overheard
an Information Management interview with
Jim Kobielus
Senior analyst at Forrester Research
Embedded Analytics and Next Gen Data
Business intelligence and performance management that will
increasingly be tuned for the workforce
Why are we hearing about embedded
analytics and what are we talking about?
What we’re often talking about is analytics that
are embedded in applications or embedded in
business processes or in the decision cycle of the
company. Another way of looking at it is where
analytics are embedded or executing in some
kind of platform where analytics aren’t traditionally executed, such as in the database or in
the data warehouse.
Why are we interested in embedded
analytics?
A data warehouse is fundamentally a very spe-
cialized database, and a database traditionally is
all about keeping tables and records and indexes
to support queries and updates, but it is not a
place where you do a lot of analytics and process-
ing. But if you embed analytics in that database,
it becomes a computing platform sort of like
an application server where we use stored pro-
cedures and user-defined functions. Now there
are new ways to embed analytics to execute on
databases, data warehouses and new approaches
like MapReduce and Hadoop. In the new world
on the data warehouse side, embedded analytics
is referring to an open framework that is differ-
ent from the way we did stored procedures and
user-defined functions on traditional propri-
etary platforms.
Why are we moving in that direction?
For one thing, the data warehouse is becoming
the biggest, baddest repository pool of comput-
ing power, memory, I/O, bandwidth and storage
in your company. It’s a powerful platform where
more and more data will come to stay, hun-
dreds of terabytes and petabytes that are hard
or wasteful to slosh around between applica-
tion platforms. Rather than move all that data,
we can think about moving the applications to
that. In that way the data warehouse is almost a
gravitational force with satellite applications ex-
ecuting natively there. As the data sets get larger
and as the applications get more demanding in
terms of compute power, it just makes sense to
move more of this logic to this new generation,
big data warehouse.
We used to look at data warehouses as
a place to put historical data that didn’t
have the same currency as the operational data that’s closer to real time.
The data warehouse has evolved to be architected
for real-time applications and a real-time ingesting query. Teradata and other vendors have gone
deep in this direction. So we’re now seeing the
