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Future of Data & Analytics in Telecom – Emerging Use Cases & Talent Analysis

Telecom Management February 18, 2021

Future of Data & Analytics in Telecom – Emerging Use Cases & Talent Analysis


The telecom industry has been at the forefront of using data & analytics to predict future outcomes. This has been possible thanks to the ubiquity of cheap data plans that gave rise to an abundance of user metadata generated every second.  

The pivot from merely reporting/describing this data to using it to make highly advanced predictions was sudden but calculated and accelerated by the rapid advances in AI/ML technology. 

However, today in the telecom industry, the talent landscape has simply not kept pace with the demand for analytics teams.  

This whitepaper will take a close look at the present scenario and analyze which way the talent winds are blowing for data & analytics teams in telecom. 

Evolution of Data & Analytics in Telecom 

What started out as Descriptive analytics has over time matured into diagnostic & even predictive analytics. But as you can see in the diagram below, the focus has now shifted to prescriptive analytics, where the point is to prescribe specific solutions using AI/MLbased upon the forecasting data used by predictive analytics. 

Evolution of Data & Analytics in Telecom

The integration of Data & Analytics with other new-age digital technologies is creating advanced use cases in the telecom industry. 

A few of these emerging use cases are: 

  1. Customer segmentationWhile this has already existed in some form, superior quality data and pointed analytics help companies finetune their process. 
  2. CLV predictionUsing Data Analysis and Visualization, companies can now determine the value a customer generates to decide whether a target demographic is worth long-term investment. 
  3. Call detail record(CDR) Analysis: Analyzing customers call details for fraud detection, churn prevention, and targeting profitable customers using RFM Analysis.  
  4. Price Optimization: Optimizing prices using past pricing data is now possible thanks to data analytics. 
  5. Location-Based Promotions: By analyzing various location and demographic datasets to promote the products accordingly in the regions. 

Understandably, these are high-end use cases requiring proven expertise in data analytics. To navigate the future of Data & Analytics in organizations, it is imperative for HR leaders to revaluate their existing talent management and workforce planning strategies. By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling. 

This fact is underlined by the global shift from piloting to operationalizing AI by 2024, which will see a five-fold increase in streaming data and analytics infrastructures. 

Introducing New, Focused Analytics Teams 

Telecom firms are rethinking their existing team structure to utilize analytics talent according to their business needs efficiently. The current model of a centralized analytics team sitting under the IT division and catering to the needs of different business divisions and core business functions is being phased out. 

Telecom firms are now building dedicated analytics teams for each business division and function, thus driving higher value with highly defined and specific workloads. So, if a business has three products, then each of these products will have its own analytics team. Similarly, for business functions, there is an analytics team for marketing, HR, operations, and others.  

While this type of organization structure is still in its nascent stage in the telecom industry, we can see that IT enterprise firms such as Microsoft have successfully adopted this model. 

But, building a core analytics team requires the HR leaders to understand the different job roles and the workloads catered by them according to the business needs. This is further complicated by the fact that traditional skills of data & analytics talent are now augmented by technologies like AI, Machine Learning, NLP, Computer vision, etc., which requires HR leaders to have an even more robust understanding of the Data Analytics skills & talent ecosystem. 

The Telecom Talent Scenario & the Emergence of Reskilling 

Globally, North American & Asian locations (especially India) have the highest availability of Data & Analytics talent pool.  

In North America, New York, Chicago, Los Angeles, and the San Francisco Bay area are sitting comfortably at a very high talent availability index. This isnt surprising because these are matured locations with a very solid supply of tech talent.  

But the question remains, how will HR find talent in emerging locations where such talent is not readily available? 

The answer is reskilling. Most roles can be reskilled to be brought under the ambit of data & analytics with enough clever planning and execution. 

Reskilling not only saves cost but also provides viable career path to the disrupted roles. 

Approximately 1.37MM jobs are projected to be fully displaced in the coming decade. Combined with the statistic that reskilling results in 20-50% cost-savings per employee, it is easy to see why more and more telecom companies are actively scouring for reskilling & upskilling solutions. 

However, an efficient reskilling strategy requires the firms to assess the transition feasibility of disrupted job roles into new-age job roles by analyzing skills gaps and other reskilling parameters. 

We have undertaken this exercise for the role of Systems Engineer, which is under threat of disruption in the telecom industry.  

Here, we use our proprietary Reskilling Propensity Index to identify the optimal target role for this position. Draups Reskilling Propensity Index measures the feasibility of career transition from one role to another role. The index is built by analyzing some key attributes that include: Technical skills, Functional Skills, Soft skills, Historic Career transitions, and Compensation. 

Our analysis presented us with four target role, viz, Big Data Analyst (RPI =7.1), Data Consultant (RPI = 6.8), Data Analytics Engineer (6.6), and Applied Data Scientist(RPI = 6.4). 

Since Big Data Analyst has the highest RPI, we chose this for further analysis and subject it to reskilling analysis. 

Based on skill gap analysis, relevant learning modules/courses were selected to showcase how a Systems Engineer’ can be reskilled to evolve into a highdemand Big Data Analyst role.


DRAUP identifies the skill-set and maps it with the existing list of courses and programs present in its extensive Database. Learning management teams are in complete control of end-to-end employee reskilling programs with functionality to restrict course & certifications recommendations by partners 

Reskilling is the need of the hour, especially in the telecom industry, which is staring at a burgeoning talent gap in data & analytics. 86% of employees around the globe also demand new skills training from their employers.  

Implementing robust reskilling/upskilling strategies is a long-term way of meeting employee satisfaction while ensuring that you keep your talent pool well-fed. 

Get in touch with us to know how you can safeguard your talent from fast-evolving technologies. 

Lead the future with talent intelligence. Get in touch with us today.