Tiger Analytics is one of the fastest growing companies in the analytics space, with a team of 100+ data scientists and engineers working out of the US and India solving complex business problems in the areas of marketing and customer analytics, digital advertising, social media insights, sales analytics, forecasting, pricing, risk, and supply chain & operations for its clients in various industries. Infact, last year we listed Tiger Analytics as one of the 10 Boutique Analytics Firms in India you wish you worked for.
We spoke to Mahesh Kumar, who founded Tiger Analytics in 2011. Mahesh started Tiger Analytics with a desire to bring his experience in management science to help organizations achieve superior performance through the application of advanced analytics.
Prior to founding Tiger Analytics, Mahesh was on the faculty of Smith School of Business, Rutgers Business School and MIT Sloan School of Management. He has conducted research in the areas of data mining and statistical modeling and has successfully applied it to solve problems related to forecasting, pricing, promotions, and customer segmentation.
Mahesh holds a Ph.D. in Operations Research and Marketing from MIT Sloan School of Management, and a B.Tech. in Computer Science from IIT Bombay. We listed Mahesh as one of the most influential analytics leaders in India this year.
Mahesh currently lives in Silicon Valley part of California with his wife and son, and visits India frequently to work with his team in Chennai.
Mahesh Kumar: Analytics is a very hot area at the moment and it has been evolving rapidly in the last few years. The analytics ecosystem is multidisciplinary, and is a combination of certain specific elements. At Tiger Analytics, we knead mathematics & quantitative skills, engineering technologies, and most importantly business acumen to achieve meaningful results. When working with our clients, we provide a complete offering.
AIM: Interesting! Now, there is also a buzz in the industry about newer technologies such as artificial intelligence, data science, machine learning, Internet of Things, and so on … So, in all this gamut, how do you differentiate between them and where do you see Tiger Analytics fitting in?
MK: To answer this question, let me take you to the beginning of our journey.
Back when I was teaching data analytics in a B-School, I used to consult with several clients for their advanced analytics needs. During this time, I found that there were a lot of players in BI reporting, but there was a need for analytics which was at its nascent stages, and there was a severe shortage of talent. I started Tiger Analytics with a focus on advanced analytics – solving complex business problems using data science. We haven’t looked back since! [quote]Our DNA has been advanced analytics – specifically predictive modelling and machine learning.[/quote]
Right now, we have a team of 100+ people and majority of them are data scientists and data engineers, solving problems using advanced analytics. I’m not aware of any other company of our size or larger, with such a high percentage of data scientists/data engineers in their team!
Our core competency lies in advanced analytics – especially Marketing Science, Customer Analytics, and Operations & Planning analytics. We take pride in the fact that we have built a very high quality team of analytics professionals.
AIM: Beyond this, would you like to add any other differentiating factor?
MK: We have a significant internal emphasis on culture and work-life balance; people are happy to work with us. We strive to keep the atmosphere informal and authentic, and very professional.
Additionally, the nature of work and learning opportunities keeps our employees motivated and excited. If you give smart people low quality work, they will get frustrated and the overall work quality will suffer. We don’t want to enter that territory. Today, our attrition rate is the lowest in this industry, at about 5%!
Essentially, we are walking down the path that McKinsey took – a small company with high quality. We will scale gradually, while retaining our focus on quality of work, customers, and employees. Analytics is a rising field, with multiple players, and everyone is eager to scale quickly. If you are in a haste to scale, quality will suffer, eventually leading to client and employee dissatisfaction.
We have been extremely cautious not to take up any engagements where we cannot deliver really world-class solutions. We do not hire with the sole goal of scaling, because we don’t want to compromise on our work quality. We have taken the arduous journey of building a high quality team, and have a rigorous hiring process.
AIM: Continuing our discussion on the recruitment of your knowledge workers, what is the channel for recruitment, what is the typical selection methodology and what are the skill sets you look at?
MK: We hire both freshers and experienced candidates. Channels include online portals such as LinkedIn, Analytics India Magazine etc. Just a couple of months ago, we completed our largest single day recruitment effort at IIT Madras, where we made 13 offers. For lateral hires, we try to identify the potential of the candidate and not just past experience. We pay a lot of emphasis on whether a candidate would fit into our work culture.
Although we are relatively new, we have become a good brand over time because of the high quality data science work that we do. We are extremely selective in our hiring; say out of 100 resumes that we receive, we make one offer.
Today, our team in India is very diverse with people from more or less all states in the country, and very different backgrounds. About 3/4th of our team hold advanced degrees from prestigious institutions such as IITs, IIMs, IISc, in India and top US universities such as MIT, Stanford etc.
AIM: What are the biggest challenges that you face right now?
[pullquote align=”right”]We are constantly trying to refine and improve our solutioning and delivery processes, to do higher quality work quickly.[/pullquote]MK: The biggest challenge is the availability of talent. Analytics being a relatively new field, it’s difficult for us to find people who can do quality work right away. We are trying to solve this by finding people with the right aptitude and attitude, and training them at our Tiger Academy.
This is reflected in the perspective of clients as well. Businesses world-wide are just beginning to warm up to analytics, and they need to see quick wins to invest further in analytics.
AIM: Coming to the topic of ‘work’ at Tiger Analytics, tell our readers, if there is a structure in terms of the offerings? How do you categorise the work that you do?
MK: We are a pure-play in advanced analytics firm providing solutions and consulting services.
We have deep expertise in three areas – Marketing Science, Customer Analytics, and Operations & Planning. From the very start, were able to build high value offerings in these areas owing to my background in Operations Research and my co-founder, Pradeep Gulipalli’s expertise in Marketing and Customer Analytics.
We provide customized solutions to our clients. Where possible, we leverage our in-house data science and data engineering assets. These assets include solution frameworks, a slew of codified cutting-edge algorithms, automation of mundane processes, best practices etc. This enables our data scientists and engineers to provide solutions to complex problems in a third of the time and cost compared to if our clients have to do it themselves. That’s an attractive value proposition for our clients.
Till date, we have done more than 150 projects, worked with close to 50 customers; many of them are large enterprises and Fortune 100 companies. Thus far, we have always had happy customers and have even received testimonials lauding our work – we have not lost a single client till date because of quality.
AIM: That is commendable! Would you like to share some success stories with our readers?
MK: A very interesting example is when we worked with a leading railroad company operating in US and Canada. One of their biggest expenses is replacing damaged wheels. To identify damaged wheels, they have sensors on the railway tracks, which stream data when wheels pass over them. The problem was that, sometimes the sensors would be damaged, because of which they would give wrong readings, resulting in replacement of perfectly healthy wheels. This resulted in significant avoidable costs.
We built a warning system that would analyse the sensor data to predict if a sensor was damaged and needed inspection. This project was a huge success and today our algorithms process sensor data from railway tracks all over US and Canada.
I encourage readers to look at some of the case studies listed on our website.
AIM: So, now that Tiger Analytics is a leading brand in analytics space, what is the road ahead?
MK: I consider the past four years as a validation of our work in the advanced analytics space. Today we have more demand than we can fulfil.
We will continue to grow, work with more clients, and help them solve a wider variety of problems. We want to invest in building more capabilities that can be used to solve complex problems quickly, subsequently bringing value to our customers. We want to create a company that is sizeable and can have[quote]We want to create a company that is sizeable and can have significant impact in the advanced analytics space. [/quote]
AIM: Moving on, let’s talk about the analytics community as a whole. From across the industry, what do you think are the generic trends in analytics industry?
MK: Right now is a good time for analytics. 10 years back very few people talked about analytics, but now everyone understands what analytics is. Ability to collect more data coupled with the advent of efficient Big Data technologies has enabled new use cases in analytics, which were not possible earlier. Ever increasing computing power is bringing to life game-changing algorithms. Applications around Internet of Things and Artificial Intelligence are manifestations of the above.
There is an increase in the interest levels of customers to invest in analytics. My projection in that investment in analytics will grow five-fold in the next five years. At the same time, with the increased of portfolio of work, people will automate several parts of it, making adoption easier. Advanced analytics that can create differentiation for a client in mature markets, will still remain of high business value. Right now, advanced analytics is 5-10% of entire analytics industry, and this will increase significantly.
AIM: Anything else that you wish to add for our readers?
MK: If you are an aspiring data scientist or data engineer, I’d definitely encourage you to take up this career path – as an industry we are at the tip of the iceberg and the future potential is huge.
This is a great space for entrepreneurs as well – there are a multitude of unsolved high value use cases, and if you are able to identify and build the right solutions, you can create a significant impact.