Why Telecom Companies Need Big Data Solutions

Anna Orlova

IT copywriter


23 May 2016

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23 May 2016

Have you ever received a retail coupon that reflected a recent purchase, or been sent a special offer via SMS from an establishment as you walked by it? Most likely you have, and the precision of retail advertising is no surprise these days. Typically, such deep knowledge of a customer is derived from a phone call and billing records, purchase history, payment records, GPS, etc. — or in other words, through the analysis of “Big Data”.

But don’t get the wrong impression: Big Data is not concerned with tracking the specifics about or behavior of individuals, with an intention to create a 360° view of a customer. Instead, the term refers to the exponential growth and availability of raw data in general, the processing of which provides valuable insight into various business spheres.

Big data

While a certain portion of Big Data is relatively easy to process — e.g. the source data for retail projections mentioned above — other data is much less consistent and harder to obtain and analyze. We focus on such data in today’s post and specifically on the opportunities Big Data brings to telecommunication company owners. We will also touch upon a few potential obstacles they might face while processing the data — and solutions to these possible pitfalls.

But first, let's clarify the key terminology, so we are able to collectively examine such a huge topic.

What is Big Data?

Put simply, Big Data is a vast and not-so-easily-managed collection of information. IBM defines it as “information that can't be processed or analyzed using traditional processes and tools.” Gartner provides a more complex definition, referring to it as “high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

Thus, what could be referred to as the “Three V’s of Big Data” are volume, variety, and velocity:

Volume — According to IBM, there has been 800,000 PB (petabytes) of data stored worldwide as of the year 2000, and their specialists expect this number to reach 35 ZB (zettabytes) by 2020. The amount of data stored today is exploding with exponential growth: Twitter alone generates more than 7 TB of data every single day; Facebook produces 10 TB daily — and some enterprises are compiling hundreds of terabytes of data hourly.

Note: Despite these enormous figures, Big Data is mostly neglected: according to Gartner Group only 10% of businesses leverage Big Data and those who do realize a 20% increase in performance and revenue as a result.

Variety — Today, data comes to us in myriad formats. And it’s no longer only traditional structured data but also raw, semi-structured and unstructured data from web pages, search engines, social media portals, active & passive data sensor systems, etc. Managing all varieties of data and transferring them into a single, coherent source of business knowledge is something many organizations still struggle with.

Velocity — The enormous speed at which data is flowing in is another key characteristic of Big Data. The explosion of sensors, RFID tags, smart metering, and other innovative technologies are driving the need to deal with floods of data in almost real-time, and handling data velocity is still a big challenge for most companies.

Volume Variety Velocity

How Telecommunication Companies can benefit from Big Data

Delivering a huge amount of data for analysis and thus leading companies to more successful decisions, Big Data is crucially important to a business. In general, it means greater operational efficiency, cost reduction, and reduced risks. Among other things, telecom companies are exploring how to use Big Data to achieve improved workflow and develop more useful new product offerings and improved services — all based on the deeper understanding of customers made possible by this vast accumulation of information.

At the Telco Big Data event held last year, several companies shared their experiences on how Big Data analysis can pay dividends to a service provider:

  • Guavus, a vendor of telco-focused analytics software, reported that for one of its customers a reduction in network equipment costs from $1B to $54M was achieved due to a better understanding of how the network is being utilized.
  • Many CSPs claimed a doubling of product sales through more efficient targeting of their key markets.
  • Sprint reported generating $10M in revenue through the external sale of marketing insight data on its customers.

How to start

Telcom-relevant data falls into two broad categories:

  • Structured data that is easily processed: billing records, electronic records (IP, service), location records, inventory, etc.
  • Unstructured data that makes it harder to derive insight from its content: voice calls, texts, web & media content, apps, etc.

Additionally, one can differentiate between:

  • Content data — the actual content moving through the network;
  • Meta-data — info describing properties, sources, costs, etc., relating to the content data.

Structured meta-data is usually found in an operational support system (OSS). If the diverse data sets and information streams generated by OSSs can be easily extracted, operators are able to formulate invaluable Big Data insights. For this reason, Azoft specialists suggest starting with OSS optimization.

Big data

Big Data: Customer case study

When one Azoft client — a large Australian wireless operator — decided to take advantage of Big Data, it had to replace its entire IT system. The major problem was the absence of a single network storage point for all dispersed data: all network equipment and configuration information was processed by Cramer OSS Suite 5 as well as by several other diverse sources.

Furthermore, Azoft (our team took care of the network inventory aspect) discovered that due to quickly evolving industry demands, the client needed to move their system to a newer version of Cramer. However, since Cramer systems are usually highly customized and fine-tuned, there is no “one-size-fits-all” migration tool for quickly transferring existing data to newer versions of Cramer.

After extensive research into what's available on the market, we procured an innovative migration tool that was the best fit for our project. Besides automating the migration process, this tool also helped us to reduce downtime by taking advantage of the bi-directional synchronization of source & target data.


The project was a success, and our client was able to benefit from exclusive data procurement made possible by Azoft. But according to an IBM survey, over 50% of business leaders today still admit that they don't have access to the insights they need in order to properly carry out their job duties. Yet it doesn’t need to be this way at all! And Big Data should provide for you significant and copious data to apply toward increasing business performance and total revenue.


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