How high-speed analytics help telecom groups monetise big data, by CTO Mathias Golombek

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This opinion piece is by Mathias Golombek, CTO of Exasol.

In recent years, the telecoms industry has become fiercely competitive. Firstly, fixed voice services are on the decline, with UK landlines generating 12.6 billion minutes of outgoing calls in Q4 of last year – 2.7 billion minutes fewer than the previous year. To add to the pain of the telecoms giants, new ways of communicating are continually entering the market, and consumers have benefited from low costs as new and old market players fight for market share.

Meanwhile, sparked by an increasingly digital-savvy customer base and the huge volume of data this now produces, traditional telecoms companies - which started life as communications service providers - are now broadly shifting their business model to wear another hat – that of the data service provider.

So it's no surprise that the industry has high hopes that they can start to monetise all the big data at their disposal and use it to generate new revenue. They readily recognise the untapped profitability that can be gained with greater automation, customer segmentation and network optimisation. Their minds are also on the Internet of Things (IoT) – expected to become the next big battleground for tech and telecoms companies – and the evolution of 4G to 5G, as well as digital advertising. In all these areas,  patterns in usage, interest, and location are key data points that can potentially drive customer intelligence and targeted sales.

It's interesting to see how this currently playing out across the different providers. Telefonica makes use of data for both external monetisation and internal monetisation, with network analytics and network optimisation based on the demands of customers and their prioritisation. EE monetises using aggregated passive probing data, such as out-of-home advertising and by providing intelligence on traffic patterns. Deutsche Telekom link network data with customers and third-party businesses to enable mobile advertising.

While some of this analysis can be carried out at a more leisurely pace in batch processing – let's say, the average number of a certain type of device in the network, or working out average busy periods in network traffic – certain analysis absolutely needs to occur in near real-time to be at its most effective. These days, advertising relies on up-to-the-minute data at every stage of a campaign to ensure the messaging is perfectly matched to the audience. Likewise with fraud detection, every second counts. Telecoms companies can potentially save millions by quickly observing voice and data fraud and preventing further misuse – not to mention the invaluable role this has in maintaining customer trust in the brand.

Network monitoring is another key area. By moving to higher-speed analytics, telecoms businesses can detect drop-offs in the network as and when they happen, addressing the situation before complaints start coming in. Network traffic can also be adjusted on the fly to improve overall performance.

In other words, while all data can be incredibly useful, it really depends on when you use it. There really is no substitute for faster analytics and action. And for the telecoms industry, more than ever, this is going to mean having access to data well and truly in the moment.

Like this article? See how enterprise data marketplaces are enabling a data-driven culture.


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