Identify use cases from any industries of your choice and elaborate on how Big Data analytics can be used to transform those businesses.

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Telecoms:

 

The telecom industry worldwide is finding itself in a highly complex environment of decreasing margins and congested networks; an environment that is as cutthroat as ever. A new IBM study on how telcos are using Big Data shows that 85% of the respondents indicate that the use of information and analytics is creating a competitive advantage for them. Big data initiatives promise to improve growth and increase efficiency and profitability across the entire telecom value chain. Yes, Big Data to the rescue again!

The potential of Big Data, however, poses a challenge: how can a company utilize data to increase revenues and profits across the value chain, spanning network operations, product development, marketing, sales, and customer service.

Big Data analytics, for instance, enables companies to predict peak network usage so that they can take measures to relieve congestion. It can also help identify customers who are most likely to have problems paying bills as well as those about to change operators, thus exacerbating churn.

Telecommunication companies collect massive amounts of data from call detail records, mobile phone usage, network equipment, server logs, billing, and social networks, providing lots of information about their customers and network, but how can telecom companies use this data to improve their business.

Conclusion:

The telecommunication industry has been boosted by the active use of machine learning and data science. This step was made only for the better. A great many aspects and issues became much easier to resolve, control or even prevent from happening.

The telecommunication sphere had to adopt modern technologies and techniques to stay relevant and not to lose positions under severe circumstances of the global market. Telecom companies operate with vast communication networks and infrastructures with the intense data flow. Processing and analysing this data with the help of data science algorithms, methodologies and tools find practical application. Therefore, we attempted to specify several of these use cases and to demonstrate real benefits one can get.

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