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Building and Scaling an Analytics Business – Insights, Strategies, and Lessons Learned with Ashwin Mittal

Our business philosophy and the way we build our business is that we want to have larger relationships with people.

Building and scaling an analytics business requires a foundation of robust data infrastructure, a strategic focus on actionable insights, and an agile approach to evolving technologies. CEOs play a pivotal role in shaping the success of an analytics business through their leadership and strategic decisions. 

To give us more insights on this, for this week’s CDO Insights we have with us, Ashwin Mittal, CEO of Course5i. Ashwin has spearheaded its transformation into a leading analytics, AI, and insights powerhouse. With a rich background in corporate strategy consulting in India and the US, Ashwin envisions Course5 as a catalyst for a data-driven corporate culture, blending human and machine intelligence. A recipient of numerous awards for excellence, he is a sought-after speaker at global forums on leveraging analytics for business impact. Beyond the boardroom, Ashwin is a pioneer in India’s tech ecosystem, a dedicated advocate for CSR initiatives, and an influential member of organizations like Mumbai Angels, Young Presidents’ Organization, and Entrepreneurs’ Organization. Holding an MBA from UCLA’s Anderson School and London Business School, Ashwin combines vision, strategic acumen, and a passion for societal impact.

AIM: Why did you decide to provide analytics services for organizations, including Fortune 500 companies, and how did you set your company apart in a market already populated with similar businesses?

Ashwin Mittal: So we’ve been in this business for a fairly long time. We are one of the earlier players and at that time, people had not even heard of analytics and AI was just something of science fiction. But for me personally, there are few things that led to the start of my journey. One is that I always felt science-based reasoning and, there was this quote, I think from Edward Demings, if I remember, “In God we trust, everyone else must bring data.”

Building and scaling an analytics business requires a foundation of robust data infrastructure, a strategic focus on actionable insights, and an agile approach to evolving technologies. CEOs play a pivotal role in shaping the success of an analytics business through their leadership and strategic decisions. 

To give us more insights on this, for this week’s CDO Insights, we have Ashwin Mittal, CEO of Course5i, with us. Ashwin has spearheaded its transformation into a leading analytics, AI, and insights powerhouse. With a rich background in corporate strategy consulting in India and the US, Ashwin envisions Course5i as a catalyst for a data-driven corporate culture, blending human and machine intelligence. A recipient of numerous awards for excellence, he is a sought-after speaker at global forums on leveraging analytics for business impact. Beyond the boardroom, Ashwin is a pioneer in India’s tech ecosystem, a dedicated advocate for CSR initiatives, and an influential member of organizations like Mumbai Angels, Young Presidents’ Organization, and Entrepreneurs’ Organization. Holding an MBA from UCLA’s Anderson School and London Business School, Ashwin combines vision, strategic acumen, and a passion for societal impact.

AIM: Why did you decide to provide analytics services for organizations, including Fortune 500 companies, and how did you set your company apart in a market already populated with similar businesses?

Ashwin Mittal: So we’ve been in this business for a fairly long time. We are one of the earlier players, and at that time, people had not even heard of analytics, and AI was just something of science fiction. But for me personally, there are a few things that led to the start of my journey. One is that I always felt science-based reasoning, and there was this quote, I think, from Edward Demings: “In God we trust, everyone else must bring data.”

That really resonated with me, and I think he also said something, “Without data, you are just another person with an opinion.” Opinions are fine. But I mean, they’re not enough. So Opinion should ideally be really close to the last 20% or 30% or whatever per cent, but the journey should start with data and evidence-based reasoning. It was always my view. And I always had a passion for math and statistics, even as a discipline. Unfortunately, I didn’t end up studying it further. I studied more conventional finance and business, all of those fields, but I always had a passion for those subjects. And just going a little bit into history. I had set up a small tech services business in the early 2000s, tech services and BPO sources business. And I managed to build a nice small little business. But then I realized that one can’t be a generic business. And be a small midsize company. So, I applied whatever I had learned in my business education. I said, Okay, I need to pick a strong growth niche area and build a leadership position there. So, I did my own study of what was called KPO at that time. If you remember the time, that was the broad terminology given to the industry, and I picked analytics as what I thought, at that time, was really going to be a future. And so this is when I embarked on building this business; that’s the story.

AIM: What challenges did you face when starting an analytics and AI solutions company, and how did you formulate a strategy to overcome those challenges and facilitate the scaling of your business while minimizing risks, especially considering the often-difficult transition from zero to one and the subsequent journey from one to 100?

Ashwin Mittal: I think I do agree with you. The zero-to-one is very, very difficult. Starting with nothing. But honestly, even the one to ten, the ten to hundred and the hundred thousand, we are all learning every day. As we are going through our journey, and thousand to ten thousand and whatever. All those stages have their own new answers, and at every stage, you have to relearn and unlearn what you learned before. Yes, there are some principles that you establish, and if you’re able to carry them forward, that’s great. And you should carry your core values. But every stage has different symptoms and different ways of building your company.

So, right at the beginning, I would say that one key thing is, really, who are the few key people that you are able to get around you? I also invest in startups, and I say compromise, anything else when you have less money or fewer resources, but don’t compromise the quality of some of the key people who start the journey with you. 

So I think that’s really critical, and then, beyond that, you just need to be really tenacious. You really need to be willing to do anything and everything yourself. I mean, I remember sitting up every night till midnight, cold calling clients on my phone. Of course, now we have a sales team of 30 – 40 people in the US and this and that. But I have made dozens of cold calls to get the first few clients. So you have to really be tenacious and willing to have the door shut in your face. You have to be a little shameless, and you have a lot of self-belief. But of course, self-belief is not with an attitude that you know everything. It’s an attitude that you are always willing to learn and adapt and always a keen listener. 

AIM: How has the process of clients seeking data science and Gen AI solutions evolved from the early days to the present, and what were some of the initial challenges and problem statements you encountered when you first started working with clients in this field?

Ashwin Mittal: So, in the early years of our journey, we were not in a position to help someone define a problem statement. We had a set of capabilities, and we went in and had to convince people that we could take the problem statements, convert them to action, and deliver some meaningful results. In most cases, we went back to very early as we were entrusted with only parts of the process. You do a certain part of the process, the rest of it I’ll handle, which was a painful part for the client, or they didn’t want to do this. So that has moved, that is more meaningful over the years. And in many ways. So, today, we are with the client at the point of the business problem. As you rightly said, it’s more about the business problem and what the use case is than applying certain technology. Technologies: It’s not easy, obviously, to develop these capabilities, but technology is available. But the key is, and as you rightly said, again, initially, you said business function and math and technology all come together.

So now we are brought to the earlier stage, and we prefer where the business problem is getting defined. And in that process, we help our clients figure out the right solution. What is the right analytical work product that you need? Should we be applying AI technology? Should we not be applying? In some cases, you still may not need AI technology; sometimes, it’s something which requires something very basic and simple, and in some cases, it requires some advanced deep learning. In some cases, it may require Generative AI. So it depends; we have to figure out what the right technology and solution are. Then, we work with the client often, in some cases, as part of the solution. But in many cases, more and more often, end-to-end programs, where we are driving the end-to-end process from actually working on the data and actually working on driving business impacts. So, today, our focus is on driving business impact.

With certain clients whom we really have deep relationships there, you can say we have this privilege with some clients where they treat us as with a partner on the table when they are thinking about the next steps of their business and how they should plan their business and around that, how they should plan, leverage of data, leverage of AI. We have the provision, in some of those cases, to be at that table and help them discuss and decide.

AIM: What is your approach when a customer approaches you to build a Gen AI or data-driven technology, especially when it’s in an early innovation phase? How do you decide whether to take on such projects, considering the need for real business value and scalability in the long run, rather than investing resources in projects that may not lead to productization?

Ashwin Mittal: Our business philosophy and the way we build our business is that we want to have larger relationships with people. We want to have larger relationships and long-term relationships; if you look at us, let’s say, top ten clients, the average tenure of those relationships is nine to ten years. Our client attrition at the top level is almost zero. We are very fortunate that we lose clients very rarely.

And we build those relationships over time. And the key term here is “business impact”. So, at the end of the day, what impact can you actually drive on the business? It’s not about giving. You have to give a great insight, a great model. And what does that result in, top line, bottom line, or customer experience? These are three things you can distil. There are other metrics. But we consider these metrics right at the very top, and we need to have an impact on one of these.

And so it’s like you rightly said, you have to be willing to say no if something doesn’t make sense. It’s really important if something is not making sense for the client, use case for that problem, you have to be willing to say no. But be willing to push back, and you need to then have the right talent internally who can understand and then re-architect the solution. This is why one major area of investment, along with our AI labs and other things, is SMEs, subject matter experts that are both functioning and industrial for certain verticals and certain horizontals that focus on certain business functions. So I think, in the long term, you have to focus on doing the right thing in the short term to build for the long term. Otherwise, it will come back to haunt you someday.

AIM: As a CEO navigating the rapid evolution of technology, especially in the data domain, how do you establish frameworks for your company to quickly adapt and offer state-of-the-art solutions? What processes ensure continuous adaptation, keeping clients competitive and delivering consistent value without getting into day-to-day details?

Ashwin Mittal: It’s not easy, but it’s something you have to keep working on. While the various things we do, maybe there are three high-level things that we really focus on. One is our investment in our AI labs. We were one of the first India-based analytics companies to carve out a separate function, which is totally focused on R&D. The function does not do any billable work for clients. It’s an investment from our end; it’s a team of AI scientists who are working on understanding the science that is being built by universities, academia, and big tech, along with various advisors that we are in touch with. Then, we work with our business teams to understand the demands of the client for the use cases and then bridge the two by building various technologies and applications that can be leveraged at scale to solve those problems using these technologies. So we don’t want to build Science. It is being built by academia and big tech, But we want to build applied technology on top, and you have to make it relevant to clients. So that’s very important. And along with that, what we do is the AI Labs ensure that it distils this knowledge and understanding throughout the organization. So when the AI labs are a small team for the entire organization of 1500 people, we ensure that this knowledge is being distilled to them. And the second initiative is basically what we call our Course5 University.

So, a Course5i university is another major investment of ours, and that university takes the most relevant content and is constantly working on upskilling and reskilling our existing team base. Today, for example, when we’re talking about Generative AI, you have, on the one hand, the need for some very advanced technical skills to actually build the models and things like that, but on the other hand, Generative AI democratizes, so many things. Because now, you have all these foundation models available, something like prompt engineering, anybody can learn. So we want to ensure that we build those very high-end tech skills, but at the same time, the entire team understands things and how to use these technologies at the grassroots level. That’s the second initiative. 

The third one is the most difficult. It is always work in process, which is culture. I read in a book once that the C in CEO stands for Culture. And I consider that my most important and most difficult job. And that’s something we always keep driving on, focusing on and working on building. One of our core values is Innovation. We have a set of core values we define for our culture. One of them is innovation, where you orient people to always be curious, always challenge the status quo, always understand new technology, and always find better ways to do things. So there is much to do to answer your question, but these are three things I would do.

AIM: In cultivating values like Innovation or respect in an organization, how do you measure their impact? Whether it’s instilling a culture of Innovation or emphasizing values like respect, how do you ensure success in implementing and sustaining these cultural shifts?

Ashwin Mittal: Very difficult. That’s why culture is not an easy subject. Culture is where the employee handbook leaves off. There is only so much you can put in the employee handbook because of the discretionary action of each individual; when they are given a choice and what they choose to do in that situation, where there is an unstructured situation, they have to make a selection. Each person we’re human; that’s what we’re good at. That’s what we’re faced with discretionary actions, and there’s not just a technical answer but a culture-related answer. So it’s not easy. For each one of our cultural attributes, we do have some of our measurement mechanisms. For Innovation, we will look at the kind of innovative projects that we’ve done. How many projects we’ve deployed? What our customers are saying about us in terms of Innovation. What our employees themselves are saying about Innovation. The voice of employees is very important in the culture of Innovation in the organization. So, it’s a combination of subjective and objective measures for each one of our criteria. But while I’m a data person, I believe that you have to measure, and what cannot be measured cannot be managed. Amongst all those things, culture is the toughest one to measure. So, there is a combination of subjectivity there. And that subjectivity is answered by going out and asking the people, whether our employees or our clients, and beyond that, we’re able to establish some objective metrics. The number of innovation projects, the kind of value we delivered back to clients and so on.

AIM: Looking back ten years, do you have any regrets, or would you do anything differently to further scale your analytics or data science endeavours, or are you content with how things have unfolded?

Ashwin Mittal:  I would say that I have no regrets. I thoroughly enjoyed the journey. If I said I did everything perfectly, Many CEOs would say that, and they’d be done. We are all faced with so many decisions every day, and I say, if you’re able to get, in hindsight, feel like he got 70 to 80% of them right, that means you probably have been very successful. And especially on some of those key decisions. But the key is to ensure that you’re always reflecting. You’re always self-critical, and you always learn from mistakes. We all have made some mistakes. I hopefully have gotten more things right than wrong. But you have to consistently keep learning and keep improving and doing. Be open to hearing from others, whether it’s your community, clients or team members.

AIM: What challenges did you face when starting an analytics and AI solutions company, and how did you formulate a strategy to overcome those challenges and facilitate the scaling of your business while minimizing risks, especially considering the often-difficult transition from zero to one and the subsequent journey from one to 100?

Ashwin Mittal: I think I do agree with you. The zero to one is very very difficult. Starting with nothing. But honestly, even the one to ten, the ten to hundred and the hundred thousand and we are all learning every day. As we are going through our journey, and thousand to ten thousand and whatever. All those stages have their own new answers, and at every stage you have to relearn and unlearn what you learnt before. Yes, there’s some principles that you establish, and if you’re able to carry them forward, that’s great. And you should carry your core values. But every stage has different symptoms and different ways of building your company.

So, right at the beginning, I would say that one key thing is really, who are the few key people that you are able to get around you. I also invest in startups and I say compromise, anything else when you have less money or less resources, but don’t compromise the quality of some of the key people that start the journey with you. 

So I think that’s really critical and then, beyond that, you just need to really be tenacious. You really need to be willing to do anything and everything yourself. I mean, I remember sitting up every night till midnight, cold calling clients on my phone. Of course, now we have a sales team of 30 – 40 people in the US and this and that. But I have made dozens of cold calls to get the first few clients. So you have to really be tenacious and you have to be willing to have the door shut in your face. You have to be a little shameless and you have a lot of self belief.  But of course, self-belief is not with an attitude that you know everything. It’s an attitude that you are always willing to learn and adapt, and always a keen listener. 

AIM: How has the process of clients seeking data science and Gen AI solutions evolved from the early days to the present, and what were some of the initial challenges and problem statements you encountered when you first started working with clients in this field?

Ashwin Mittal: So, the early years of our journey we were not in a position to help someone define a problem statement. We had a set of capabilities and we went in and had to convince people that we could take the problem statements and convert them to action and deliver them some meaningful results. And in most cases going back to the very early as we were entrusted with only parts of the process. You do a certain part of the process, the rest of it I’ll handle which was a painful part for the client or they didn’t want to do this. So that has moved, that is more meaningful over the years. And in many ways. So, one is today, we are with the client at the point of the business problem. As you rightly said, It’s more about the business problem and what is the use case than applying certain technology. Technologies, they’re not easy obviously to develop these capabilities but technology is available. But the key is and as you rightly said, again, initially you said business function and math and technology all come together.

So now we are brought in the earlier stage and we prefer where the business problem is getting defined. And in that process we help our clients figure out what is the right solution. What is the right analytical work product that you need? Should we be applying AI technology? Should we not be applying? In some cases you still may not need AI technology, sometimes it’s something which requires something very basic and simple and in some cases, it requires some advanced deep learning. In some cases it may require Generative AI. So it depends, we have to figure out what the right technology is and what the right solution is. And then we are working with the client often in some cases as part of the solution. But in many cases, more and more often end to end programs, where we are driving the end to end process from actually working on the data and actually working on driving business impacts. So today, our focus is gone to driving business impact.

With certain clients where we really have deep relationships there you can say, we have this privilege with some clients where they treat us as with a partner on the table, when they are thinking about the next steps of their business and how they should plan their business and around that, how they should plan, leverage of data, leverage of AI. We have the provision, in some of those cases, to be at that table and help them discuss and decide.

AIM: What is your approach when a customer approaches you to build a Gen AI or data-driven technology, especially when it’s in an early innovation phase? How do you decide whether to take on such projects, considering the need for real business value and scalability in the long run, rather than investing resources in projects that may not lead to productization?

Ashwin Mittal: Our business philosophy the way we build our business is that we want to have larger relationships with people. Where we want to have larger relationships, and long-term relationships. If you look at our, let’s say, top ten clients, the average tenure of those relationships is nine, ten years. Our client attrition at the top level is almost zero, we are very fortunate that very rarely we lose clients.

And we build those relationships over time. And the key term here is “business impact”. So at the end of the day, what impact can you actually drive on the business? It’s not about giving. You have to give a great insight, a great model. And what does that result in top line or bottom line or customer experience? These are three things you can distill in. There are other metrics. But we consider these metrics right at the very top and we need to have an impact on one of these.

And so it’s like you rightly said you have to be willing to say no, if something doesn’t make sense. It’s really important if something is not making sense for the client, use case for that problem, you have to be willing to say no. But be willing to push back and willing and you need to then have that right talent internally, which can understand and then re-architect the solution. Which is why one major area of investment along with our AI labs and other things is SMEs, subject matter experts that’s both functioning and industrial for certain verticals and certain horizontals that focus on certain business functions. So I think, in the long term you have to focus on doing the right thing in the short term to build for the long term, otherwise, it will come back to haunt you some day.

AIM: As a CEO navigating the rapid evolution of technology, especially in the data domain, how do you establish frameworks for your company to quickly adapt and offer state-of-the-art solutions? What processes ensure continuous adaptation, keeping clients competitive and delivering consistent value without getting into day-to-day details?

Ashwin Mittal: It’s not easy, but it’s something you have to keep working on. While the various things we do, maybe there are three high level things that we really focus on. One is our investment in our AI labs. We were one of the first India-based analytics companies to carve out a separate function, which is totally focused on R&D. The function does not do any billable work for clients. It’s an investment from our end, it’s a team of AI scientists that is working on understanding the science that is being built by universities, academia, big tech along with various advisors that we are in touch with. And then working with our business teams to understand the demands from the client for the use cases and then bridging the two by building various technologies, applications that can be leveraged at scale to solve those problems using these technologies. So we don’t want to build Science. It is being built by academia and big tech, But we want to build applied technology on top and you have to make it relevant to clients. So that’s very important. And along with that, what we do is the AI Labs ensures that it distills this knowledge and understanding throughout the organization. So when the AI labs are a small team for the entire organization of 1500 people we ensure that this knowledge is being distilled to them. And the second initiative is basically what we call our Course5 University.

So a Course5 university is another major investment of ours and that university takes the most relevant content and is constantly working on upskilling, reskilling, our existing team base. Today, for example, when we’re talking about Generative AI, you have on one hand, the need for some very advanced technical skills to actually build the models and things like that, but on the other hand Generative AI democratizes so many things. Because now, you have all these foundation models available, something like prompt engineering, anybody can learn. So we want to ensure that we build those very high end tech skills but at the same time the entire mass of the team understands things and understands how to use these technologies at the grassroots. That’s the second initiative. 

The third one is the most difficult. It is always work in process which is culture. I read this in a book once that the C in CEO stands for Culture. And I consider that my most important and most difficult job. And that’s something we always keep driving on, focusing on and working on building. And one of our core values is innovation. We have a set of core values we define for our culture. One of them is innovation where you orient people to always be curious, always challenge the status quo, always understand the new technology, always find you and better ways to do things. So there is much to do to answer your question, but these are three things I would do.

AIM: In cultivating values like innovation or respect in an organization, how do you measure their impact? Whether it’s instilling a culture of innovation or emphasizing values like respect, how do you ensure success in implementing and sustaining these cultural shifts?

Ashwin Mittal: Very difficult. That’s why culture is not an easy subject. Culture is where the employee handbook leaves off. There is only so much you can put in the employee handbook because the discretionary action of each individual, when they are given a choice and what they choose to do in that situation, where there is an unstructured situation, they have to make a selection. Each person, we’re humans, that’s what we’re good at. That’s what we’re faced with, discretionary actions and there’s not just a technical answer but a culture related answer. So it’s not easy. We do have for each one of our culture attributes, we do have some of our measurement mechanisms. For innovation we will look at the kind of innovative projects that we’ve done. How many projects we’ve deployed. What our customers are saying about us in terms of innovation. What our employees themselves are saying about Innovation. Voice of employees is very important about the culture of innovation in the organization. So it’s a combination of subjective and objective measures for each one of our criteria. But while I’m a data person, I believe that you have to measure, and what cannot be measured cannot be managed. Amongst all those things, culture is the toughest one to measure. So there is a combination of subjectivity there. And that subjectivity is answered by going out and asking the people whether our employees or our clients and beyond that, some objective metrics, we’re able to establish. The number of innovation projects, the kind of value we delivered back to clients and so on.

AIM: Looking back 10 years, do you have any regrets or would you do anything differently to further scale your analytics or data science endeavors, or are you content with how things have unfolded?

Ashwin Mittal:  I would say that I have no regrets. I thoroughly enjoyed the journey. If I said I did everything perfectly, Many CEOs would say that, and they’d be done. We are all faced with so many decisions every day and I say, if you’re able to get in hindsight feel like he got 70 to 80% of them right that means you probably have been very successful. And especially on some of those key decisions. But, the key is to ensure that you’re always reflecting. You’re always self-critical and you always learn from mistakes. We all have made some mistakes. I hopefully have gotten more things right than wrong. But you have to consistently keep learning and keep improving and doing. Be open to hearing from others whether it’s your community, clients or team members.

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