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A New Use Case for Purpose-Built Analytics: Saving Lives 

  • November 2015
  • 12 pages
  • Frost & Sullivan
Report ID: 3406211


Table of Contents

A New Use Case for Purpose-Built Analytics: Saving Lives


Data in the world of Communication Service Providers (CSPs) is a double-edged sword. On the one hand, the seemingly never-ending growth in data volumes traversing telecom networks represents a cost—something that must be dealt with in order to survive. Networks must continually scale up to handle more traffic. Additional spectrum is needed to meet the needs of mobile customers. And, the business management and operations systems—what Stratecast now refers to as Operations, Orchestration, Data Analytics & Monetization (ODAM)2 —must address these larger data volumes, often in real time.

On the other hand, CSP data provides valuable benefits. Many in the industry, including Stratecast, believe that data ownership is the currency of today, and the gateway to long-term business success. Data and, more specifically, the analysis of data, provides the key to opening the door to smoother operations, more satisfied customers, and hopefully increasing revenue. Supporting large data volumes, and analyzing the customer usage traversing a network, is not an either-or situation. As data volumes rise, the cost increases; yet, so does the potential for greater intelligence and better business results.

This week’s SPIE looks primarily at the benefits realized from CSP data analysis, and examines how CSPs, globally, are using data analysis to address a myriad of business needs tied to use cases for improving operations and generating new revenue. However, there are times when data analysis is important for reasons outside of the CSP; when the needs of the community outweigh the bottom line. For this purpose, the report also looks at how one supplier—Neural Technologies—is expanding the use of CSP data analysis in ways that may ultimately save lives.

The Benefits that Data Analysis Brings

There is no question that the trend for global data volumes has been one of explosive growth. To gain a global perspective on the amount of data that CSP networks now address, Cisco started examining this trend several years ago. Using forecast information from numerous external organizations, along with insight from its internal work teams, Cisco publishes its findings in the annual Cisco Visual Networking Index report. This report, published for more than five years now, is designed to help the industry better understand why a term like “explosive growth” is not a reach, but a business reality, with findings from the latest edition stating:

- Global mobile data traffic grew % in 2014.
- Last year’s mobile data traffic was times the size of the entire global Internet in 2000.
- Global mobile traffic is projected to increase nearly tenfold between 2014 and 2019.

Every day, each of us encounters examples of how data analysis makes our lives a little easier, though few of us likely recognize this trend. For example, a company such as Netflix will suggest a movie to watch; if a person takes its suggestion, there is a high probability that he or she will enjoy the movie. How does Netflix know what movies a person will like?

Data analysis. Netflix encourages its customers to rate entertainment viewed via the Netflix service or via other venues, such as on television, in theaters, or on DVD. Netflix knows which shows are watched via its service, whether the customer takes the time to rate the viewing experience or not. If customers have shared what they like, and what they do not like, along with what they have already seen outside of the Netflix ecosystem, most Netflix suggestions will likely be spot on.

All of the suggestion process is based on data analysis. Even if a customer has not shared ratings, the customer’s viewing history on Netflix provides some strong clues that data analysis may be able to exploit. Netflix is not alone in its use of data analysis. Facebook suggests new friends based on analysis of one’s current friends, and their friends, and their friends’ friends.

Amazon suggests other items to consider, based on what a customer is currently viewing or purchasing, and what other customers looking at the same item have ultimately done. While these examples are outside of telecom, CSPs and their suppliers are quickly learning to take advantage of the power of data analysis.

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