How Telecoms Can Adapt to the Internet of Things

by   |   March 13, 2015 5:30 am   |   0 Comments

Don DeLoach, President and CEO, Infobright

Don DeLoach, President and CEO, Infobright

As the Internet of Things continues to evolve, many companies, especially network operators, are struggling with rapid increases in data volume. Data load predictions of 24 – and even 12 – months ago have long been surpassed. This creates a tremendous amount of strain on infrastructures that did not predict the dramatic increase in the amount of data coming in, the way the data would need to be queried, or the changing ways that business users would want to analyze data.

Mobile network operators such as Verizon, AT&T, British Telecom and China Mobile are attempting to manage massive loads on systems that were designed, on average, five to 10 years ago. Those systems contemplated a certain load based on the number of devices on the network and the nature of how those devices were used. Call centers are bearing the brunt of this, from the explosion of mobile and other connected devices hitting the network to the ways they use the network. Seven days of call data records (CDRs) in 2008 would be a mere fraction of seven days of CDRs recorded today.

Related Stories

The Evolution of Advertising in an Internet of Things World.
Read the story »

Real Time, Location, and the Internet of Things.
Read the story »

The Shortest Distance to the Internet of Things.
Read the story »

Podcast : Tech Summit to Explore the Internet of Things and M2M Data.
Read the story »

A traditional approach to dealing with this volume would be to invest in a monolithic relational database and, in a lot of cases, sacrifice some functionality for the ability to load that data quickly and as cost effectively as possible. The advantage of the Internet of Things is the ability to do analytics on the tremendous amount of tightly structured data that is coming in and find things that are interesting, to look at data in the context of other information and look for patterns requires the storage of an enormous amount of historical data for longer periods of time. If mastered, it can be used to gain and maintain a competitive edge, to provide customers with improved service as well as possible additional value-added and customized services, including the following examples:

    • Exploring historical call data records to identify patterns in service issues to enable changes in network configuration to improve overall quality of service


    • Querying customer service tickets to explore shortcomings in service that can be addressed with new offers


  • Identifying usage patterns of value-added services to plan for marketing campaigns and promotions aimed at increasing uptick


More and more operators are also recognizing that the one-size-fits-all approach to data analytics infrastructure just does not work. Performance and cost requirements are leading them to look to specialized technologies, such as real-time investigative or ad-hoc analytics. Under heavy network loads, it is important to be able to mine through high volumes of records to determine the nature, location and, ultimately, the cause of issues in order to quickly and effectively address problems. The operator will need to interrogate the data, query it on an ad-hoc basis in order to quickly understand and troubleshoot problems that may be occurring on the network. Issues may be a function of certain device types, operating systems, browsers, applications, or cell tower locations. They may be associated with the communication technology, be it 3G, 4G, LTE. Or perhaps there is a problem with voice, video, data, or any number of other dimensions that are captured within the call records.

This myriad of dimensions, especially in combination and across billions of records, can make the troubleshooting process a bit of a needle-in-a-haystack type of problem. The ability to quickly and easily maneuver through this data in real time becomes critical to successfully troubleshooting and fixing network issues and ensuring ongoing customer satisfaction.

As the scramble to adapt to this new and unpredictable data reality takes shape, a spotlight is being shone on the limitations of traditional approaches to database infrastructure and analytics. Telecoms, and their service providers, will need to think about their data and analytics infrastructure in a completely different way if they are going to be adaptive in an Internet of Things world.

Don DeLoach is CEO and president of Infobright and a member of the Data Informed Board of Advisers. Don has more than 30 years of software industry experience, with demonstrated success building software companies with extensive sales, marketing, and international experience. Don joined Infobright after serving as CEO of Aleri, the complex event processing company, which was acquired by Sybase in February 2010.

Prior to Aleri, Don served as President and CEO of YOUcentric, a CRM software company, where he led the growth of the company’s revenue from $2.8M to $25M in three years, before being acquired by JD Edwards. Don also spent five years in senior sales management culminating in the role of Vice President of North American Geographic Sales, Telesales, Channels, and Field Marketing. He has also served as a Director at Broadbeam Corporation and Apropos Inc.

Subscribe to Data Informed
for the latest information and news on big data and analytics for the enterprise.

Tags: , , , , , , ,

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>