how powerful, data is just a tool . . .
We are not the only people to describe this distinction. Tony Bates' latest blog post describes a crossroads we face in automation vs. empowerment:
The key question we face is whether online learning should aim to replace teachers and instructors through automation, or whether technology should be used to empower not only teachers but also learners. Of course, the answer will always be a mix of both, but getting the balance right is critical.
What I particularly like about the Wernicke description is that he gets to the difference between analysis (detailed examination of the elements or structure of something, typically as a basis for discussion or interpretation) and synthesis (combination or composition, in particular)1. Data is uniquely suited to the former, the human mind is uniquely suited to the latter.
This is not to say that the use of data and analytics can never be used to put information back together, but it is crucial to understand there is a world of difference in data for analysis and data for synthesis. In the world of education, the difference shows up in whether data is used to empower learners and teachers or whether it is used to attempt automation of the learning experience.
- Using Google's definitions. [↩]