Modern Telecom Architectures Built with Hadoop

This is the third in our series on modern data architectures across industry verticals. Others in the series are:

Modern Healthcare Architectures Built with Hadoop
Modern Manufacturing Architectures Built with Hadoop
Modern Telecom Architectures Built with Hadoop
Many of the world’s largest telecommunications companies use Hortonworks Data Platform (HDP) to manage their data. Through partnership with these companies, we have learned how our customers use HDP to improve customer satisfaction, make better infrastructure investments and develop new products.

Hortonworks partner Teradata recently gave some use case examples in this video about how Verizon Wireless uses Teradata in combination with Hortonworks Data Platform to keep their customer churn below 1%.

Rob Smith, Verizon Wireless’ Executive Director for IT, describes how his team uses their discovery platform to improve customer interactions, by:

Identifying better ways to communicate with customers about their payments
Analyzing social media to better understanding customer sentiments about Verizon policy changes
Tailoring marketing communications to each customer’s individual needs
Smith describes how this new customer insight helps their IT and Marketing teams align business objectives (for the benefit of Verizon customers).
Deliver a Modern Data Architecture with Hadoop
Our other telco clients have identified their own Hadoop use cases, but there are similar patterns in the Hadoop data architectures that they all build. Those data architectures allow telcos to store new types of data, retain that data longer, and join diverse datasets together to derive new insight.

The following reference architecture diagram represents an amalgam of those approaches that we see across our telco clients.


With their Hadoop modern data architectures, telecommunications companies of all sorts can execute the following six use cases (and many more).

Analyze Call Detail Records (CDRs)
Telcos perform forensics on dropped calls and poor sound quality, but call detail records flow in at a rate of millions per second. This high volume makes pattern recognition and root cause analysis difficult, and often those need to happen in real-time, while the customer waits on the line listening to hold music. Delay causes attrition and harms servicing margins.

Apache Flume can ingest millions of CDRs per second into Hadoop, while Apache Storm processes those in real-time and identifies any troubling patterns.

HDP facilitates long-term data retention for root cause analysis, even years after the first issue. This CDR analysis can be used to continuously improve call quality, customer satisfaction and servicing margins.

Service Equipment Proactively

Transmission towers and their related connections form the spinal chord of a telecommunications network. Failure of a transmission tower can cause service degradation, and replacement of equipment is usually more expensive than repair. There exists and optimal schedule for maintenance: not too early, nor too late.

Apache Hadoop stores unstructured, streaming, sensor data from the network. Telcos can derive optimal maintenance schedules by comparing real-time information with historical data. Machine learning algorithms can reduce both maintenance costs and service disruptions by fixing equipment before it breaks.

Rationalize Infrastructure Investments
Telecom marketing and capacity planning are correlated. Consumption of bandwidth and services can be out of sync with plans for new towers and transmission lines. This mismatch between infrastructure investments and the actual return on investment puts revenue at risk.

Network log data helps telcos understand service consumption in a particular state, county or neighborhood. They can then analyze network loads more intelligently, with data stretching over longer periods of time. This allows executives to plan infrastructure investments with more precision and confidence.

Recommend Next Products to Buy (NPTB)
Telecom product portfolios are complex. Many cross-sell opportunities exist for the installed customer base, and sales associates use in-person or phone conversations to guess about NPTB recommendations, with little data to support their recommendations.

HDP gives telco sales people the ability to make confident NPTB recommendations, based on data from all of its customers. Confident NPTB recommendations empower sales associates (or self service) and improve customer interactions.

A Hadoop data lake reduces sales friction and creates NPTB competitive advantage similar to Amazon’s advantages in eCommerce.

Allocate Bandwidth in Real-time
Certain applications hog bandwidth and can reduce service quality for others accessing the network. Network administrators cannot foresee the launch of new hyper-popular apps that cause spikes in bandwidth consumption and slow network performance. Operators must respond to bandwidth spikes quickly, to reallocate resources and maintain SLAs.

Streaming data in Hadoop helps network operators visualize spikes in call center data and nimbly throttle bandwidth. Text-based sentiment analysis on call center notes can also help understand how these spikes impact customer experience. This insight helps maintain service quality and customer satisfaction, and also informs strategic planning to build smarter networks.

Develop New Products
Mobile devices produce huge amounts of data about how, why, when and where they are used. This data is extremely valuable for product managers, but its volume and variety make it difficult to ingest, store and analyze at scale. Not all data is stored for conversion into business info. Even the data that is stored may not be retained for its entire useful life.

Apache Hadoop stores more data for longer, economically. This puts rich product-use data in the hands of product managers, which speeds product innovation. It can capture product insight specific to local geos and customer segments. Immediate big data feedback on product launches allows PMs to rescue failures and maximize blockbusters.