Uday Hegde’s Journey with USEReady: From Inception to AI-Powered Future

In the ever-evolving landscape of data and analytics, there are individuals whose leadership shapes not only their organizations but also the industry at large. Uday Hegde, the CEO of USEReady, is undeniably one of those trailblazers. Headquartered in the bustling hub of New York, USEReady is a data and analytics firm that thrives on the mission of turning data into a potent competitive advantage for businesses.

But what sets Uday apart is not just his role as a CEO; it’s his unwavering commitment to values that resonate with both clients and colleagues. At USEReady, the core principles of Customer Centricity, Community, Continuous Improvement, Integrity, and Humility are not just buzzwords—they are the driving force behind every decision, project, and initiative.

In this interview, we embark on a journey alongside Uday Hegde, charting the course of USEReady from its inception to its AI-powered future. We explore the company’s growth, its impact on the data and analytics sphere, and the invaluable insights Uday has gained during this transformative voyage.

AIM: Could you walk us through the origin of the name ‘USEReady’ and the thought process that led to it? Starting from the very beginning, we’d like to understand the journey of how ‘USEReady’ evolved into an AI-powered company specializing in analytics and data science.

Uday Hegde: Very good question. I get asked that a lot. In fact, the name USEReady, the user, is symbolic to a citizen data scientist who is trying to wrangle data to make decisions every day. In our naming, what we are really addressing is our marketplace and why we exist. USEReady exists to make this community of average, daily decision makers or citizen data scientists to be successful with data hence the name USEReady.

AIM: When did you recognize the potential of the data industry to become significant? What drove your belief in your ability to succeed in this field, especially in the face of growing competition and the established IT presence in India offering similar services?

Uday Hegde: It’s actually an interesting question. We started in 2011, it was right around when I decided to quit my corporate job and decided on this journey of entrepreneurship with some hunch. If you look at the general technology industry like the PCs in the 80s, Internet in the 90s, then mobile, in 2000’s. It was a very interesting inflection point. It was sort of inevitable that it would be decades of data. What we did not know then and how that’s going to unfold if you recollect by then there were some companies that were already in existence doing data science as a business model.

And they were building huge service organizations around data science communities. And then the industry of Big Data and Hadoop, especially was the rage. And then if you look at that time, the BI industry, the traditional BI was at the end of the runway. And the cloud had not taken off. So, these were some of the situation elements at that point in time.

So we watched some of it due to the background that I had. It was going to be self-service analytics, that’s the key theme and that was the twist to the game. So where the data went to the users, to the hands of the decision makers or to the data went to the masses. And that’s what incubated us almost. In order to be part of the journey, we had to make some choices. Choices being you started a business so what or where do you want to go? How do you acquire customers? Those questions were difficult of course, for any company that starts. We decided to explore the channel around ISVs. Kid you not, we interviewed about a dozen or so BI companies that existed at that time and we landed on Tableau which was a very small company out of Seattle. Not a well known brand but I had some experience around them because while I was in my corporate job I’d run into them as a small tool had explored them a little bit. Thought it was the right timing to be partners with them. We were actually the second partner to them in the world. We almost incubated their channel and the partner ecosystem and their company being in Oklahoma. So we were the two first partners in that ecosystem that became massive. The self -service analytics is the theme that really took off from there. We became known as

Tableau people but most importantly to me it resonated with our name so right to help organizations and users to be successful with data that really helped us to get to those users.

Usually it is always the chicken and egg answer. My background in the past was the prior six, seven years I’d spent around data. So there was a background. However, when you launched, it wasn’t exactly clear. This is the way to go, and this will take us home. So, we had an idea of a zip code. I remember telling, in the early days, that if I could not build a 20 million dollar business between Hundred Street and Battery Park in Manhattan, then I should go back to corporate.

That was my sort of mental barometer or benchmark to say, this is where we should get to. And that was kind of the path. And if you see Manhattan as your sort of the perfect location to really build a business, in my mind. You have all your customers, you can, almost literally run to them or walk into them walking on the street. But you have to have the right product, right reason, right thinking to be there. So it was sort of the chicken and egg answer to you. It was a hunch but at the same time it was a deliberate choice of partners. It was not accidental.

AIM: How did your experiences in various companies shape your vision for what USEReady is today? Was there a specific point or set of learnings in your career that led you to the realization that there might be a need for a service provider like USEReady to complete the puzzle, enhance efficiency, and help businesses become more user-ready?

Uday Hegde: It’s kind of funny. I lasted in two parts. First part from a personal lens perspective. I always ask any one of one question: can you convince your boss to be a customer. So, most of the time, if you struggle, then, you know where to improve. But the point is, when that is a required quality to my mind to really be a good product founder. In the second part, yes, some of my customers and actually the fun fact, though, is this way. In my experience, no knowledge. you gain working at a job goes waste if you apply them. So, going back to your first question, which is Wall Street, Credit Suisse was a bellwether. It’s now part of UBS, but to be a great place to work for and learn a lot, amazing culture and it was a part of the door to the world of Wall Street for me. And I was really able to appreciate the value of that. In fact, I was there during the best times of Wall Street. I would say hedge funds, IPO, trading. They were all on fire in those days. Almost all Wall Street was firing on all the cylinders, especially real estate and the mortgage that did really well early on, before the crisis. So those are the best times to be in and we were incubating so many new concepts, new ideas, and a lot of capital flow available in the market. Interestingly,

I was part of the market risk team which actually helped me to appreciate complex financial instruments. How transformative the banking system is for the economy and we could really understand and appreciate all sorts of statistics around, risk calculations, whether it’s value at risk, and scenario, sensitivity, and the data storage systems to really make these things work, models and their complexity. A lot of the learning comes from those days. It sort of became the backbone and obviously, you’re very quickly successful as USEReady in the financial services because of the background. So you have a bedrock foundation. Going back to the other job I had where I started with actually not good at backrun but digital equipment. If you know that you’re almost like, older generation. Because most people don’t know what digital equipment is anymore. Deck was actually the pioneer in their days that was the highly innovative company before modern tech forms that came about. So the deck became compact. I started as a programmer and eventually ended up doing some commerce programming during the dot com later days in everything. That really improved my understanding of e-commerce business and the CRM domain. Life comes full circle and Salesforce acquired Tableau basically a big CRM player rights as Cloud Player acquiring the BI Leader. So it was kind of full spectrum for me. But I would like to say that those learnings were extremely helpful when we started building the business and taking off and especially selling to large enterprises. If you have an understanding of how they buy, that is a huge plus to thrive and succeed as an entrepreneur because every day we are in front of these customers, we know they’re buying cycles but otherwise, it would have been a huge gap.

AIM: What advice would you give to someone looking to start their own data science company today, considering the competitive landscape where services are evolving into products? What key considerations should they keep in mind to establish a successful company in the field of data and analytics?

Uday Hegde: It’s a good loaded question actually. I think every entrepreneur should know one thing. There is no such thing as perfect time. There’s no such thing as an early mover advantage. None of that is actually true. Everything is a myth in a way. But the key thing is, the question is really what do you want to be after you start. Things that you started, now what? So your priorities keep changing in the first hundred days to thousand days from thousand days to ten thousand days. So if you plan to stay around to see that 10,000 days, you can kind of, think about what are the components you need. So, starting as a services company was a product company. The key theme as a services company. You have very little capital, you need to bootstrap if you want. You could literally start in your basement. But if you make other choices then you have to plan for a lot more capital. That’s why many companies need angel investors and VC’s and through the rounds. All right, so there’s no such thing as perfect timing. One should be able to start any day.

AIM: In the competitive data science field, when should aspiring entrepreneurs start building their team? Should they hire data scientists before securing clients, or should they secure clients first? How can they effectively make their first sale with limited resources?

Uday Hegde: That’s very true. First of all, almost everything  solved in this space in the journey of entrepreneurship is a chicken and egg problem. That is a given. Most importantly, I think one perception is that hiring to a specific gig is one way to look at it, then you can absolutely go to gig economy, vendors like Upwork. You get plenty of talented individuals and in fact if you cannot afford probably, that’s your best bet. But if you decide to use a word which is employees. Then have a bigger connotation to that because, I think, hiring employees is a huge responsibility, One should know that. Meaning, you cannot just hire someone as an employee and then for a project and terminate. Unless you have plans for them for their career, for their journey, for their outcome, for their success, one should not hire as an employee. If you want to build a team then I think one should start with what is the first thing that you need to build a team with capital meaning you have to pay their salaries. So from the day one till date, right from the first higher to the current stage, we never missed the payroll. 

Because we were always planning for that. We knew if the person has to be hired, we should plan to take care of them and whatever we promise you have to come through. So, that is not something that you can take lightly. Planning for that is crucial. I’ll give you another line that I always tell people that in life surprise is good, in business surprise is not good. You should do everything possible to mitigate the surprises in business. It’s okay to lose money in a year. May not make a profit, whatever happens. But if that’s a surprise, that’s when it’s a problem. If you are going to make a million dollars, go for it, that’s fine.

AIM: What are your thoughts on the belief that profitability in data science primarily comes from serving Fortune 500 clients? How did UserReady manage to scale successfully from serving a few clients to a larger portfolio, especially considering the salaries paid to data scientists, and what strategies were employed during this scaling process?

Uday Hegde:  I think it’s an offering oriented strategy, to be honest. So you have to really decide on what you are selling? Who’s your target market? In general, I would say this way if you’re from scratch trying to find a business and trying to come up with an idea. I would always say that go after Rich People’s pocket. Why are you going after a beggar’s pocket? 

So rich people in the sense I’m saying are large enterprises, have deep pockets, and big budgets. They have money to spend and they can give you a piece of the valid, no problem and that’s good. But on the other hand, to go after that obviously everyone knows this so you’re not the only one who knows this. So now everyone’s after the same philosophy so that means as a company you have to build on the right foundation that you can do business with them. You have to understand how these big enterprises buy from companies. They always want to buy, but they just have to be the right supplier, who matches their credit. Just to give an example. If you want to do business with any reasonably sizable bank, they will ask you at least 10 million dollars of error and omission insurance for any mistakes your people may make. So think about it, 10 million dollars of error and omission insurance alone is going to be not cheap. And that money is not going to be back, it’s going to be gone. So if you are not able to charge them, enough money to even recover the premium. Forget about paying your employees and everybody else. Think about it, so you have to really have the offering that they’re willing to pay, the money that you need to run the infrastructure. And then there are decision cycles. Eight months, nine months, one year, budget cycles. So choices of whatever the choice you’re making in the industry, people, offering everything is connected there, so it’s not one angle alone. So think carefully.

AIM: When clients aim to enter the Fortune 100 special market, is this a critical factor in USEReady’s client acquisition strategy? Are there specific criteria or filters in place for evaluating potential clients, or is the decision primarily driven by project size and budget? How does USEReady approach these client acquisition considerations?

Uday Hegde: Client acquisition is a game of statistics. So if you look at anybody, any company that’s looking to acquire customers or clients, you have the same game of statistics that plays into you. Whether it’s social media, whether it’s  e-commerce, whatever strategy you’re deploying for marketing or demand generation is the same. If you can afford to pick up based on that, then you’re better off selecting those that have deeper pockets and very reasonably sizable wallet expansion. So by that guideline, from the get-go, we knew that we want to be playing in the large enterprise category. So we divide our offerings, we made choices to play with that community to be part of that. But that’s swimming among the whales. So you’re dealing with Accentures and Deloittes, IBM’s every day of the week. But then you need to know who’s your customer in that big organization. If you go to the same buyer that is buying from accentures of the world, obviously, they’re not going to buy from you. Most likely. So choose someone else, there are a lot of buyers within that big organization that have different budget levels and map up to that level of the organization with the right budget. So that’s the homework that one needs to do.

AIM: Is there a point at which a data science service startup reaches a scale where it can weather unforeseen challenges, such as black swan events, without the need to consider shutting down? How can this scale be measured for business resilience?

Uday Hegde: Yes, there is. So first of all, businesses again, don’t collapse on their own. If a black Swan event happens, the question is, is that a surprise? So I’m going back to my original premise. I said nothing in a business should be a surprise. So I’m asking, was that a surprise? I will tell you in my experience having interacted with a lot of founders also, almost all of them will say “It was not a surprise, but we ignored it potentially. Or it was really a surprise.” So now, if you see those two categories, the third category is “It was not a surprise. We knew it, but we prevented it or we did something.” Covid was a perfect case. Lot of companies could not survive, you can say that’s a black swan event. But, If you were in any part of the world, there was a possibility to survive your business, but don’t give up. I think many times the founders are entrepreneurs who give up before the business dies. For the eventual demise. To me that’s something to watch out for. Otherwise, businesses don’t die. If you have a customer paying your money, Even one dollar, the question is okay, you took the $1. How much does it cost you to service that one dollar

This basic math sounds seemingly basic. You can go back and do the research in so many companies. They don’t have this math right. The companies die because of that. The unit costs, the economics, It’s called unit economics sometimes is something that most founders don’t understand. You will never make a loss if you’re able to service that within a profitable measure. They always think the profits will come later at scale but you should know that most companies don’t have that. Eventually many companies, obviously there are many companies that are highly loss making even at scale burning cash, and eventually found some acquirer and stuck with it. But then otherwise it’s a basic arithmetic issue.

AIM:​​ What single piece of advice has been a constant guiding principle throughout your journey, particularly in never giving up and scaling a data science services company into an AI powerhouse?

Uday Hegde: This is something that I will say is a very common theme. Meaning everyone knows it but somehow don’t get it. I’ll tell you that. The core values. So I would say that one thing that is the core values is what you need to really pay attention to. You might have seen every company out there has a page on their website dedicated to core values. Walk into their office, It’s bold, written everywhere. But It is not practiced. And what is that then? What is the core value? So basically it’s about what you say and what do you practice? Whenever there is a gap, that causes a cultural problem.

So to me, that is one thing I would say, stuck with me. It was so amazingly surprising to me because I did not realize the value of it when I was in corporate, it came out. Even when I was running the business , I recognized that if you identify those core values of your business properly and stick with it, you could build any business. Doesn’t matter service or product or whatever, you’ll be fine. You’ll be successful.