Page 10 - Building Big Data Applications

4 Building Big Data Applications

in situations like this where you can find a lot of knowledge in connecting the
dots with data and create a powerful set of analytics to drive business trans-
formation. Business transformation does not mean you need to change your
operating model but rather it provides opportunities to create new service
models created on data driven decisions and analytics.
The company that we are discussing here, let us assume,decides that the current
solution needs an overhaul and the customer needs to be provided the best quality of
service, it will need to have the following types of data ready for analysis and usage:
Customer profile, lifetime value, transactional history, segmentation models, social
profiles (if provided)
Customer sentiments, survey feedback, call center interactions
Product analytics
Competitive research
Contracts and agreementsdcustomer specific

We should define a metadata-driven architecture to integrate the data for creating
these analytics. There is a nuance of selecting the right technology and architecture for
the physical deployment. A few days later the customer calls for support, the call center
agent is now having a mash-up showing different types of analytics presented to them.
The agent is able to ask the customer-guided questions on the current call and apprise
them of the solutions and timelines, rather than ask for information; they are providing a
knowledge service. In this situation the customer feels more privileged and even if there
are issues with the service or product, the customer will not likely attrite. Furthermore,
the same customer now can share positive feedback and report their satisfaction, thus
creating a potential opportunity for more revenue. The agent feels more empowered and
can start having conversations on cross-sell and up-sell opportunities. In this situation,
there is a likelihood of additional revenue and diminished opportunities for loss of
revenue. This is the type of business opportunities that Big Data analytics (internal and
external) will bring to the organization, in addition to improving efficiencies, creating
optimizations, and reducing risks and overall costs. There is some initial investment
spent involved in creating this data strategy, architecture, and implementing additional
technology solutions. The returnon investment will offset these costs and even save on
license costs from technologies that may be retired post the new solution.
We see the absolute clarity that can be leveraged from an implementation of the Big
Dataedriven call center, which will provide the customer with confidence, the call center
associate with clarity, the enterprise with fine details including competition, noise,
campaigns, social media presence, the ability to see what customers in the same age
group and location are sharing, similar calls, and results. All of this can be easily
accomplished if we set the right strategy in motion for implementing Big Data appli-
cations. This requires us to understand the underlying infrastructure and how to leverage
them for the implementation. This is the next segment of this chapter.