5 hot topics from Telco Data Analytics 2017

Hot topics from TDAEurope

This year’s Telco Data Analytics brought together some of Europe’s leading telecoms experts, tackling topics such as machine learning, new infrastructures and the impending General Data Protection Regulation (GDPR). In this blog, we share the top 5 topics from the 2017 event.




With GDPR on the horizon, it’s no surprise that it was discussed by several speakers at this year’s Telco Data Analytics. Andres Vegas, Global Big Data Director at Telefónica, explained that privacy and personal data management are top challenges that operators must tackle ahead of the fast approaching GDPR compliance deadline of May next year. And with 89% of customers already worried about what happens with their data, along with harsh penalties for non-compliance, it’s a challenge that must be taken seriously.


Also addressing the subject, Ludovic Lévy said that Orange, where he is VP of Global Data Strategy and Governance, is looking at GDPR as an opportunity, rather than a constraint. Operators must make the opt-in process transparent for subscribers; instead of pages and pages of terms and conditions, they could take a modular approach, with ‘feature-based’ opt-in. He used the example of a recent update to Waze, a popular navigation app. Updated terms and conditions allowed the app to access a user’s calendar, alerting them when to leave for their next appointment based on traffic conditions. Although Waze had been transparent with this change, messages within terms and conditions can often be overlooked by users, and a simple ‘new feature’ opt-in would likely be better received and build users trust


2. Machine Learning Use Cases


Unsurprisingly, artificial intelligence and machine learning were discussed throughout the event. Almost everyone agreed that these technologies must be use case driven to ensure they actually bear a benefit to operators, whether it be financial or operational.


Perhaps one of the most polished use cases presented at the event was the use of machine learning algorithms to improve out-of-home advertising, presented by Roman Postnikov, Director of Customer Analytics and Segment Marketing at MegaFon. In this particular use case, MegaFon wanted to use their data to track movement, and customise digital billboards to adapt to the local audience. However, the telecoms data was not accurate enough to track the physical paths taken by users, so a machine learning algorithm was developed to map the path with the most likelihood. Results showed that 95% of the time, an accuracy of <25m could be achieved, and this is expected to reach an accuracy of <10m over the next 12 months as the machine learning progresses.


3. Data integration challenges


A common pain point that was highlighted by both operators and vendors at this year’s event was the continuing challenge of data quality and data integration. Pratik Bose, Head of Mobile Big Data Solutions at EE/BT explained that data issues are rarely spoken about, but can have a massive operational impact, especially on newly arising use cases in the fields of machine learning and artificial intelligence. Joar Anderson from DigitalRoute expanded on the topic, saying that data scientists were spending up to 80% of their time on cleaning, collecting and organising data before they could utilise it, and this was an issue that must be acknowledged and tackled head-on.


4. Real-time analytics


Several discussions tackled the subject of combining legacy toolsets with real-time analytics solutions. A panel featuring voices from Docomo and Swisscom saw a common question arise; what actually is real-time? For Docomo, a data latency of 30 minutes for mobile alarming applications can be classed as real-time, where others may see a delay of a few seconds to a few minutes as real-time. What was also apparent at the event was that not everything needs to be in a real-time analytics environment immediately. Instead a balance is needed between existing systems that already work well, and specific use cases within a big data application.


5. Data scientists


Finally, a topic touched on by many was the resourcing of data scientists, specifically the challenges operators face when it comes to attracting the best people. Telco’s are up against the likes of Google and Facebook, who can often appear more attractive, and their type of data is usually more relatable. In fact, in an executive panel discussing the topic of creating a sustainable analytics culture, it was highlighted that the problem may well not be about data scientists, but about data translators, providing an understanding of telecoms data which can only come from experience within the industry.



As always, this year’s event tacked some of the most pressing topics within telecoms right now, some reoccurring from last year’s event, and some newly arising challenges. What was clear is that data analytics touches multiple areas across the telecoms environment and will surely be a hot topic well into the future.