E-Fusion 2008
McDONALD: So, what we’re going to do is to move
straight to Pat. What I’m going to do is to transfer the pre-
sentation to Pat, and Pat, you should be seeing a prompt
on your screen. You should accept that prompt and then
we’ll see your screen.
PATRICIA SAPORITO: It’s my pleasure to talk about this
topic. It’s one that I’m extremely passionate about – about
analytics and also about dashboards and really the graphic
representation of data. As my title indicates, I’m now part
of our professional services organization so I no longer
have the pleasure of talking about what people should do,
but I actually go out and help them do it. So, basically,
defining their strategy and one of the key areas that we
work in is something that we call performance analytics
and I’ll explain what that is during the course of my pre-
sentation.
This is a slide that I use with customers and it’s really
talking about the insurance analytic evolution. What I’m
to information to knowledge to wisdom – using all of that
attempting to show is going from the left hand side to the
data. That’s the analytic evolution and the other thing that
right hand side of these chevrons, from the least advanced
I’ll just comment on is when we look at marketing, prod-
to the most sophisticated or most advanced areas. Look-
uct development, pricing and underwriting in particular,
ing across disciplines such as marketing, product devel-
companies tend to do a very good job of integrating their
opment, pricing and underwriting claims and accounting
underwriting and their pricing. What really they need to
and finance, but also looking at some of the enablers – the
do is integrate all of that so that your marketing programs
metrics and the data. Let me give you an example – in fact
are totally aligned and your product development is totally
Mark and I were chatting about this a little bit earlier before
aligned with your pricing and underwriting so that you’re
we started. So, if you take a look at marketing – initially
not spending marketing dollars marketing to companies
companies are very product centric and looking at prod-
that ultimately you don’t want to underwrite or can’t price
uct value, moving to customer segment value, moving to
effectively.
customer lifetime value and ultimately getting to actually
dynamic customer value.
Let me move on. If that’s the evolution that companies are
looking at, I challenge our listeners today to take a look at
These orange dots are my opinion of where I believe the
this at their leisure and think about where your organization
industry on a whole is and the dialogue that I have when
is and where you’d like to be and think about what’s hold-
I talk to a customer about this is tell me where your com-
ing you back. Just as there is the analytic evolution, there’s
pany is today and then tell me where you’d like to be
also a business intelligence evolution. The tools that we
because now we can start to have a discussion about
use – it’s certainly about the data, it’s about our strategies
what’s holding them back and what they have to do to
and what we want to do, but it’s also about the tools and
get there. More often than not, it involves those two bot-
they’ve evolved. In the 1990s they were expert tools for
tom layers. It’s either the metrics or the metrics are siloed
a few, this was really data-mart focused. They were very
much like the approach to their marketing or their product
functional and along with that obviously had functional
development. It’s siloed where there have lagging met-
limitations within the tools themselves. They were also
rics, so they’re not really looking at the predictive or the
organizationally and functionally oriented.
drivers. Then as they get more sophisticated they start
to look across business units or functions. They’re also
In the 2000s now we’ve gotten to a suite of tools, so it’s
linking those metrics to objectives and as they become
not just the BI itself, but now you’ve got the data integra-
more sophisticated they become more strategic, there are
tion and data management tools combined. And it’s not
objective links and now they start to use predictive drivers.
just the reporting tools; it would be the predictive modeling
They’re not just looking at the past; they’re looking at the
tools as well as the dashboards, etc., some other visualiza-
future. And ultimately they’re truly totally integrated – pre-
tion. And it became an information democracy. So, there’s
dictive models and metrics.
multiple applications going on, not just that single function
and also trying to get to now a data warehouse and hav-
There’s a similar path that goes on with the data. Again,
ing that single version of the truth. And of course that’s a
siloed, getting to more integrated data, starting to use the
challenge in populating, getting all that data integrated.
data, using it not just as we talk about the path from data
So, where this is headed in future – the near future and