How Vodafone Are Innovating With Data

Manu Kumar, Their Head Of Data Science, tells all


Ahead of his presentation at The Predictive Analytics Innovation Summit in London on May 13 & 14, we spoke to Manu Kumar, Head Of Data Science at Vodafone, about his role, the spread of analytics and how Vodafone are using data.

Manu has 14 years of experience in Marketing and Sales functions across Technology, Telecoms and Retail. His educational background includes a Masters in Applied Mathematical Science from UC Berkeley with a Bachelors in Engineering. He currently leads the Targeting & Optimization data science team at Vodafone Global Enterprise. The primary focus is to use a range of analytic methods to derive opportunity areas for B2B telecom leveraging every available source of data.

Innovation Enterprise: How did you get started in analytics?

Manu: After a failed stint in silicon valley, i joined a Fortune 50 retail company in their analytics team. Retail tends to have very clean and structured datasets and most of the analytics could be performed with Access and Excel. So it was an easy lead in to the world of analytics for me.

Are there any recent innovations in the analytics data science community that you see as a ‘game changer’?

Absolutely, there is a massive wave of disruption in data science. This is led by the commoditization of algorithms and open source analytics languages like R. When you combine these with faster processors and advances like visual analytics, it gives you an unprecedented flexibility to crunch your datasets to find patterns and proxies. In the algorithms space, machine learning techniques are a game changer. Of course the secret sauce is to find the right solution for the right business problem and that's where we as data scientists come in.

What are the unique challenges facing Vodafone that you are looking to solve with data science?

Vodafone is very progressive when it comes to the adoption of data science. My challenges are primarily to ensure that I reward the faith of my stakeholders with exceptional solutions when they need it. Secondly it is to scale up my teams with the right skillsets and to institutionalize the data scientific mindset.

What will you be discussing in your presentation?

The presentation will have 3 sections:

1. The challenges of finding B2B opportunity areas given the unique datasets and business realities

2. Advanced analytical techniques e.g. Machine Learning that have helped us deliver exciting results and some that have failed.

3. Democratization of insights i..e how we work to ensure that all business functions can benefit from our analyses by creating apps and tools. 


Read next:

Working At The Boundaries Of Aesthetics And Inference