Big Data Reduction 3: From Descriptive to Prescriptive

Hello Andrew,

Thanks for continuing the conversation.

I don’t mind thinking big. And I do think big. I just have to be careful who I talk to when I’m thinking big. To me, there is thinking big realistically, and thinking big day dreaming.

If you like, I invite you to take a look at a piece I wrote for Wharton Future of Advertising. That is one example of my way of thinking big realistically.

Thinking big day dreaming would be anything and everything is possible in the future.

Not only can analytics systems prescribe in real time, it can actually do it faster than real time by predicting what will happen in the future, Moreover, with enough data from bio-sensors on the mobile device + computing power, machines will be able to anticipate my actions before I actually take any action. So it would know even the choices I make before I make them. That basically allow everyone to map out his entire life and prescribing every action he need to take in his life as soon as he is born. You might start to think that I’m just day dreaming…

Alright maybe that is too big, so let me tell you how this can happen more realistically. How about we give someone a mobile device that can communicate with implanted neural and biochemical sensors transmitting signal the mobile device at birth. Then the machine can learn all the neural and biochemical patterns as he grow up and learn all the choice he make, learn his taste and decision making pattern. Then when he’s 18, he can say, “I want to retire in France countryside, have my own vineyard, and make my own wine when I’m 68 years old.” Then the machine will map out all the actions he need to take in order to achieve his goal. After all, the machine had 18 year to learn about how this person work at the neural and bio-chemical level. Moreover, as he takes these actions, the machine continue to update the possible future mapping out the next action he need to take to best achieve the life he want at 68. It’s definitely not impossible. With the internet of things and ubiquitous sensor networks, this not too far fetch.

That said, I totally agree that real time offers a lot of opportunities, b/c it’s something that we can’t do before. But give it 10 years or so after real time prescriptive analytics is made popular. Then it will just be another tool. And there are problem that need real time and there are other problems that need long history. Which analytics people chose will fall back to what they are trying to achieve, rather than going to the new shiny toys.

Same for this life mapper prescriptive system too. Even if it exist 10 year from onw, after 20 year, it will just be a tool, and there will still be problems that will use simple plain of descriptive analytics. Right now prescritive problems are probably less than 1%, so we see a huge opportunities. But once it is commoditized, the type of problem that use descriptive, predictive and prescriptive analytics will probably be pretty even.

Yup, the next generation will always out do us, b/c they have all the knowledge and technologies we created. No doubt on that.

Thanks for the interesting conversation.

Most of my physicist and mathematician friends are more of a realist. So thank you for prompting me to think bigger.

Hope to see you again on lithosphere.