How to build a puzzle without knowing the picture

Analytics is becoming an increasingly dominant theme in the telecommunications industry. Operators are interested in using it to improve organizational agility, optimize processes, enhance customer experience and develop new revenue streams. However, analytics isn’t a one stop solution — it’s just one piece of a much bigger picture. But what does that picture look like?

There are many advanced analytics use cases for telecommunications service providers, varying across networks, operations, customer and external monetization business models. The two key areas telcos deal with mostly will help provide a broad overview of the benefits of the right analytics.

1. Using data analytics to optimize customer relationships
   
   Because most communications today are digital, operators’ networks have become a highway of interaction. Consequently, there’s a huge opportunity to use the wealth of information available to optimize customer interactions, increase the strength of the relationship, and improve or develop new products and services.
    
2. Using data analytics to optimize internal operations
   
   This can be an easy win. Many telcos today are already digital, so data is abundantly available and ready to be used. And in most cases, the technology engineers are already there too, primed to understand it all.

The challenges of change

While it’s clear to see the potential of analytics, several challenges must be considered upfront before they become serious stumbling blocks. And with the current democratization of data-driven decisions within enterprises, it’s important that everyone has visibility of them.

• Culture: The traditional culture of management-driven decisions is shifting. Those at the middle level, or even the front end, can be now equipped with the insight to make real-time decisions. So, how will you adapt to this new model and accept that you’ll have to relinquish some responsibility?
• Priorities: When it comes to analytics, there are a lot of data sources to connect, and a lot of data to analyze and exploit. Also, around 80% of your initial effort will be taken up on gathering and managing the data itself before you even begin to analyze it. So, where do you start? Prioritization based on value and technical feasibility is vital.
• Success: Once you begin to use analytics, how do you assess its success? Many operators currently don’t because they’re afraid of what it will show — but it’s a key part of being able to make meaningful progress.
• Operations: To be able to make the most of analytics, your organization must adapt to accommodate it. Which existing operations will need to change? Where will the data analytics reside? Who will manage it? And who will sign off the budget for it?
• Evolution: Even the biggest operators in the world can’t keep up with the speed of change today. And it’s impossible to have instant access to every new piece of technology that emerges. So, which ones will you adopt, and who will drive these decisions? Which skills should your data engineers and analysts learn
  ?