As the Telecom industry goes through phases of troughs and crests, the one constant that marks the industry is the abundance of the types and volumes of data that it has. While the industry had great expectations of Big Data in the initial years, this vibrancy was followed by a period of doubt. Now, as things look up again, let us see how the trends have evolved over the last six years.
The Basics
IBM had, in a 2012 study on an overall basis across industries determined that a successful Big Data implementation required the delineation of business requirements first and the setting up of the implementation to be followed. However, the study also spelled out the need to upgrade infrastructure over time and to focus on the achievement of short-term results with prime importance needing to be accorded to customer-centric results.
A Different Strategy
In the following year, in 2013, a report by Booz & Company, now known as Strategy&, outlined the need to adopt the opposite strategy, of beginning with the data and then moving up to identify ways to tap potential rather than beginning with a business requirement and then working down. The Strategy Consulting firm based its recommendation to overcome the situation of benefits of Big Data not having been achieved in a concrete manner at that point. The firm had recommended the setting up of pilot programs to implement this bottom-up strategy. The report specified benefits of Big Data through
- Real time inspection of packets to improve quality of performance of the network
- Real time analysis of call data to identify fraudulent activity
- Marketing activities based on location of customer and events on social media
- Monetize anonymous customer insights
Internal versus External
Arthur D. Little specified in a 2015 report on Big Data in Telecom that the monetization possibilities within a Telecom organization are far higher, than those through the sale of data to external customers or partners. The report by the Management Consultancy identified the crucial aspect of the business needing to make the strategic decisions that would lead to the success of a Big Data implementation. The report stressed that Telcos need to determine the exact prospects for achieving benefits in B2C, B2B and third-party, and for the strategy thus worked out to be implemented. Strategic planning was noted as being key to exploiting potential and generating returns by Big Data implementations.
The deployment of Big Data identified in the Arthur D. Little study were
- Ability to find out the exact cause of call drop
- Improve data plan renewal rates through analysis of renewal patterns towards finding out when best to contact customer etc.
- Create customized marketing plans by monitoring of social media
Projections for the Near Term
Ericsson in an October 2015 report, while estimating 50 percent Compound Annual Growth Rate for the Big Data based Telecom analytics sector and a market size of $5.4 billion by end-2019, identified three basic components of Big Data implementation for Telecom companies. They were optimizing operations, enriching operations and the creation of new business opportunities. In the third component, B2B revenues from a third party and the accompanying issue of privacy were discussed. The inadequacy of regulations to provide sufficient robustness was raised and privacy was recommended to be kept in sight through every stage of the development of a product or service.
Current State of Big Data Analytics in the Telecom Industry
In a June 2016 article, McKinsey & Company reported that 30 percent of survey respondents (making up a quarter of industry revenue) had invested in Big Data and analytics. In this group of 80 companies out of a total of 273 Telecom companies, those where profits were up 10 percent attributed the results to the investments. Most reported slight increase in profits while negative profit growth reported by a few was attributed to poor quality of data, and lack of talented manpower. Returning to the Arthur D Little Study, it had recommended companies with lower maturity levels in data processes and companies without adequate manpower to go in for a centralized operational mode. More mature companies that had the skilled manpower were recommended to go in for a distributed operating model.
McKinsey found that strategy was absent in the implementation of Big Data initiatives among the companies that had made the investments, with the management of Big Data “being at too low a level to make it a strategic priority.”
The use instances detailed in the McKinsey article were:
- Predict time brackets with heavy use of network, for example on account of video download
- Identify customers likely to defect by analysis of network usage data, and call center and social media data
Analytics Solutions
Checking out a few vendors with products for the Telecom industry
- Teradata’s ‘Insight Advantage Integration’ provides capabilities of device analytics, subscriber behaviour and usage patterns, location analytics, social network and media analytics, eCommerce and channel analytics and network and service quality analytics. The company’s value addition propositions are Next-Gen services and products, Next-Gen customer experience management, monetization of infrastructure and data and improvement of Opex and Capex performance.
- HortonWorks ‘Big Data Solutions with Churn Prediction and Analytics’ provides capabilities to analyze call detail records, service equipment proactively, rationalize infrastructure investments, recommend next product to buy, allot bandwidth in real-time and develop new products.
The Way Forward
In summary, what the industry needs is a complete revamp of operational style. With complete focus on data, Telecom companies can step into a Big Data environment where analytics are the key quest and activity. Each company can make the shift in keeping with its operational situation so that the best possible integration of Big Data tools and processes are brought in. In this new paradigm of operation, Telecommunication companies can create better value for their customers and themselves if they get all the basics right. This includes the data analysis platform as well as privacy and security of all records. The crafting and positioning of a strategy is essential both for in-house or external monetization as well as for an implementation that creates data-collection initiatives or one that works with existing data to scour for business improvement potential.
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