Data analysis is already a big part of the telecommunications industry, and yet still, experts estimate that most companies have not yet seriously leveraged the data at their disposal to increase profits. That means there’s still a lot of work to do - but where do the biggest gains lie, and how can teleco companies get there?
Embracing the ML and AI Movements
Static or one-off data analysis on past data is out; machine learning (ML) for artificial intelligence (AI) is in. Given that the largest telcos collect up to 6 billion call detail records (CDRs) per day, unless the plan is to hire every person on the planet, that’s not data that a human workforce can analyze. Add in other types of data (like that from CRMs, social media, and more), and it is abundantly clear that in fact, without ML and AI, it’s impossible for telecommunications companies to gain exponential insights out of the increasingly exponential data at their disposal.
ML, and AI (in tandem with data science platforms or tools) offer the opportunity to:
- Execute automated data parsing and analysis to extract predictions and insights instantaneously, in real time.
- Identify patterns (using advanced ML strategies like anomaly detection) within the call volume or other data that may point to problems that need to be addressed (such as dropped calls, long download times, and poor call quality).
- Create scalable data visualizations that also help identify these patterns and issues.
- Leverage predictive maintenance techniques to identify early issues likely to result in poor customer experience or down time before they cause more widespread problems.
It Takes a Village
This kind of data-driven overhaul doesn’t happen by simply buying a tool or hiring a team of data scientists. For telecos to fully embrace the ML and AI movements, it will take a coordinated effort between technology, people, and processes. To extract maximum value, data must be at the hands of everyone in the business (not just a specific, siloed team) to use it to its full potential for every possible use case.
The good news is that thanks to the relatively slow adoption of advanced ML techniques in this industry, there’s still time to be on the cutting edge. Ready to start the transformation? You’re in luck - check out Top 4 Growth Areas of Machine Learning in Telecommunications for a full breakdown of the opportunities (+ how to get there).