Michael Li: We hear a lot about data science, AI, machine learning.
These are all things that are in the milieu right now. I think they fundamentally point at the same idea, the same concept, around you might call it machine intelligence—where it’s about how do you use computers and the vast amount of data that’s out there, that kind of big data, and then leverage that to make more intelligent decisions as an organization, as a government, as a nonprofit.
This really comes from a few major secular trends that are happening.
One is the plummeting cost of computation and the plummeting cost of storage.
So now we have the capacity to store that data relatively cheaply and be able to process that data relatively cheaply.
And then the other major trend is that everyone’s walking around with smartphones. Everyone’s interacting with the internet for a large portion of the day. And so we’re able to capture huge parts of the human experience and digitize that information and store it in the cloud.
So when we have all these connected devices that are measuring us, we can actually say a lot about human behavior. And that’s actually really, really fascinating.
And from that we’re able to create products, services that are so much more rich and so much more personalized than we’ve been able to do before.
And so if you think about maybe even the simplest example, it might be something like Netflix with a recommendation engine that’s able to serve up content in a very targeted way so that they give you, they show you out of their library (of probably millions of possible videos for you to watch) the five to ten that you’re most likely to want to watch.
And they can do this from what’s called “look-alike analysis” where they would look at what other people, who have watched a similar set of videos as you have, how have they rated those videos. How much they’ve liked those videos.
And then see what other videos those people have liked that you haven’t yet watched. And that’s probably a good candidate for a video that you should watch.
So that kind of look-alike analysis—or if you’re a data scientist you probably call that a recommendation engine—That’s actually a very powerful technique and it’s sort of very fundamental to a business that has tens of millions of videos and they know you’re only going to watch one tonight.
How do you pick out that one good video so that’s not such a huge search problem for a consumer but it’s actually a pleasurable experience for them? And that has implications beyond Netflix.
If you think about a company like Amazon, that’s incredibly important for them. They have billions of items in their store. You need to be able to figure out what to buy and so they can tell you the right item that can maybe get you to buy something that you otherwise wouldn’t have purchased. And that has a direct impact on their bottom line. And it also makes consumers happier, right?
It helps you reduce the amount of time you spend searching for products and services. So I think these kind of data-enabled services where companies can give you what you want when you want it, that’s becoming increasingly powerful within the kind of consumer market and it’s becoming increasingly the standard.
So I think what we’ve seen is that for a lot of legacy enterprises that are not digital first, that haven’t been able to embrace data and data science, there’s an almost a kind of an adversarial relationship between the consumer and that product or service, where you’re saying as a consumer, “Hey, I have this great experience when I’m interacting with Google or Netflix. They seem to give me what I want. Why can’t you give me what I want?”
And when you experience that there’s a lot of fear on the part of those companies, the legacy companies, that their ability to sort of maintain that market is going to rapidly evaporate and it’s going to deteriorate at a really quick rate if any of these sort of digital companies enter that market, because they understand how to leverage data. They understand how to use the information they have about consumers to give consumers a fundamentally better product.
There’s obviously a lot in the news about Cambridge Analytica, Facebook and how we societally deal with data and how we should deal with data. Should there be regulation? Should we have more privacy protections? And I think these are really interesting and valid questions that we as a society should ask. And it seems that we’re entering a period where—that certainly within Europe with the passage of GDPR—there’s increasing reluctance to just say, “Facebook, Google you can have free rein over all of our data in an unchecked way.” And I think we’ll see what happens as we move forward with regulation and this kind of consumer protections.