individual has a digital footprint. And by looking at that footprint you can pretty much

tell what drives them. That's new. When I went to school it was all about I have so

little data therefore let me come up with the fanciest tools to get as much information

out of it. Today it is about I have so much data how do I get to figuring out what does

that data tell you? And I think that's the shift. Those are the new sciences. And here's

the problem with data. The problem with data is I can do a lot of garbage in garbage out.

I can look at data and say here's the relationship between this and this. And that doesn't mean

the one thing causes the other, it's just that the two are linked. So a lot of the data

sciences are about this is correlated with this and therefore I'm going to assume they're

related. I'm going to assume they're correlated and that one drives the other.

But that's not the case. That can be pretty dangerous. So the best new techniques get

at the constant of causality, what causes what? That's what I really want to know in

data. And when you get to thinking about causality, causality is usually some human behavior that's

driving that. And so the confluence of behavioral economics and behavioral sciences that tells

you this is how humans behave and taking that, together with data, and trying to create these

behavioral causal models is really the Holy Grail of data analysis. And that's an art.

You can have lots of machines and people can do it, do it to a great extent, but the last

mile is always about understanding that human behavior. So today it's still an art. It's

a very powerful art. But the benefit of that is that you could actually aid individuals

in saying this is what's driving this behavior or this is what's driving what you're doing.

And by the way, do you want to correct that or keep doing what you're doing? That's so

important in the world of finance because people tend to procrastinate, sometimes they

don't need to look so far ahead as they should. These kind of sciences can add a lot to helping

individuals and their financial needs as an example.

When you look at data, when you look at what drives people and how they behave, there's

so many different types. The one thing we can be sure of is that it's a rarity to find

sort of the rational Adams Smith individual. There are fewer of those than people think.

Everybody has some different ways of behaving, but there are so many of them. For example,

pick a number and you may say 50. And if I follow up that with a question of how many

people do you think are in this building? It turns out your mind gravitate to 50 and

the answer is pretty close to 50. That's called anchoring. What you just heard influences

what you say and what your judgments are all about. That's a big bias. The other kind of

biases you see is people have invariably huge impatience factors, meaning that you value

that coffee right now a lot more than ten coffees tomorrow. So it's another factor,

which is instant gratification is something people want to pay for and they pay for a

lot and they will defer things like savings, as an example, as a result of that. You know

you get into confirmatory biases, when you believe in something and you don't say you

believe in it but it's implicit in what you're doing. When you go do analysis you search

for the data that confirms what you believe and then you say I've got this data that shows

this is right. Well, it turns out, well you were looking for it. So there are eight, nine,

ten of these things, there are many of these things. It doesn't mean that somebody is doing

something wrong, it's just the way we're wired sometimes and just the way we think. And the

more you expose it the better off we're going to be as individuals to think about whether

we should counter it or shouldn't counter it. At least we need to have that choice.

So, knowing what the biases are I think is important. Knowing which individuals have

which bias and making that clear to them and transparent, I think that's important too.

Having said that, people need to choose to counter these biases doesn't always happen.

So you've got all kinds of the health apps that tell you this is what's happening. Should

you be doing this? And guess what, people still do it sometimes. So modification of

behavior is a tougher challenge than just knowing that your behavior is driving certain

things. But having said that, there's some very clever ways of doing it and that's where

this art of behavioral science and economics come in. It's not about saying hey you're

a rational person, this is irrational behavior; you shouldn't be doing this, if it was you

would have probably already done it. It's about how you create the right nudges and

the right kind of incentives to drive people to do the right thing. Sometimes it's about

is this behavior that you have right for your kids? And that sometimes tilts how people

start behaving. So the art still lies in how you take that information and turn that into

nudges and other prompts that get people to counter the behavior.