July 17, 2012

3 Game Changing Big Data Use Cases in Telecom

Like many industries undergoing transformation,  the Infrastructure/Security/Compliance function within large telecom companies is becoming more data driven. Flutura Decision Sciences has been at the forefront of some cutting edge use cases for Telecom Infrastructure/Security Intelligence. Here are 3 powerful use cases which vividly bring out new possibilities in Telecom big data

Telecom use case-1: Contact centre text mining and Telecom Bandwidth throttling

 In most organisations the contact centre channel data is analyzed typically from a SLA(Service Level Agreement perspective). For example TAT (Turnaround time), Average wait time etc. But the actual transcript of the conversation can yield powerful insights regarding telecom infrastructure usage. Surge in Contact centre keyword frequency as a lead indicator to infrastructure bottlenecks.
Telecom providers are competing with each other to get greater ARPU (Average revenue per user) from data services as opposed to voice services. In this competitive environment a telecom provider launched a new but extremely viral gaming application on Mobile devices. A few days after its launch it started observing a burst of calls to the call centres and on text mining the transcript Data Scientists found a sharp spike in the keywords alluding to performance. The specific intelligence regarding keyword burst and specific time of day at which this was encountered was shared with the infrastructure planning group which then put a plan in place to throttle the bandwidth dynamically based on usage.

Telecom use case-2 : Collocation Analysis from Cell phone towers

This is a security use case where if an investigation team wants to find out if there were multiple phones with the same person. When a call is made typically the following data points are captured - subscriber , date, time, and duration. Depending on the type of call, additional data can be gathered, including switch data, cell tower IDs, device identification (serial) numbers, as well as International Mobile Subscriber Identity (IMSI) and International Mobile Equipment Identity (IMEI) codes. The unique ID of the cell tower a handset was connected to when a connection was made is one of the most important components for collocation analysis
By examining terabytes of CDR/Tower  records from the switch one can triangulate on a few collocation events.  A co-location event can be defined as the same cellphone tower  being used to route calls during a specific point in time. This is almost like looking for a needle in a haystack and traditional solutions would have trouble handling the massive volume of tower and switch data. But with a combination of massive Hadoop clusters and columnar database architecture, these queries can be executed at lightning speed to surface a significant few events of interest from the massive ocean of log  data across devices

Telecom use case-3 : Multi device event stream analysis co-relating Firewall & IDS & Switch activity

Typically in most telecom infrastructures IDS ( Intrusion detection systems ) sit at the periphery of the network monitoring malicious activity and recording the same as log entries or alarm events into a log file. Firewalls and application logs also store  a plethora of important events which if triangulated thru a central log repository to  provide a comprehensive picture of any patterns which are dormant in the attack
One key components to enabling a Central  Log File repository with events streaming from multiple devices which are ingested and collated centrally.  Once this central log file is set up to store the torrent of event data, it can be  channelized into intelligence to optimize network infrastructure and aid security of the telecom assets. Flutura Decision Sciences is convinced that setting up of a Network Intelligence team consisting of Security experts and Data Scientists who work in a collaborative fashion can yield dramatic game changing insights to catapult an organisation to the next level


  1. I am not very much cleared with the point number 2. Can you throw more light on it to get it better explained?

    Shivlu Jain

  2. Sir,
    Can you share more details on the data sources for each use case( CDR, application log etc) so that we egt a more clear picture of how unstructured data was utilized to provide solution to these problem statements?

  3. Hi Ishaan/Shivlu,

    The breadth of data sources used are
    - CDR : Call detail records from the switch
    - Firewall alarm event logs
    - Location logs from towers
    - Virtually any device in the telecom infrastructure instrumented to capture event logs. Trust that helps

  4. I need some clarification like from where M2M usage information is captured? How devices & customers are linked?(The way we have Mobile Number/IMSI linked with customer in Wireless Telecom).
    Does M2M billing happens on data usage done through GGSN/SGSN? If yes, how to identify it?
    Are there any separate billing plans those are offered for M2M service enablement?

  5. The fifth necessity is to frequently check and screen systems and their exercises. Security ought to be frequently checked for redesigns. All endeavors to get to private data inside the system ought to likewise be checked.

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