Is Your IT and Telecoms Data 'Big' Enough?

4 minute read


The IT world seems awash with claims for Big Data. We know that our communications produce large quantities of data, but it can be hard to see what we can actually learn from this data, or whether our networks produce enough of it to be meaningful. The truth is that you do not have to be Facebook, Google, or the NSA to incorporate the lessons of big data into your organisation. To understand how the data your network produces can work for you, it’s helpful first of all to have a sense of what big data really is.

What is Big Data?

Between 1992 and 2012, digital data became roughly 19000 times cheaper to store, going from about £380 per gigabyte to 2p per gigabyte. Over this period, not only did a whole range of day-to-day communications and devices come to depend on the easy flow of digital data, but it also became much easier to store this data for analysis. A simple example of analysis would be spotting spikes and other trends in usage to reroute networks at particularly busy times to ease traffic. These data volumes exceed the amounts a human could cope with, but analytic tools have developed to process huge quantities of data.

Analysing Big Data

The increasingly cheap cost of hardware for storing data has made it easy for networks to build up data lakes: huge reserves of data collected on the duration, time, and location of their communications. In the computer analysis of big data, this is not treated as one singular lump of data, but rather as hundreds of thousands of discrete parts, looked at by hundreds of thousands of separate processes. This analysis looks for all kinds of trends between the many packets of data, from which previously hidden usage patterns and abnormalities are revealed. Moore’s Law shows that the costs of the processing power required for this kind of analysis continues to decrease, making data analysis increasingly viable.

What Are the Benefits for Your Network?

The idea that the large quantities of data already being produced by networks can be harnessed by computer analysis has led to the perception that ‘Big Data’ has the answers to all problems: cost saving and increased productivity guaranteed. While this does appear to be true, it’s best to incorporate big data analytics into your network in order to provide a new frame to approach problems with. Instead of trying to impose data analytics from top down on pre-existing issues in your network, use big data as a model applied from the group up to see what insights it provides: let the data speak for itself.

  1. You are already accumulating large quantities of data. The prevalence of data-transmitting devices in all aspects of telecommunications and business means that organizations are already producing amounts of data that are prime for computer analysis. Data analytics allows for patterns to be seen in this pre-existing data that are invisible to conventional human analysis: there is no lower limit on the amount of data required for ‘big’ data analysis.
  2. Data analysis can lower costs, raise efficiency, and provide alternative sources of income. Having a complex understanding of a network’s usage patterns, analytics and computer learning offer insights into how to maximise available hardware. In addition, understanding the ways in which potential customers interact with all kinds of digital technology is of increasing value to a wide range of businesses, meaning there is an increasing market for the insights gained from data collected by telecom and IT networks.
  3. Big data offers real-time analysis instead of backwards-oriented solutions. Analytics can easily see trends as they unfold across multiple locations, where more traditional ways of looking at network usage risk missing crucial correlations. As one simple example, big data analysis can easily compare network statistics with great complexity on one day with any other day, on one Friday with any other Friday, and in one network location with any other network location, simultaneously. To achieve multivalent analysis of this sort without big data analytics would involve too much time and too many blind alleys to be feasible.
  4. Once put in place, big data analytics are easily and cheaply scalable. The low cost of storing more data and the requisite levels of processing power make it easy to process increases in data traffic. There are many opportunities to digitise a wide a range of business and telecommunications practices, all of which increase the potential pool of data ready for analysis.

Fighting Fraud with Big Data

While many of the solutions big data can bring to your organisation might only become clear once your available data starts to be analysed, it is now clear that big data leads the way in preventing fraud. High-profile attacks on Vodafone and the UK’s NHS have increased consumers’ concerns for network security, and big data has risen to that challenge. Argyle Data have shown how machine learning and data analytics can take the available data from a network and use it to spot and predict the kinds of anomalous activities that are a tell-tale sign of fraudulent behaviour. All networks can benefit from the security provided by data analytics, increasing the confidence of current users and potential customers.

Make the most of your network’s available data and let analytics show you what your network is really capable of!

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