Hey Ingo! Hey, Happy New Year! Thanks, same to you! Alright, well, look so it's the new year
it's 2015, very exciting and this is, this is Five Minutes with Ingo so we're going to
spend five minutes hearing from you, hopefully not any more than a few
seconds hearing from me, and what we want to talk about today is: when you think
about the new year and you think about 2015 and the analytics, right, what are
the big challenges you see on the horizon for analytics? Okay, makes sense. Um. we first start on
the challenges - actually I would really like to start, spend one second on
the great stuff. The great stuff is really you don't you don't have the
needs to explain the logics to people any longer. Everybody gets it. Everybody
gets the need for analytics. It's really one of the biggest changes we have seen
in the, in the, in the past couple of years, that people are moving their own
business, pros and everything, in a way that actually is supporting them.
So that's great. Everybody understands the value of analytics and we don't need
to educate people on that. But unfortunately it's also coming, well with
a small problem. And it's coming at a price. And the problem here really is
that, actually well the demand is very high, but there are not many people who
can deliver this. Well, I can. I can give you actually, like a visual example for that,
that would be great. Let me do my best. Let's maybe take this pen - I'm hoping you can see this - so do me a favor. So what is that?
A triangle. Yeah right but actually for a guy from marketing that's a shocking lack of imagination. So let me help you a
little bit. So probably you're getting an idea... any second now. Safety cone, dunce cap, a mountain?
Okay now you get it in a second look at that.
Oh it's a unicorn! You got it.
So, and this unicorn really is the big problem. What do I mean by that?
The unicorn is really for me, is a metaphor for data scientist right now, and the reason for
that is a data scientist is a superstar programmer. They need a PhD in
statistics and of course they understand every business problem in the world.
Right, so difficult to find. I heard they're existing but they're really
difficult to find. And that's also true for unicorns so and that is really the
key problem for a for analytics. Today everything is moving away from
those backwards looking questions like "what happened in the last year" - who cares?
It happened already! So people would like to answer questions about the future.
They want to use predictive analytics to predict what's going to happen next, but
they think they need these, those really rare resources: the data scientists, those
unicorns, for doing so. And I say well, that is a huge problem. It's becoming a
larger problem. At the same time, the value of analytics is, well increases.
But it doesn't need to be and that's, that's sort of a prediction for this
year as well. I think this problem is there. But it can also be solved. So we
have the challenge of talent, right. Of finding unicorns or empowering other
people to become unicorns right? So how do we then balance the talent, the tools,
and the technology. And even more so how do you explain that in under two minutes?
Well we have two minutes left in Five Minutes with Ingo but let me try that so, so the
first thing is it's exactly all three elements. It's, it's really, it always
starts with technology. So you need to store the data and big data was really
going a long way there. So we now store basically everything on a very detailed
level, and that's great because if you're doing predictions about the future
you need this, this detailed level. Otherwise, all those predictions you are
doing are, well, more or less on a very higher level. They might support your decisions,
but that's not good enough. The biggest value of predictions is if
you're doing like millions of small predictions and automate them and
integrate them into business processes. So big data was important. So that's
about technology; fine, great. We have all the machine learning algorithms. RapidMiner
is like 250 of them, so there's a lot of stuff going on there as well. Good,
so technology bias - that's good. The next level's with the tools; and why are tools
so important? Because tools are really changing people and how they are working
with those kinds of, well, those different kinds of
technologies. So that's where RapidMiner also comes into play. It really
is a software, but is not so much about just throwing whatever those hundreds of
machine learning algorithms on those big data infrastructures. No, that is
important, but it's not, it's not all. What RapidMiner really is about is
actually about making this transformation. Transforming normal
people, people like you and me, that's transformed them into those unicorns,
into those data scientists. Because that's the biggest problem we have. We
only have those few data scientists here. But this is the need for analytics. So
how can we solve this without transformation? Well I don't think we can
solve it without, that's why we do what we do.
Awesome! And this has been your Five Minutes with Ingo. Thanks!