Top 4 Growth Areas of
Machine Learning in


If there is one industry that should be leveraging data in every way possible, it’s telecommunications. After all, it’s through their services that billions of people each day are generating massive amounts of data on smartphones through calls, text messages, app downloads and every kind of internet activity imaginable.

No wonder, then, why an analysis by ReportsnReports in 2014 projected that the use of data analytics in telecommunications would grow nearly 30 percent per year between 2013-18. A report the following year, by Ericsson, went even higher, projecting a 50 percent compound annual growth rate by the end of 2019.

And yet, experts say that the telecom sector has only just begun to explore the potential of data science.

A study by McKinsey, Telcos: The Untapped Promise of Big Data, based on a survey of leaders from 273 telecom organizations, found that most companies had not yet seriously leveraged the data at their disposal to increase profits. And only 30 percent say they have already made investments in big data.

The McKinsey study found that most companies that invested in data science attributed the investment to a modest increase in profits, while a small number achieved major increases. But the good news is that calculating return on investment (ROI) for these types of investments in data is becoming more concrete, and as it does, more and more businesses are willing to take the leap.

So while there is certainly debate within telecom companies about whether the return on investment is worthwhile, there is no doubt that data science, machine learning (ML), and artificial intelligence (AI) are inevitable when it comes to the industry’s future. Those that figure out how to leverage these techniques and technologies will thrive; those that don’t will be left behind.

The importance of data science, ML, and AI to the telecom industry will likely present itself in these four areas in particular, which this paper will take a look at individually:

  1. Techniques in troubleshooting
  2. Combating fraud
  3. Marketing tactics
  4. Customer experience improvements