Database Reference
In-Depth Information
the CSR. The advice could be to answer the question differently, escalate the call
to the next level, provide specific incentives to the customer, or simply to be
more polite.
By intercepting, monitoring, and analyzing such calls in real time, this client
is able to vastly improve support effectiveness by taking immediate remedial
action to improve customer satisfaction. We want to emphasize that these
capabilities don't replace traditional offline analytics; rather, they augment
them by incorporating new varieties of data (voice in this case) and performing
analytics in real time.
Key Considerations for the Analytic Enterprise
Differentiating on analytics means using data-driven insights to enhance organi-
zational strategies
and
using analytics in ways that were previously impossible.
Limitations that used to restrict where and how organizations could run analytics
are being eliminated. Moreover, some of today's most “analytic enterprises” are
changing their analytic deployment models to find new competitive advan-
tages and differentiate themselves from their peers.
In this section, we share with you the key tenet for enterprise analytics:
if
you want to boost your Big Data IQ, you've got to start with the right ingredients.
We will describe these “ingredients” using real-life customer examples.
Run Analytics Against Larger Data Sets
Historically, performing analytics on large data sets has been a very cumber-
some process. As a result, organizations resorted to running their analytics
on a sampled subset of available data. Although the models that they built
and the predictions that they generated were good enough, they felt that using
more data would improve their results. They recognized that the sampling
process could sometimes lead to errors or biased conclusions.
Organizations that can run analytics, at scale, against their entire data sets,
definitely have an advantage over those that do not. Pacific Northwest National
Lab's Smart Grid Demonstration project is a great example of this. The project
hopes to spur a vibrant new smart grid industry and a more cost-effective reli-
able electricity supply, both of which are drivers of US economic growth and
international competitiveness. They plan to collect large amounts of data—
specifically event data from 60,000 metered customers across five states—and
run complex analytical models on them. Using IBM's Big Data technologies,
they expect to validate new smart grid technologies and business models.
