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Telecommunications & Data Science

If there is one industry that should be leveraging data in every way possible, it’s telecommunications. The telecommunications industry services billions of people each day, generating massive amounts of data. Though not many telecom companies are leveraging this data

Another challenge is itself running such telecommunication network at a vast scale. Millions of subscribers latched on to the network must have a great experience. This calls for managing a large and complex network. This calls for extensive analysis of tremendous amount of data being generated every second. Telecommunication networks are hence migrating from hardware-based network to software-based network. From data science perspective, software-based networks can easily be managed from the edge of the network.

Why Data Science cannot be ignored?
  • Customer Care: Telecom customers walk into store, call up customer care lines, chat with representatives on daily basis. The wait line is long. It is important to serve these customers as quickly as possible and get their issues resolved. Pre-emptive maintenances like booting a modem remotely without even the customer knowing solves the problem before it occurred. And the customer did not have to call the helpline.
  • Leverage Location Data through Data Science: Telecom companies generate tonnes of location data from mobile devices in the network. This data will be handy during the role of 5G networks in 2020. 5G network will need more transmitters of the signal in order to get to the customers. Data Science can help augment that need on the basis of location data. It helps install ‘Small Cells’ effectively in places where they are needed.
  • Personalisation and Recommendation: Likewise, other areas and industries, personalisation and recommendation are big in telecommunication industry as well. A person taking a 45 minutes train ride to work would want to consume different content than what that person would like to consume sitting on a couch. Recommender systems on mobile networks at particular times will help improve the customer stickiness. No one would know, but Data Science is working in the background!
  • Behemoth End Devices: The number of end devices keep on increasing consistently. This poses a great problem of handling the signaling but also storing this vast amount of data coming into the network every second. The data being generated is growing exponentially and becoming extremely challenging.
  • Traditional Data Storage: Most of the networks still use traditional methods of data storage. Both RDBMS and flat files storage systems do not work well with latest Data Science models. Some of the problems are Retention, Longer Search, Non-Realtime Information and Fragmentation.
  • Data Analytics: Traditional data analytics were set up at NOC that use stacked server blades and SAN for making data available for analytics. However, these are linear performance models and will not improve beyond a particular point. These systems are not equipped to handle Big Data which is mandatory for Data Science.

Telecommunication & Data Science

Within Telecommunication industry, Data Science applications are widely taking over processes to streamline the operations, maximise profits, build effective marketing and business strategies. Key activities at any telecommunication network includes: Data Transfer, Exchange and Import. Data Science is aiding Telecommunications to handle the exorbitant data in the following areas: Fraud Detection, Predictive Analysis, Customer Segmentation, Churn Prevention, Lifetime Value Prediction, Product Development, Network Optimisation, Recommendations Engines and Customer Sentiment Analysis.

Data Science is meant to achieve a business or a company goal and not be the goal itself. Existing data analysts in the telecom sector can easily be upskilled to leverage Data Science. Since they are part of the telecom company already domain knowledge and knowhow in available with them as a pre-requisite.

Advantages of an upskilled Workforce

  • They would know what the company’s business objective is and will be easily able to connect it with Data Science modelling.
  • They have clear understanding of what and why the challenge is.
  • Since Data Science is a consistent and dedicated requirement for the telecom industry, it is better to up skill the current workers into Data Scientist, Data Engineers, Data Analysts and Data Architect.

How Can SkillUp Technologies help you leverage Data Science?

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