So guess what! It's time for Five Minutes with Ingo! Excellent! So, here we are at Strata+Hadoop
in San Jose and it's been a great show. A lot of activity, A lot of people. Tell
me a little bit about what you're hearing on the show floor, really. What's the hot topics?
It's great, even if you compare this to the last time, in previous years. So in the previous years everything was
about understanding Hadoop, understanding big data, understanding those big data infrastructures.
This year it's a reality and people have those data infrastructures in place.
That's the way people store the data. But they really need to make value out of this. And
we talked about this a couple of times already. Analytics is really one way of, of doing this.
Right, right but the big problem I'm seeing, at the same time, if I'm walking the floor here and talking to people,
is also that they say, 'Well, it's just not efficient enough. It just takes so long,
it took us ages to actually get the data in there to some data infrastructure.'
Now, we were to make or create value out of this with analytics. And how can I do this
in an efficient way? Okay so that's something I'm hearing here constantly.
So, previous years was understanding and defining what big data is, what it means to
organizations, how do they actually do something with it. Now they're trying to
figure out ways to get efficiencies out of that so they can get to those insights,
get to that action faster. Exactly, okay, so how do we do that, how do we help them understand that?
So, it's, well, just think about some, some use case. Um so of course we are always going to talk about
predictive analytics here. A use case could be - let's use some standards churn prediction, for example.
Churn prediction - okay you are like, a telecorp. You are losing like 100% of the customer base. Yeah, you would like to
predict who are the customers who are churning, who are switching over to the competitor.
And if you can do this prediction, maybe you can make them stay. Yeah, that's a big job.
It's a big job, exactly, and especially if you're a large telecorp, you have like 50 million, 100 million customers.
You can't do this by hand, so you have all the big data, you have all the important data, you have all these kind of
things in your, in your big data storage. But how can you build an analytic process on top of that?
Now is the fun thing: nobody should believe that they're the first person who are actually solving
a churn prediction problem for the very first time.
Well, let's face it people, our data is special, but it's not that special.
The problem is not that new, so other people have been doing this before.
So I would like to compare this with this beautiful picture here. Okay, that is the Mona Lisa.
That is correct and look at her here: if I would ask you now to paint the Mona Lisa,
and if you use colors and paint and canvas and everything else, you could really do this?
No way. That's what I figured, but there is a good way how to do this. You see this on the back of this paper here?
So, you know this concept of paint by numbers. I do. So you just have a few numbers or colors
like those here, and you just use those colors in places where, where you find the correct number
for the color. So this is something where you're following a certain path, a certain template.
So other people, other data scientists actually have been already thinking about how to solve a churn prediction.
Problem and now you can use this template, and you fill your own colors. But you always have this
chance actually to, to change things so if you don't like her brown hair, well, make her a blonde. Why not? Okay,
but that looks, so I get this but this looks very basic to me. I mean I could do that, but
my data is a heck of a lot more complex than that - or at least I would think so -
if I'm dealing with customer churn. So, but let's think about how we are actually, as data scientists,
really solving this type of problem. So let's say you found a model which is pretty
good. So get, you get whatever, an 80% or 90% prediction accuracy, and now you
would like to optimize things and would like to get a little bit better. So let's have a detailed look.
Let's zoom in a little bit. Look at her beautiful smile here, gorgeous.
Yeah it is, isn't it.
Ah, perfect I know.
So, if you zoom in to some details like, like the modeling step, yeah, and you would like to
figure out what is the best model, what is the best parameter setting
for a certain model type, you could actually make a zoom in and then you can still use the same concept of
of paint by numbers. Oh yeah, okay sure.
That's a little more complex. All right that's fine.
It is, so you still use the same paint by numbers here on this detailed level, use the same concept
of, of templates. But to become more efficient. So you build basically your analytical processes,
your solutions, on top of what other people did. And that's something which I think is now,
you can see more and more of, because people realize 'My gosh we're doing similar,
we're solving similar problems. We use similar data infrastructures.' You do, data sciences, but there are more out there.
Other people are doing this, and they can build their results on top of theirs.
So part of the idea is, instead of trying to start from scratch every single time when
you're trying to do something complex like the churn model is, either you
now find a way to accelerate what you need to do or find other people who have
done incredibly complex things and then figure out a way to build upon those
models. That's exactly what I mean. Perfect, that was easy. It was. Thanks!
Thank you! And this has been your Five Minutes with Ingo!