Being surrounded by techies, I listen to most of their discussions about latest technologies and innovations but they always prefer to talk about “Big Data”, “Big Data Analytics (BDA)” and that made me curious to learn more about BDA. I’ve got the basic ideas and knowledge from our discussions, but still I had my own queries and I needed a real-time example which I was surfing through Internet, but I got the answer from my day-to-day life.

For the past months, I have been using 3G network and my usage is limited to 2.5GB, But in recent days that was not sufficient for me. So I started using extra data for credit. Suddenly I received a message from my service provider saying that they are ready to offer me a personalized internet pack of my own data usage. I was shocked and excited about it and that was the moment where I got the complete understanding about the power of Data Analytics.

So here I am, trying to explain about the most complicated things in a way where everybody could understand about Data Analytics.

By the proliferation of smartphones, Communication service providers (CSPs) started recognizing the huge expansion in the volume of data, travelling across their networks and they really wanted to make use of that data to improve their revenue and to get a clear insight about their customers.

CSP’s are aware that their traditional storage and analytic solutions will not help them to manage this huge amount of unstructured, expanding and diverse volume of data. That is why they need new storage and analysis solutions, so that, they can cost-effectively store and derive new insights from this growing volume of customer’s petabytes of data.

Our team of experts handpicked some incredible use-cases to give you a clear understanding about, what Big Data Analytics (BDA) is.

Use Cases:

  • Fraud Detection: Communication Service Providers need to protect their customers by proactively detecting the fraudulent activities. They can analyze the usage data, location-specific data and customer account data in real time to model baseline “normal” behavior. BDA can help build models that can flag anomalous phone calls that might indicate theft or hacking, both in business-to-business and business-to-consumer environments.
  • Clickstream Analysis: Communications service providers can generate more revenue and create better customer experiences by tracking and analyzing customer clickstreams to understand their preferences and propensity to buy. For example, if clickstreams show a customer has been researching specific products, the CSP can serve up targeted promotions or offers to that individual customer. They can optimize web pages to increase conversion including cross-sell opportunities.
  • Network Management/OptimizationCommunications service providers can optimize quality of service and routing by being able to analyze network traffic in real time. This enables them to respond to the fluctuations in traffic and reallocate bandwidth as needed.
  • Location-Based Services: 
  • Consumers want the best deals for all the purchases. Based on their real-time location, the CSP’s have to prefer the customer’s privacy preferences and provide relevant offers only when it is requested or opted-in.
  • Using location data to build out the cross-industry solutions such as eHealth, mobile payments and ticketing, Smarter Cities (traffic management, disaster/emergency response) and vehicle telematics.

Some of the giants like AT&T, Verizon Wireless, SingTel and Huawei improved their revenues, operational efficiencies and real-time operational decisions using the insight from customer’s data.

Erian Laperi, Director of Data Governance-Big Data at AT&T said “The age of Big Data is upon us. Storage costs are going down, and data analytics is becoming more capable and more user-friendly. Even your auto mechanic will be storing a petabyte of data soon”.

Finally, this is how I started understanding Big Data Analytics and still I am a learner, after all it is a Bigggggggg Data. Right..?! If you have any other interesting use cases to learn data analytics, please do share with us. Stay Tuned for more updates.

Date Published: July 13, 2015

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