Interview: Ramkumar Ravichandran, Visa on Customer-focus Mindset for Analytics

We discuss career advice, need for customer-focus, Analytics trends, desired skills in Data Science practitioners, and more.

As Director of Analytics at Visa Inc., ramkumar-ravichandranRamkumar Ravichandran is responsible for helping the Leadership & Stakeholders with actionable insights derived from Analytics. The business questions he works on span the whole spectrum across the Product, Marketing, Sales and Relationship. His team leverages any of the various options, i.e., Strategic analysis, Advanced Analytics, Text Analytics or Mining depending on the problem being solved.

First part of interview

Here is second and last part of my interview with him:

Anmol Rajpurohit: Q6. What is the best advice you have got in your career?

customer-focusRamkumar Ravichandran: The advice that has defined my career was from my first manager at Modelytics - Girish Subramanian. He said “Never ever make your customer work – your output should be exactly what the customer needs and can use directly. For that you need to think from Customer’s perspective, analyze from the end goal and deliver in the format that they need”.

Yes, it took me time, learning and practice to be able to fully deliver on that but today more than ever I realize the value of those words and how the “Customer Service” mindset helps one become a better person and professional.

AR: Q7. What major trends in Analytics do you expect to dominate in the next 2-3 years?

analytics-trendsRR: Over the past few years there has been a “genuine” revolution in data and analytics space not only in the awareness of the value add from data and insights but also in the way what is captured, processed, analyzed but also in the way it is consumed.
  • On data/technology front: The key trends are the big data (Volume, Variety, Velocity and Veracity), Cloud Storage, In-memory processing, Real time streaming.
  • On analytics front: The speed and nature of Analyses will become more independent– Machine Learning/Artificial Intelligence will become more prominent. Descriptive Analytics would mostly be automated; Predictive will become more Prescriptive and involvement of Analysts will be more strategic vs. low end inefficient time sinks.
  • On User Experience Front: Storytelling and User Experience & Design of output, Natural Language Understanding and delivery platforms.

The biggest disruptor will be the “Internet of Things” world with its own nuanced challenges (from Data Instrumentation to analyses to actions), but the learnings from today will help us evolve far quicker in that world.

Investments are being made, Innovations are being done to make them scalable/fast/efficient and integrations done to make all this enterprise ready.

AR: Q8. What key qualities do you look for when interviewing for Data Science related positions?

RR: With the operating philosophy of “Customer Success”, the fundamental need is for the person to be passionate about solving Customer’s problems. Going the extra mile “is not desired, but a mandatory requirement”.

We require the person to understand the end goal vs. the request from the Customer, suggest efficient and effective solution by choosing among the various data points and/or analytical methods available and deliver in a format that the Customer will understand and can act on right away. Also we would love the person to help us become a better team by constantly innovating and reinventing him/herself and the processes/products/technology/culture.

AR: Q9. What was the last book that you read and liked?

RR: The last book I read and loved was “The Rare Find: How Great Talent stand out” by George Anders. the-rare-findIt is a wonderful read on how crucial “right” hiring is (any level, any organization). Reading it helped me understand the hiring is not just an HR task and it cannot be just “Job Description” based, i.e., years of experience, education, skillsets and level based. It is much more than that – integration with the culture, attitude and vision of the company.

The key message that stuck with me was that we have to make “talent find process” iterative, cost efficient and quick – we have to constant adapt with changing times and needs. This becomes especially important when hiring for “new” needs where we don’t have history to learn from.

Disclaimer: The views/opinions/ideas in the interview is purely on personal basis and not representing VISA in any form or matter. It is based on learnings from work across industries and firms. Care has been taken to ensure no proprietary or work related info of any firm is used in any material.

anmol-rajpurohitAnmol Rajpurohit is a software development intern at Salesforce. He is a former MDP Fellow and graduate mentor at UCI-Calit2. He has presented his research work at various conferences including IEEE Big Data 2013. He is currently a graduate student (MS, Computer Science) at UC, Irvine.