Headline Partner

Shreenivasa Rajanala, Vice President, Global Data Science and Customer Insights Solutions, Bayer

Describe your career to date

I have been a key leader in the data science and analytics domain for over 13 years. I started off in consulting, working across customers covering a broad range of industries from pharma, life sciences, retail, insurance, and fast-moving consumer goods. Throughout, my focus was on understanding their business challenges and delivering analytics solutions.

I started the global analytics organisation at Bayer and grew from a single digit team to well into three figures. Initially the focus was on ROI analytics within key EMEA markets, but this expanded to broad-range commercial analytics and predictive AI solutions covering markets across the globe.

Data literacy is a key enabler of the value and impact from data. How are you approaching this within your organisation?

We tackle it in three pillars. First with a data strategy that links our business outcomes to key data enablers and capabilities, and that also links our performance to what the data team delivers.

Our second focus is on building the enablers: solutions that business users like sales reps, marketeers, and management use on a day-to-day basis as part of their business process. AI or data science is embedded within the solution, lowering the barrier to access.

The third and most important pillar is capability development within people. Identifying the skills needed, hiring the right talent, and equipping business users with the right skills to interpret data and take actions.

What are the key challenges to your data function that you are facing as its leader?

The main challenges we face is knowledge and action asymmetry. What I mean by that is two things: those who need to use the output of analytics or AI need to understand what they are working with, and where the gaps and drawbacks are to be able to make informed decisions.

Upskilling people in this is not to make everyone an analyst, but to instil an understanding of the questions to ask and how to interpret results; this exists for business users as well as data professionals. The changing and rapidly evolving world of AI has brought a lot for data professionals to also upskill and adapt.

What I mean by action asymmetry is in areas where data is rich and sparse at the same time –such as in pharma and outside markets like the US – it is important to connect analytics outcomes to actual actions, evaluate performance, and tune the model accordingly.

The risk-taking nature of this process is not amenable to many areas, hence the challenge in getting users to actually use the analysis is the key. Once executed, it is also critical to reach the right time scale to decide if it is working or not. Managing expectations and encouraging stakeholders to go along with this journey, and to drive action, is the most critical and challenging element as a data leader.

Shreenivasa Rajanala
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