Interview: Ramkumar Ravichandran, Visa on Actionable Insights – Easier Said Than Done

We discuss Analytics at Visa, adapting to the Big Data world, gaps between expectations and delivery from Analytics, delivering Actionable Insights, and tools/technologies used.

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.

Here is my interview with him:

Anmol Rajpurohit: Q1. What do you do for VISA?

visa-logoRamkumar Ravichandran: Analytics at Visa is responsible for generating actionable insights from data and helping with decision making for various stakeholders across Product, Marketing, Sales, Relationship Management & Solution Delivery. Our operational philosophy is “Success of our Customer is our success”.

AR: Q2. In the last few years we have seen a sharp rise in the magnitude and complexity of data. What measures do you recommend to businesses to adapt to this new world of Big Data?

RR: The overarching need is to ensure that data is reliably and efficiently captured, processed, managed, analyzed and used in strategydecision making. It is also critical that users are able to understand the end product easily, intuitively and when and where they need it.

An Outcome Focused Strategy, where the Analytics/Data roadmap is driven from the high level Business goals will be the best way to ensure that right prioritization happens and key things are taken care of first and best way possible. It encompasses everything from right Instrumentation, Data management & Governance, Reporting, Analytics, A/B Testing, Research and most importantly Knowledge Management & Education.

Link to presentation

AR: Q3. What are the key gaps between what the leadership/stakeholders desire out of Analytics and what they typically get out of internal Analytics efforts?

gapsRR: Although the Customers typically use one word “actionability” to describe all the gaps, it typically encompasses everything from Turn-around time, Relevance of Analytics and fit of answers to big picture, access of the data/insights whenever and wherever required, ease of understanding of the insights and finally the trust around the numbers.

AR: Q4. How can businesses optimize the delivery of actionable insights?

actionable-insightsRR: Some of the above mentioned gaps will be addressed with the Technology getting better (speed, delivery and access) but others require proactive leadership from the Analytics world (relevance, fit and ease of understanding). We need to hire right skills (UX and Storytellers); train analysts to think backwards from the end goal/handle ambiguous questions and apply best Project Management principles (Iterative learning, Feedback from End Users, Analytics on “Analytics Practice”). But to make it all work, we also need to educate our Customers & manage expectations (set and exceed).

AR: Q5. What are the most common Analytics tools and technologies that you use?

analytics-toolsRR: Depending on the problem being solved, the methodology varies (Data, Business or Advanced) and so do the data (API logs, Clickstream, Transactions, etc.) - which means I use any or all of the tool kits (Microstrategy, Tableau, MS Office, Google Analytics, SAS, SQL, Hadoop Hive, Power BI, Optimizely, etc.) depending on the need.

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.

Second part of the interview

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.