Chicago-based outsourced analytics company Mu Sigma has raised over $163 million in venture capital so far and has recently crossed the $100 million revenue mark. The company, which has a bulk of its operations in Bangalore, is one of the fastest growing technology startups to come out of India in recent years. Founder and CEO Dhiraj C Rajaram spoke to StartupCentral a couple of weeks ago about the company’s journey to $100 million revenues. Scroll below for the transcript of the conversation.
The motivation to start Mu Sigma
We got started in 2005 so we are no more a startup. The company is more than $100 million of revenue. We are doing about $10 million per month. We are today the largest analytics services company.
The vision (when we started) was to help people institutionalize data driven decision-making and analytics and our perspective was that given the deluge of data that is happening you will need a lot more applied math. So that’s where Mu Sigma came in and we wanted to build a company that could do that. Solving the problem was the biggest motivation.
The motivation to become an entrepreneur
You can have a career or go and change the world. There is a no way a career can compare with what we are doing at Mu Sigma. The company has grown about 1000 per cent over the last three years. We are funded by the guys who put money into Google and Facebook. We work with more than 100 Fortune 500 clients and most of whom are Fortune 100. And that sort of journey… there is no doubt that you would think about a career working for someone else when you can go and build something which can be game changing and make so much impact for customers, employees, stakeholders and investors. I think it was an easy decision.
Let’s say that if I was making $250,000 per annum, worse come worse, I can always find a job. I have the education and everything else. And I will lose a bit of savings. But if you are able to do this, then it’s game changing for so many people — so giving up a career is a small price to pay.
Seed funding the venture
The seed funding was from my resources. I sold my home in Schaumberg and told my wife that I am going through my middle age male menopause and I need to start this company. And she knew that I have been a crazy guy throughout my life and said that if you decide something nobody can stop you, so go ahead and do it. I was in Chicago, living there and travelling to Bangalore and spend 15-20 days in Bangalore and 10 days in Chicago. So I was traveling pretty much every month.
The first nine months
The first nine months was extremely difficult as I was the single employee in the company. Our customers were talking to us but it was hard to convince the employees to get into this crazy idea. But I think the first big milestone was Microsoft. They (Microsoft) became our customer and gave us a chance and eventually a large insurance company also started working with us. That resulted in the traction happening. After those first nine months we put together a team of about 10 people and ended that year with 20-25 people. But bear in mind this is not a people business. Initially all our employees were based in Bangalore and I was in Chicago but soon we started hiring in Chicago as well. We hired some of my college mates from the university of Chicago. In fact, they were emotionally blackmailed into joining the company!
The business model and how it has evolved
We have stuck with the same business model when we started the company. The business model is the fact that decision sciences and analytics, which is the combination of math and business technology is going to have to become mainstream and as it becomes mainstream you will have to take an occult science and demystify and democratize it. Which would mean you have to build the right people and processes and technologies. Those technologies and even IP properties and algorithms all of which should be as usable as possible. So this is a long journey.
The buzz around Big Data
I think you should not focus on big data as much because it is a hype. I think analytics and decision sciences is far bigger. Big Data is a small portion of decision sciences and analytics. Vested parties are creating hype in my mind but the real thing here is how people make decisions and how they use analytics. Big data is not a new concept. It has existed for quite some time. But the amount of data we have today and the technology that you have to distribute that data and use cheaper computing resources in a redundant manner has enabled us to do things… solve certain complex problems efficiently. Do all the problems in the world need complex solutions? Probably not. But yes, there is a hype cycle around that and we all like to talk about things in the hype cycle.
The difference between Big Data and decision sciences
I think it’s important to differentiate between big data and decision sciences and analytics. The concepts of big data will be used with an understanding of statistics, econometrics and operations research and applied math to help people make better decisions. It is the combination of all of these things which will eventually create value. It is of no use if you have a big hammer but don’t have a big nail. So it’s very important to understand the full ecosystem.
I think (when we started out) the industry understood that data is just exploding . I think the industry understood that the number of signals that we see today in our world is far higher than what we have seen before. And the ability to synthesize and process those signals is going to become very necessary. That is not going to happen if you don’t have an interdisciplinary experience of business technology and importantly applied math. So a company had to be created that would work with the business aspect like a consulting firm, the technology aspect like an IT firm but also bring in the applied math aspect.
So if you look at a company like MuSigma, you can call it a mixture of a design shop like an agency or a consulting firm or the combination of a design shop and laboratory, which uses applied math, econometrics, statistics, operations research and artificial research. The way I like to describe it is Art plus Science done at scale but thought of as whole.
Raising venture capital
We did not need the funding, the funding felt the need for us because the company was doing well and others wanted to invest in the company and they came to us to a large extent. Until now whoever we have taken money from, they wanted to put money into Mu Sigma. We took the money as we thought it would help us de-risk ourselves a bit and give us access to networks. So those are the reasons why we took money from Sequoia as these are the guys who have invested in Google, Apple, YouTube, Instagram, etc. About 20 per cent of Nasdaq-listed companies is funded by Sequoia Capital. So that’s interesting. Then you have General Atlantic Partners, one of the best private equity investors in the world. So we have the best West Coast guy and the best East Coast guy.
Value addition by venture capital investors
They were helpful. But were they game changing? No. They were good people who you could get some interesting thoughts from. You could get discipline into your company, you could get good governance and things like financial governance, audit governance. But the core of your company is you and what you are thinking. All of these are helpful, meaning they are not harmful, but having said that giving it more credit than necessary is also a disservice to future entrepreneurs. Entrepreneurs have to realize that they have to build companies. Investors cannot build companies for you.
We have enough competition. We have completion from systems integrators like the IBMs, Accentures and Deloittes of the world who are basically saying we will build great tools. Now that we are building these great tools we need to be able to do more with the data and we can do some decisions and analytics sciences for you. Then you have the data companies like Nielsen and IRI who basically are providing interesting data but on top of that they would like to be able to provide value add services. Then there are BPO companies who are doing some interesting things all the way from finance and accounting and everything and they want to be able to do more and they want to go up the value chain. Then you have the consulting firms like McKinsey and BCG who understand the fact that they have to look at the data and do new things. So I think, the first three are on the left of us and the last two are on the right of us.
Goals, next steps after $100 million revenues
We have much to do. We have just hit 100 million. IBM has been around for 50 years. Give us at least 20 years and we can show you what we can do. We started off as a common noun, we are today proper noun where we are getting people to talk to us, have investors to put money in us and customers who approach us. What we want to do is be a verb. People should say, why don’t we just Mu Sigma it! Why don’t we just Mu it! That’s what we want to do. We want to be like Intel Inside. Every company should have a bit of Mu Sigma inside it.
If you look at the number of years a company took to make $ 100 million revenues, TCS or Infosys Technologies took around 10-12 years. Cognizant took eight years but they did not start from zero. They started from $30 million because they were Dun & Bradstreet. Mu Sigma has done this in seven years. From that perspective it’s game changing and it sets new paradigms as it’s not purely services nor is it purely products. So it’s a combination of products and services and a combination of all of this coming together.
We are also proud to be associated with India. A large India presence was not an afterthought but a forethought. Mu Sigma could not have been built in any other place but India.
We don’t know when it will happen. If it’s the right thing for the company we will do it.
Transcript has been edited for readability. Image courtesy: Mu Sigma