The big buzzword in business is still “data”. Or better yet, “big data”. Or more complicated sounding, “analytics”. Better than just analytics is “advanced analytics” which is like analytics but more advanced. With all that data, a bunch of “algorithms” are figuring everything out.
Take your pick. Sure other trends have picked up in the last few years—how about disruption anyone?—but since I started business school, data (or one of those words related to it) is everyone in business’s obsession. Companies launch whole businesses now who run off business models entirely related to collecting customer data. Facebook’s Cambridge Analytica revelations just brought this to the fore.
Especially the streaming video services. They get so much data that was never there before, including the entire viewing history of a customer (and their shopping history on Amazon). So when I talk to MBA students—either at alumni events or recruiting events or in a classroom—I invariably get asked this question: “How did [your streaming platform] use data to pick which TV series and movies to make?”
I can’t help myself from sighing. And being slightly sarcastic. So instead of answering, I usually flip the tables.
I ask a simple question (if say I am in a classroom). Something like, “Let me ask you, when you decided to take this course, what data did you rely on to make the decision?” Usually, the answer is some stumbling around, ultimately ending on someone had recommended the course to them. I point out that this is data, a qualitative piece, but still a personal recommendation.
Notice that word I paired the word “data” with “decision”. So let’s use a real-world example. From my life, but anonymized to protect the innocent.
A group of Hollywood executives are sitting around a table. They only have the budget to renew one more TV show to release in the upcoming year. The decision is around two shows. The first I’ll call, “Cop Show”. It’s a show about police officers. Yeah, creative title. The other show is called, “Awards Show”. If you went into a laboratory to make a TV show to win awards, this was it.
Now, we have to be clear, there is no lack of data. The studio execs have reams of it. They know have how many customers watched each show. They know what shows customers rated highly. They know what critics thought. They have surveys of customers and have conducted focus groups. They think one show will probably stay more popular, but will never win awards. The other show will likely win awards, but customers didn’t watch the first season. But they have a ton of data to draw on at that table to make these conclusions.
Of course, in addition to all the data on the table, there is a lot of data off the table. Pieces of information influencing the executives, but that doesn’t make the strategy powerpoint that justifies the decision. For instance, some of the development execs have friends helping make one of the TV shows. Some development execs are considering whether a popular but uncritically acclaimed show will help their chances at getting their next jobs. And the marketing execs want the easiest show to market.
To top it off, some people just like one of the shows a lot. It’s their favorite show on television. That happens.
This is why I asked the students about the “decisions” they had to make. The execs have a ton of data…how do they use that data? Data doesn’t make decisions, the people do.
Big decisions, like what MBA school to attend, have a lot similarities with picking a TV show at a network, which is why I used it as an example. A prospective MBA student has tons of data to pour through. Though a lot of that data is qualitative. And some of it is on the table, and some is subconscious. And a lot of it has vague predictions about the future: which school will I enjoy the most? Which school will help me get the best job? And in what field? That’s making assumption about the future. Every MBA student had to decide where to go to business school; likely, they didn’t have a data-driven process. They picked the highest ranked school from a list of school rankings whose methodology they probably barely understood.
Because, honestly, people don’t really like using data to make decisions.
Let me say that again, because it is the biggest myth in our business world right now: people don’t like using data to make decisions.
Most huge corporations already have a well-worn, time-tested, established system to make decisions called: HIPPO. Highest Paid Person’s Opinion. (I’m not the first to write this.) Basically, you have a CEO making anywhere from $1 million to $50 million dollars. What if you gather a bunch of data that says they’re about to make a huge strategic mistake? Will they change their mind?
Of course not! They’re the boss.
To reverse course would admit that they were wrong. And this goes all the way down the chain. Say you are a middle manager who is convinced to make some key decision. You decide to change how your team processes something or other related to payments. But you also have a Business Intelligence team. A BI team that developed a sound forecasting methodology, with clean, reliable data and that team’s new methodology argues against your strategy. Whose opinion do you take, the data or you?
You pick you! And that’s what development executives not just at the streaming service I worked for, but for all of Hollywood, do on a daily basis. It’s what financial executives do when making investment decisions. It’s what corporate strategists do when determining their strategy. You can develop a data-driven approach to making decisions, and really think about what decisions you make, or you can go with your gut.
This isn’t to say business folks don’t use data. They have tons of data to read. Like those studio executives around the table. They make reports justifying strategic decisions that are loaded with data. And they may even listen to it a bit. Let it inform what they think, at some level. But the key is executives are the ones making the decisions, not methodologies. Not the data. And with tons of data, more data than ever, it is even easier than ever to construct a narrative that you’re making sound decisions.
This is why I return back to the core theme of this blog: it isn’t about data, it is about decision-making. The goal isn’t to just have data; it’s to use that data to make better decisions. Making decisions is about much more than regression analysis or random forests or neural networks or some other complicated algorithm. It is about asking the right questions, to even know what decisions you are making. Then you figure out your process, your methodology, your data and how you will measure success. Then you let the process make the decision, not your gut.
You cannot make wise decisions without data, but instead of focusing on the “data’ we need to focus on the “decisions”. This idea will come up so often, when I write about entertainment strategy, that I felt the need to make it my first “theme” of my writing, a point I will return to again and again.
As an undergraduate, I heard a description of hierarchy that has stayed with me Basically, the professor said, “salary in an organization is determined by the value of the decisions you make. If you’re decision is, ‘Do I empty this trash can?’, you get paid the salary of the janitorial staff. If you’re decision is, ‘Should we enter China or India as our next market?’ you get paid like a CEO. One of those decisions is valued at cents (the trashcan) the other in millions or billions (entering new markets).”
This site is concerned with those decisions as they relate to entertainment. And making them better.
Data isn’t the solution, better processes and decision-making are. And a lot of that better decision-making will say, ironically to this entire post, to use more data in places where it isn’t being used at all (lots of parts of entertainment) to help make better decisions. In the long run, we can even identify who is making the best decisions and reward them, instead of the executives at the top who likely aren’t making the best decisions, or ensuring they even have a good decision-making process.
Hopefully this website, over time, will help you and your company do that better.