Databases Reference
In-Depth Information
In order to get an enterprise-wide view of their business, the telecom operators
had relied on data-integration initiatives moving data from distributed applications to
a centralized data repository and then through reporting solutions on top of this data
repository. They had developed metrics to review their state of business to identify trends
and patterns. But all of this analysis was more or less done in an offline mode, partly due
to cost implications of using solutions that can do real-time analysis and secondly due to
technology challenges to manage the volume and variety of data. A big data and analytics
platform solves these challenges in a cost-effective manner, and below we will discuss
a specific use case around improving customer experience.
As a subscriber, the plan you signed up for pretty much defines the services you
would get; however, your experience with the services is very dynamic. The primary
reason for this is “network performance” and your “usage patterns.” Thus, it's all the
more important to integrate network performance data with subscriber usage patterns
to understand what is happening in the complex intersection of network and services
(voice, data, and content). For example, while monitoring the network performance,
the telecom operator detects a spike on the load of the network; however, if you don't
correlate the network performance issue in real time with the segment of customers
who will experience degraded quality of service, you can imagine the kind of a customer
service experience you're delivering!
Figure
3-4
below illustrates how a big data analytics platform combines different
data types to deliver real-time analytics correlating streaming network data with
subscriber data.
Figure 3-4.
Big data analytics platform to deliver a 360-degree view of the customer
at real-time
