Big Data Analytics, Traffic Monetization and Management

I’m in not-so-sunny Barcelona this week for Informa’s Telco Big Data and BBTM conference. Amazing how more enjoyable and relaxing Barcelona can be when there is no GSMA Mobile World Congress to attend.

Telefonica presenting Big Data Analytics use cases

The underlying theme for the day was how to Telcos can utilize Big Data and Analytics. While most of the focus around Big Data and Analytics in Telcos is usually on customer facing monetizing and revenue opportunities, there are quite a number of internal-facing use cases too.

Both Telefonica Deutsche Telekom discussed utilized big data analytics to improve internal processes within operators for making better, faster decisions. Many operators are looking beyond the obvious Marketing and Sales divisions when it comes to utilizing analytics. A major issue that was touched on by a number of operators (which probably resonates within any organization looking to utilize analytic data for internal process) is a cultural barrier within the organization that may prevent data from driving decisions – a similar point was made by the Harvard Business Review in a blog earlier this year – here

When it comes to monetizing Big Data Analytics it would seem that imagination is the limiting factor. Sessions delivered by Orange, Telefonica, T-Mobile, AT&T, Huawei, Amdocs and others all presented fascinating use cases that demonstrated how operators are already or planning to productize the big data at their disposal.

Telefonica presented their “smart steps” product which goes all the way up the analytics value chain by offering footfall analytics to retail outlets
T-Mobile talked about indoor analytics for retailers, heat mapping of large stores, personal greetings, Telematics and mobile advertising.
Huawei talked about profiling subscribers’ online lifestyle, getting to know your subscribers and what experience they really want.
Amdocs gave a rather unique session (using augmented reality) and stressed the aspect of charging analytics
Openet gave a great session on real-time contextual market utilizing big data analytics. While this was a use case that I have seen before (and really like), Openet assured me they now have some operators that have commercially implemented this use case.

Overall it was a very interesting day and my key takeaways for the day were as follows

Big Data is endless! Without a clear, decisive objective to the analysis, focusing on all that data can burn a lot of time and resources. The key is to focus on a clearly defined set of case studies and aggregate the specific data point needed for those use cases.
There are a lot of privacy concerns surrounding big data analytic and monetization in particular. The recurring theme here, that was stressed by a number of operators in particular, was transparency and trust should take precedence over anything else. A clear opt-in should be required whenever operators intend to utilize any type of subscriber data externally.
Don’t abuse your big data. Just because you can do something, does mean you should. So, even in the case where you have opt-in from subscribers, some big data analytics use cases may cause serious damage to an operator’s brand, even though they fall within the regulatory bound. For some operators, the lure of the big data analytics and what they can do with it might prove too great.