Djagmo: Welcome to the Knowledge Entrepreneurs Show, where we celebrate the innovators driving change in the education industry. At EdisonOS we've worked with over 500 knowledge entrepreneurs to turn their edtech ideas into profitable businesses. Today I have the pleasure of introducing Gautam Munshi, a brilliant entrepreneur with exceptional leadership skills.

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Djagmo: Gautam has been praised as a wonderful visionary with a clear and articulate style of doing business. His analytical skills combined with his clear vision, have led to a successful career and made us touch on everything he does that is sure to make an impact in the world. We're excited to dive into his journey, insights and experiences, and learn more about how he's achieved such remarkable success.

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Djagmo: Stay tuned for an engaging and inspiring conversation with Gautam Munshi. Okay, Gautam. First of all, just wanna check some basic things. You're able to hear me all right, right. No audio problems. Perfect. I can hear you very clearly as well. And thank you so much for taking time out during the weekend and for, you know, agreeing to be a part of this podcast.

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Djagmo: I'm, you know, I was like, really looking forward to this and we are here finally. 

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Gautam: okay. Pleasure. Because, you know TeachEdison has been, you know, we've been kind of engaged with teachers for a long time, so always a pleasure to be part of the, any initiative of TeachEdison. 

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Djagmo: Great. Great. Thank you so much.

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Djagmo: That means a lot. So Gautam, we just had a call, you know, I had sent you a list of questionnaires and then, you know, you had called me a while back and you said, hey, Jag listen, I have stopped teaching. I'm not into education anymore. I'm into consulting. I was like, okay, fine. You know, I just took a bit of time, but then I realized, look, you've ran a training.

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Djagmo: Business from 2007 to 2021, which is like close to 15 years. And that must come with a lot of experience. You must have seen a lot during the period. You had, in fact, training academies especially, analytics based have been, in the trend not since 2007, probably since 2014, 15 maybe. That's when it kind of started off, but you started way earlier than that.

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Djagmo: So I thought, you know what, even if you've stopped your training business, at, in 2021 and if you're into consulting now, there is still a lot that I can pick from you. So I thought, okay, fine. You know, we'll still go ahead and have the podcast and then, you know, we can talk about how you also transition from education to consulting, because that also can add a lot of value to our listeners.

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Djagmo: So Gautam, let's start off with, you know, how you got into the training business in 2007. If you can, you know, give context and walk us through that entire story. Okay. In, you know, as much detail as possible, that'll be wonderful. 

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Gautam: Okay. So let's, let's just start off by saying that, so I started my career in banking.

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Gautam: So after my management and, one of my skills in banking was data analytics. In term, in those days, it used to be called Business Intelligence Unit. For which really got me into data sciences. And five years into banking, I really, you know, wanted to do a lot of work in data analytics and in entrepreneurship.

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Gautam: So this is way back in the year 2005 when I got an offer from a wonderful company called Marketing Technologies. Marketing Technology. It was just a, it was a startup. And,  with a very, nice bunch of founders and team. And, I moved out of multinational banking into startups and in the, and that is in, and in that startup I was doing leading of fairly large team of data scientists data.

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Gautam: In those days it used to be called data analyst. Now, the training business, the vocational training business, I really think that the boom happened with N I T in the way back in 2019. They were actually the pioneers of the data and they, NIT AP Tech, there was a company called gcc, cargo Tech, all and all these, they used to supply an enormous amount of manpower and technical skills for the Indian IT boom.

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Gautam: So there was always a thought process between the founders of marketing and given that I was able to recruit people from across outside the industry. There was no data analytics industry as organized as it is today. We are talking about early, or mid two thousands. So what happened was that we were structuring a plan that okay, and given that I was reasonably passionate and good in ensuring that fairly complex topics could be taught easily by explaining example .

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Gautam: Whatever.You know, the whole plan was that we may have an opportunity to build a HCL NIT equivalent of data analytics just like NIT. And itself, part of the same group, those separate companies. Same thing that analytics training would be a feeder to marketing and a lot of other players in the industry.

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Gautam: So that was the conceptualization of the business plan. 

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Djagmo: You, you said, sorry to interrupt. It is N I I T and HCL . Okay

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Gautam: Was Pioneer in the nineties, right. The Indian IT industry was fueled by, there were, if I remember, there were three top players. I'm sure I missed out. There was a company called gcc, calgo.

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Gautam: Training, something like that. Computer center there was aptech. And, and NIT, and you know, plus the, obviously the engineering colleges, but before the engineering college, they were the ones who really perfected the art of training our engineers and too, for the IT industry. So, analytics industry started really scaling up and growing from 2004, five onwards started scaling up.

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Gautam: So there was an opportunity for a training company, which is a feeder because many of the machine learning concepts were not taught in colleges then. In 2005 and, you know, in taking an analytics term, much of the statistics stopped at uni variate statistics, a maximum by variant. Advanced multivariate statistics was not taught in all the courses.

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Gautam: So that is what was required really. Plus knowledge of tools like SAAS and those Python wasn't too strong. We had, we had, SPSS was required by the industry. So that is how I conceptualized with the, some of the founders of marketing to think in terms of setting up this ATI . Now what happened was marketing got sold to wns.

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Gautam: This was in 2007 and I had already seeded this thought that, you know, running a good training academy, which is changing data scientists. For the industry right will be a good purpose and a good mission in life. Personally, it'll give me a lot of joy. So that's when I said, okay, let me start this analytics training institute.

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Gautam: So after the marketing sold off, we started at ATI, which was the subsidy of redwood algorithms that they used to call ourselves Redwood Associates. So we had a, we started off with training in the way back in 2007. So that was the journey to start. So after it got sold out I was able to quit my job and start this in full earnest.

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Djagmo: Got it. Great. Okay. And when you started off, you started off as an individual, like a solopreneur sort of a model.

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Gautam: Before we started off, the whole initial idea was it, it would be with some of the folks at Marketics. As a part of marketing. But then marketing got sold off. So then there was a very dear colleague and friend of mine, called Pavan Bhat, and I started off together. 

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Gautam: So Pavan was there, I was there and one of the founders of market X was mentoring and mentored us. So two of us started, we started the full journey in earnest on April 1st in 2007. So 2007 was trial, but 2001st April  late, actually May 22nd, 2000. Yeah, April when I think we took up the first office and stuff like that.

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Gautam: So that's how we started. 

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Djagmo: Got it. Right. So, but you said. You know, you said one important point while you were leading up to this situation in marketics, you found yourself kind of teaching or guiding people with a lot of stuff that was happening. I think that's probably Where is that where you thought that, you know, you could teach or you already had teaching skills from way earlier?  

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Gautam: thing was that even when I, we were in school, our school encouraged a lot of teaching right from, we were in Class six, seven, the school where studied. A school in Calcutta. So they organized workshops where all of us had to go and teach English to different parts of different schools in across Bengal.

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Gautam: So as a part of the Social Service league, so I used to enjoy that. Those were the times I used to love spending Sundays, you know, because it was used with, I was teaching anyway, so that was when I started. The second thing was in standard Chartered Bank where I was working. So as part of something called the Management Associate program, where every 18 months you moved to a different department.

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Gautam: Now I started off in analytics and the demand, obviously there were a lot of analytics which was happening, so the full cadre of analytics, in terms of the definition, all that was not,  structured then. So that is when the bank nominated four or five people who are from the sales department and call center department to become analysts and 

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Gautam: I took the thing that, okay, when I'm moving from one department to other department, I will get five people in place who can do the job better than me. So that was the third process. So that was when I had trained my first batch of analysts way back in 2002 within the bank. So there, there were, all the conditioners.

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Gautam: Anybody who was good in maths in the sales department and the customer service department. And there was a hiring spree, so we could only recruit analysts from internal bank. And there were, I think I told my senior, senior management that, don't worry, you give me five people who enjoy mathematics and in two months they will be trained.

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Gautam: And they did it and they have gone on to do very well. And that is when I said I enjoyed it. So same thing, which happened in marketing. So I was, we were getting, this is just like going tomorrow. So, and I was teaching and we needed talent in those ways. I talked about 2005, 2006. In term we needed people who could run economic models and statistical models.

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Gautam: So you had Madras economics, I think producing about, 80 to 100 students a year. I may get some of my numbers wrong. JNU was producing about 50 to 60. Delhi School of Economics was about hundred. And you had one or two of the other institutions producing maximum 300 people in the country.  So, obviously, and most of the people wanted to ISA, you know, what ISA was producing 20 people, 20 to 30 people.

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Gautam: They want, they were going PhD, so there were hardly hundred people left for, but we were getting business worth hundreds and hundreds of people. So something had to be done, so same, same discuss with the founders that lets, and the HR, there were department that will, recruit talented people in good in maths from engineering colleges, MCA, anywhere else as it is no rocket science.

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Gautam: So simple. It can be, you know, it's common. Every follows the steps. So we were able to do that and we were able to really scale up and that's when  I realized, okay, nta, we can really do a lot of work. So as soon as I stepped out, we had lot of students, lot of clients Where we were training.

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Djagmo: Great. Great. Now, now things fall into place as to, you know, now it's a whole picture as to how a So ATI journey, literally, you know, the first seed may have started back in 2002 when you were with the Standard Bank. When you train the first set of five people who are from sales and customer service. 

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Djagmo: Fortunate people. Those five people I think.

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Gautam: I was also fortunate, right. I got an opportunity to do that. So I think I was more fortunate. 

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Djagmo: Yeah, yeah, yeah. So you also discovered that, you know, you have this side to you, and then there's a whole market that is lying out there. And in 2002 you're saying that the people available to do this kind of job were only hundred per year in India and Okay, great.

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Gautam: credit goes to my first boss, a gentleman called, Joseph.

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Gautam: he inducted me into the Analytics team. He was also from a credit and collections background, so he was an India head BIU, what we call, he used to call analytic BIU, those days with this intelligence,So he was right head of this. So I owe, owe a lot to him. 

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Djagmo:so Gautam, when you say you trained people, you know, from whoever were interested in maths and who were good in maths, you know, you got them into this thing, but you yourself, were you trained like this by someone or you had a statistics background yourself?

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Gautam: Yeah, I had an economics management background, so I'm an economics major and an MBA. And as I mentioned, I joined, we were from Campus Standard Chartered and recruited us in something called a management associate program. Right.The first program in management associate program. In the first four years, the HR can put you in any department, anywhere in any branch of division of standard Chartered anywhere.

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Gautam: Coincidentally, so you don't really, it's like part of the general management pool of the bank, so my first unit, maybe because I'd given a preference I'd come during my training, I'd come to Bangalore in 2000. I like the city. Thanks. Pleasant weather, beautiful place. So I ticked off Bangalore as a first preference.

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Gautam: So I, there was an open, so educating, wanted to recruit somebody in Bangalore needed. Okay. They plugged me in into this unit. I had no knowledge of coding. I had no knowledge of anything. And there was, there were two people, I still remember the names. I had no knowledge. I was a liberal arts guy with an MBA degree and I needed to generate reports.

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Gautam: So there were two people. There was a guy called Rajesh covered from credit. He gave me something called Little SAAS Book. He said, Gautam learn this.  There was a guy, IT department called Ranga. He gave me a book on Unix. Okay. I learned, I looked at both of them and in one week I said, okay, let me start.

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Djagmo: Okay. Got it. 

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Djagmo: Those, uh, two books, just to repeat, the first one was, uh, the little SAAS book

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Djagmo: Okay. 

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Gautam: Saas as in software as a service, SaaS as in statistical analysis software. There was large company. So it's the first, so I did not have a too much of a background apart from MBA we are taught everything.

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Gautam: I mean, you know, but I did not have a, what I call a hardcore background in analytics, but I was an economic, so it comes to certain data analysis comes naturally. Not at all. And I, it's not just for me, given that I can say this with full confidence, given that if we have trained more than 10,000 people, it's not hard for anybody to pick up.

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Gautam: So, that is a very important thing. No skill is hard for anybody. 

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Djagmo: So, 2002, after all of them, you know, going abroad and stuff like that, only a hundred people left in India to today, 2023 I think today it's the other way around, right? There is a surplus of analytics people out there.

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Gautam: There's no shortage of analytics. There's no shortage. The, I think the only thing which is very important, which we definitely inculcated in ATI. See technical skills is one part of it, but there are two or three other important, thing which are required from a data analyst. Number one

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Gautam: Ability to connect the dots by interpreting data. It's not just about, running the machine learning model. If I'm getting this model and these five factors are important, which is coming out of my analysis, what can I do with it? How do I, how does this gets implemented on which challenges it get implemented?

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Gautam: So though is a surplus I would urge all people aspiring, not just for data. Everybody has to use data analysis in their careers. Everybody, I don't think this is even if you are a archeologist, there's a lot of data analysis in archeology in history. There's a lot of data there. So I'm seeing as it's important to, when one is looking at the data, one has to ask, so what?

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Gautam: What can I do with this information? The British, left India 1947. What were the technologies available then? What were the technology we got developed due to World War II? How did we benefit or did not benefit from that? So being able to connect two different data points. I'm talking somebody as diverse in an history should be able to connect.

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Gautam: A lot of data points and then interpreted, okay, the railways, the advancements in telephone, which got set up during any war, which happens a lot of technology and analysis gets developed during war times. Used to. The biggest computers get, so this whole data, what I would say to all, what is very important for anybody to understand is they need to be able to look at data and ask the question why.

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Gautam: And so what, and try to visualize and connect the dots. And the best way to do it is on a whiteboard. So in ATI, we used to try our best to have group discussions and case studies. And there's no right or wrong because right data analyst is a probabilistic science. You may think it won't rain, I may think it may rain.

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Gautam: There's a probability. So ability to ask questions, debate. That, that is required in an environment. Now, whether it comes in a college, whether it comes in the workplace, or whether it comes from a group of friends, aspiring data scientist has to create an ecosystem around himself or herself, which is able to facilitate this pedagogy.

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Gautam: It's not just about learning the how to run a regression model or you know how to run a cluster model. It's much more than that. That needs an environment, and that is what in our classroom at ATI, we tried our best to do in 

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Djagmo: Got it. Got it. Got. I think, I think this particular segment, you know, where I understood, I have no idea about data and data analytics and all those things, right?

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Djagmo: But I know the jargons and the popular words that are used by job aspiring people and all those things, so, I think this segment kind of, you know, addresses what to look for in somebody who's training people in that segment. I think, for starters, what I feel is after having spoken to you, especially after having understood your journey from 2002 to 2007, I think for starters, students can look into people, hey, you know, what were you doing before you started data analytics?

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Djagmo: Did you just learn and did you come or did you like really do something and come? I think that probably plays a huge role. Got it. And, Gautam, do you think today, you know, there's so many because, you know, the, the kind of company that we are, right? We are going and looking for people who are in training and we see there are a lot of people who are into data analytics, a lot of data analytics academies.

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Djagmo: So do you think, all of them kind of are in sync with this core that you told now, you know, are they sticking to this or has it become more of a, okay, this is a curriculum, let's just kind of, you know, finish this, portions and stuff like that. Is that what's happening today and is that, that organic lack of case studies and stuff?

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Djagmo: Do you see that or is it better than that? 

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Gautam: okay See, because I don't think I can comment too much on the other industries and the training institutes because it'll be wrong of me to paint everybody with a paintbrush, you know, because I'm not right this thing, because as, as I told you, last three years, we've been totally into consulting.

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Gautam: But having said this, I think it is more important for the student to develop. See one is telling the trainer that, okay, you have to teach me this. Now the, the trainer will teach the most trainers will teach,there's a, India is a large country, there's a population, so most trainers will teach the technical part so that at least the student can start accessing data, managing data, manipulating data.

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Gautam: But I would say that every aspiring data analyst has to understand that the technical part of it is only 30% of the job. 70% starts there. And for that, they just need to ask what is happening? What is the data around me? One is the work which they're given. First thing they should do is maybe start looking at the facts around them.

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Gautam: Look at, say, if they need go analysis, see, look at or take out all the data from Google and do an analysis their own locality, Was it better or worse than somebody else? Why was it better and worse? Say I'm in pin code ABC. I should be able to take out the data. This the government has given publicly.

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Gautam: The government has given a lot of data publicly. So every data center should take out some data assessed that important assessed, why is it, am I better off or worse of than best of the country? If I'm better off, why am I better off? If I'm worse off, why am I worth off? That can be derived from a statistical model.

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Gautam: Now, if the data analyst cannot do this, then building a career and data analysis will not be a good idea. They should enjoy doing this. So whatever they've been taught many times it's a fact that, okay, this is the latest wave, but they should just enjoy doing one piece of analysis on their own, which is not demanded by their employer or the teacher, and finding answers which impact their life around them.

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Gautam: Why is, it can be as simple as why is the price of tomatoes going from X to Y? Why is the price of oil going from A to B and that's it. If you can answer these questions on your own, using a good rigorous method of data analysis, you yourself will know. And that, I can assure you anybody who's in the teaching, I know I'm speaking on behalf of all other teachers, but as teachers, most teachers have, all of teachers are very passionate people. If a student comes and tell them that, I've made this analysis ,him/her. I've made this analysis, can you evaluate this and discuss this? There will be no faculty, which will say no. So the ball is also in the students court to pick up these skills. 

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Djagmo: Got it. Got it. Gautam. Gautam, if you may, you know, I'd just like to share a small thing.So last week I had a podcast with a person named Prudhvi. He runs an academic called, Supervised Learning. So when I was talking, the reason I'm bringing this up is that, you know, you said there has to be something out of the person's interest to stumble upon data analysis. And then when I was asking him how he got into it, he said this right? He was a electronic and electrical student, and he finished his engineering. And then he runs some placement company or some sort of startup himself. But then, he comes across this article in newspaper where, in the marriage, a husband was wrongly charged with a certain case. It falls under some 498 A section or something, you know, where the husband is charged with dowry, harassment and all those things. And then this kind of disturbs him. And then he you know dives into the rabbit hole. And then he gets all the statistics with respect to this in terms of how many, such cases are registered in India. And then, you know, what happens, this kind of shocked him, basically, because probably, you know, he imagined himself and you know, what it could happen and stuff like that. And then he said, this is, this research made him want more than a simple Excel tool to kind of slice and dice. And that's how we got into data analysis. And then he did a course, and then he started teaching. So, I just couldn't stop remembering about this particular thing 

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Gautam: exactly that is what is required. I also have a friend of mine. Somebody I know who runs. So I thought it was the same person, but this person. Yeah. But having said this is exactly what is required, you cannot become a data analyst until, unless there're sense of curiosity. If there's a case sense of curiosity into this world, the kind of tools and the data, which is there never in the history of civilization has this kind of data been there and generated. Yes. For example, let's look at, I don't, I mean, you are obviously much younger than me. Let me talk about myself. I'm 46. Yeah. But the age of say 25, I think if I were to chronicle my life from one to 25, they will be maximum hundred photographs, five photographs, a year. But television five will also be more, I'm think maybe two or photographs a year. Some holidays would've gone with my parents. Now, this data, if somebody sees this data, isolation, they will say, okay, Gautam had 20 events in his life when he was growing up. Oh, what a boring time if someone doesn't contextualize it. Whereas today, the world of big data, people have five photographs a day. Now that data can be, I mean if, it's available to, and you can run machine learning models, you can do the data capture. I'll just give an example on your own life also. If somebody does their data analysis on their own hard drive, everybody will have one terabyte of data and the whole story can be taken out. You can model your story and you can model. Okay. You, I'm sure people can model On Saturday mornings when the sun is bright, I am happier than Saturday. Those kind

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Djagmo: Okay. Now I get it. I was wondering, okay, where are we going with the photographs? Because what data are we gonna get? But then you're saying with the help of those expressions in the pictures and all those things, there is some insight to be taken on, is what you're saying

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Gautam: Which may be new to you. So I would encourage all data analysis, and they don't need to say that many. Come, sir, I want a project in a bank. I want a project with this. I tell them, dude, you look at your own hard drive, analyze your life, and then build a predictive model of where you're headed. Look at, look at your own, student data and see, okay, who will reach where in the career? That data is accessible, you can build a very large data set and a model on that. Now, if you can't do that, that's the domain. You know, your life better than anybody else. If you cannot build a predictive model for yourself, how will you build a predictive model for an organization? 

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Djagmo: wow, this is like fascinating. Nothing, nothing sort of fascinating. This is, okay, if there is somebody, you know, who's aspiring to be a data scientist, and if he's, he or she's, listening into this, and, you know, this is just one thing that is to be taken away from this segment, is that if you do not find a project, to do some data analytics, your life is their boss. Do some project based on your life and do some predictive analysis of your life. And, is that gonna be valued when you go asking for jobs or something Gautam?

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Gautam: you don't need to talk about the predictive model. You're building your life. But I, I can assure you that if anybody presents in an interview, it'll be interesting. But having said this, the skills one will develop in predictive modeling, being able to capture data, select the right models, understand the clusters which are driving you. Take the photograph how you're playing your cricket shots if you're a good cricketer and how you can model your shots better. So see, visual AI is the present and the future- autonomous driving, everything. So you have got your own photos and videos. You should be able to analyze that. Now that you apply it on publicly available data sets, Kaggle data sets, do some research, do some this thing. Of course it'll get valued and do projects with all analytics and consulting organizations are always looking to give out internship programs provided the candidate demonstrates that he or she's good. So for an internship program, if somebody comes and says that these are the kind of analysis I've already done on my own, I see no reason why any organization would reject them for an internship. And once you do an internship, then you're in the industry. Then, uh, then the second part is the first thing I would encourage all. Aspiring data scientist. Get yourself an internship. When you're in the internship. The second most important, don't go by what your friends and peer group are saying that only when you run a machine learning model, the value is there. The most important thing is do analysis, which your boss or your client can appreciate and value, and it gives them some more knowledge about their business, which they don't did not have before you came. If the analyst does these two things, their career is sorted. We focused on the customer. We focused. I think a lot of the analysts are not client focused. They're just interested in using Python to run one or two codes and not at all linking it to the actual implementation. Actual work in terms of can this get implemented? Can somebody make money out of it? Can my company where I'm interning make money out of it? Can my client make money out of it? Please do not. You not do in research just for the sake of research. Even, even in the biggest of research, there is a funding every research has to have an outcome, prospective outcome. So I feel too many people are doing very random analysis work especially people who are getting into the absolutely random, they're taking case studies and data testing on data sets. So there's a site which I really, really like, which I think all data analysts like it, it owned by Google is a site called Kaggle. So its world's number one data for to practice on data sets before even when move into Kaggle, it's important to look at data around you. Because what happens is the moment you get into Kaggle, you're comparing yourself. You Kaggle should be there. It has to be a part of everybody's, you know, toolkit. but that should be second phase.

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Djagmo: Got it, Gautam. I'm just gonna repeat a couple of lines. I think it's gonna be very important for people, you know, especially with data analysts or aspiring data analysts. So, Gautam said data analysts, most of them are not linking what they do with the clients. They're just doing random stuff in Python, running a couple of models. They're not linking it with how in reality it's gonna be implementative or is it actually going to change a company's direction or add value. I think this is a point to be noted. And I think instead of doing, 10 random stuff,if someone can do one such thing, like where, you know, you can link it to real life thing, I think says that's gonna add a lot more value to your resume than anything else. Got it. Thank you so much for that. Gautam, when we take, let us say, now we are talking proper analytics, training and everything that's associated with it. There are two aspects, right? when we come to analytics training. One is the business aspect of it, one is the teaching aspect of it. I'm, I see,  I'm supposed to find a lot of information about the business aspect of it mainly, but I'm like really,  more interested to ask this one question before I jump into that. So, if there is a young data analyst trainer, or a data science teacher that is listening to this, right? from a teaching perspective, not from a business perspective, from a teaching perspective, what are some of the insights that you can give them based on your experience, you know, that can make them really stand out and actually add value and make difference to people's lives?

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Gautam: Okay, so the first thing is education has to be enjoyable. Check if any of your students have any fear of analytics. Remove their fear. Spend half a day is required to giving, figuring out a way to remove their understand why is their fear and remove their fear. It could be because they're worried that after the course will they get a job? It could be because some math teacher in the past has carried them out of mathematics and technology. Now they're doing it because of a compulsion. So number one is remove any kind of fear to the subject. Number two, try your best to tell the students to learn concepts and read concepts, or go through videos on their own and discuss case studies in class. I would always focus on case studies in class. Don't go A, B, C, D, E, F, because in the real world, nobody, I mean, they're expected to solve business problems. So the first one is remove fear. Second one is solve case studies. And in that call solve case, don't spoon feed. Let them, even if it is wrong, see, I may come up with a totally gusty solution, I'm a student, but if there's no fear in class,  I present it and then you can correct me as a teacher on that. So it's very important to be for students to be able to don't spoon feed them. I think that is very, very important. Third one is whatever data analysis they have come, irrespective of whatever is our mother tongue, let them be able to present the insights in their mother tongue, because finally the brain is interpreting data. So there is numbers. We are interpreting data in our mother tongue. And what happens is many times I've noticed the student will try to explain the solution in English. Which may not be, which is not our native language. Allow the student to explain in their mother tongue and then your, because your what is most important first, the student has to understand how to come up with good insights, how to come up with good analysis. After that you can always, today you've got enough AI tools to translate, present, translate, do whatever presentation can be done later. But I would, so these are the three things. Remove fear, focus on case studies, inside presentation in their mother tongue

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Djagmo: This is, this is great. Yeah, just three things. You just,  I'm really amazed that you quickly, you know, told three important things because sometimes, a long list or, you know, you just beautifully pointed them out. Great. Removing fear is one thing. Focus on case studies and not the A, b, C of it, and then allow them to present their insights, what they understood need not have to be in English. English is just another thing, but most important is what insight they're coming out with because making them do it in English, they may lose a lot of insights because of the lack of language. Got it.  

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Gautam: Imagine if I have a, if I have a Chinese client and I'm expected good Chinese, I will be focusing more on the Chinese and presenting the analyst.

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Gautam: It's easier for me to do that. And have a translator. Translator into Chinese.

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Djagmo: Got it. Got them. Great. Okay, so any data scientist trainers, if you're listening and you know, if you think you're not doing some of this, I think probably this can really help you go to the next level as a teacher. So now if you're, you know, of course there could be great teachers who are doing exact things, what Gautam said, but they're kind of, you know, struggling or finding a little difficult to get enough students somewhere. You know, there are the business, skills that are lacking. So, Gautam, what would you like to tell these people who are great teachers, but you know, probably not great business people. 

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Gautam: Okay, that's reach out to me. I'll give you some tips in terms of, I think yeah, you can reach out to me, but, let me just answer this question. See, I think if your great teachers, attach yourself to a fairly successful platform. I mean, TeachEdison has a good platform for education. Right? Attach yourself to a good platform where students are already getting aggregated. That's one part of it. Alternatively,set up, make sure that all the students you're teaching, at least 50% of them land up getting good quality career breaks, after you teach them, word of mouth will spread automatically. Word of mouth spread. And that will only happen if you do, I mean, apart from, because a lot of students who are spoon fed and there, they will not be able to get a job in today's world, it's not easy for them to get a job. If it is, if you focus on spoon footing, they will not be able to form solutions and then they won't be able to clear interview. So you as a teacher may have spent a lot of time thinking that you work very hard at teaching them for next statement if then statement, everything. Or a, you know, logistic regression versus a cluster and all that you've taught them, but Right.  If the mug up answer any interview can do that and then they will, they might end up blaming the teacher and I was not taught properly. So it's better to teach five students really well, let them get the job. And,  it'll automatically grow word of mouth from there in their ecosystem. And I have a great digital footprint. Yeah. The other very important point, which I discovered during online, is that Which I shared in one of my LinkedIn posts also, see when you are studying something online, visualize yourself in a classroom. Imagine if there are 20 people who are coming and saying, hi, how are you doing? The notification coming, somebody saying, hey, come out for a minute. It's not gonna happen. Now, when we are online, you have your WhatsApp window open, you've got your Facebook open, you've got your email, it's an email open. You've got five. One idea, which I thought, I mean you can edit this out. The, you should have a lock. Okay. Lock features mode. The thing which is preventing online education to get adopted, because I also taught so much of online and I was realizing what my students were learning in the classroom was not comparable to what they're learning in online now, and was discovering it and I realized it is a distraction. Then I put myself in the same situation.So if a software has a e-learning software should have a lock features thing, it'll immediately change the whole learning experience.

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Djagmo: Wow this is brilliant. I mean, seriously, I have you know, part of discussions about product and, you know, give out suggestions and all those things. But this is something I think that can come only from actual experience, I think, which is what you are saying 

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Gautam: just get the student locked into the classroom. Just the way, not locked. I mean, when we, you're in the class, we don't, the teacher as teachers, we, yeah. We tell the, please put their phones on silent. Please remove this. Please remove all distraction, focus on the notice board. And when I'm presenting in a classroom, it's not that I'm doing Facebook and presenting ,or YouTube and doing that, there are student learning there, got all these windows open. It doesn't, nobody can multitask, can learn.

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Djagmo: Super. I mean, because one of the most, you know, spoken about topics today is everything is fine. We have online quality of content is amazing, but what about student engagement? What about course completion rates? How can we address that? I think you might have just shared something very meaningful to address that particular thing.

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Gautam: It, I'm telling you, the moment this happens, and the biggest adopter of metaverse has to be online education. So what happens is you put your headset on, you're able to lock out everything else.And you give it to every kid in schools. That is when real edtech will happen. You have to lock out the noise and whether we like to believe it or not, even as adults and very mature adults, we find it difficult to switch our Gmail or WhatsApp. How can you expect the child to do it? 

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Djagmo: Got it. No, this makes so much sense. And this could, I'm totally gonna, you know, now I know why you said edit it out. So I'm gonna, I mean, after this podcast, the first thing I'm gonna do is call Deepak and tell about, hey, you know what, the podcast went great, but one thing I want just quickly pass on to you is this is what Gautham said.  I'm gonna tell about this locking out feature. Thank you Gautam. Thanks, Gautam. Now, you know, you answered in a very effortless manner. Two things. One [00:44:00] is for a data scientist trainer who's aspiring, you know, what can make him better. And then you also answered another important thing. What can a great teacher do to improve his business? But now,this is all coming from your experience that you've had, right. But I'm gonna step back a little bit and then allow the listeners and allow, I mean, give myself an opportunity to see how your journey was from 2008 to 2021 by asking you questions related to that particular time. Okay? So, going back to 2008 Gautam, you said that, you know, obviously from,you see when, when we compare or when we kind of have to look at you, you obviously have a very different background coming into the training thing. Right. Yours is too organic. Like, literally, you are really solving a problem and as a consequence of it, you are making a life, or you know, you're making a living for yourself. But that is not the case. Let us agree, right? I mean, a lot of people choose as a career path, teaching as a career path, and they come. Right. So it may not be fair to, you know, have people, look at your journey and then, you know, see if something can be done. But I'm sure there is some value to be taken away here. So that is where I'm coming from Now,you started off, you know 2008. Now tell me, despite having all the experience and you know, you know what is the industry meeting and all those things, but how did you, start off from a customer acquisition perspective? You know, at the end of the day, you still had to go out and bring in students, right? You had to acquire customers. 

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Gautam: the fact was very simple. There was, we were first in the market first or the second in the market. I just put the word out there that people want to learn analytics join. So students started coming and we did, we did a good job. See in, I will again come back to this main thing. All the trainers should visualize how are they adding value to the students? And what is it the moment you do that? India is a very vastly populated and an underserved country. There is everybody or India, and across the world the thirst for education is there. Provided you are doing something, you're really teaching well. And in the teaching you should be pro, you will get to. So I just felt the word in my ecosystem and those days we didn't have WhatsApp. I'm talking it did not become so big, right? So I got the first six or seven students from the, one of the IT companies. They liked the classes they got internally transferred into the funding analytics department and they started referring. And yes, I definitely had the cushion of my corporate career for eight years. So it's not that,I needed immediate income. So I was able to, in fact, my first set of classes way back in 2008 was I hardly charged any people to come to my house and work. So I would tell all teachers, first is ask yourself why are you getting into this? And this is not just a good data analytics. What is the prep? And if you're getting into this, how are you going to add value to your students. At ATI , our value was these four principles, T E P T, theory, example, practice, test. And we used to do those examples and as I mentioned those case studies. So we were very clear on our value of that we will give practical case studies and we had something called wrap, Redwood Apprentice program for data analytics. So after you complete the course you or during complete start working with us on our, we had a boutique consulting practice where they used to join us. So I would urge all trainers, student acquisition is not a problem. You've got tools like Facebook, Google, it is not a problem if you are focused on adding. If you believe that you can add value to the student, the student has to perceive that. So you have together set, basically marketing 1 0 1. You have to get the segmentation, targeting and positioning, right. Let us assume there's a student who is, who feels that they can buy an education. Obviously, you can't write value. Whatever you teach, that will require a different strategy. You'll have to show them infrastructure. You have to show them that you've got 10 certificates from the U.S.. So there is a segment somebody wants to genuinely learn, then you have to be at the cutting edge on your own. And for that, you cannot be in the cutting edge if you're not doing consulting.At least you should be doing one project on your own for corporates. So all our faculty, were all consultants. We did not have hundred percent. So, okay. I'm not a personally [00:49:00] an advocate in vocational space of anybody being a hundred percent training and not just analytic. I think across the board, think of medicine, all our colleges, whether you talk about All India Institute of Medical Science, you talk about Jipmer in pondicherry, you know, you talk about any institution, it's associated to the hospital. All the faculty, faculty at the end of the day is doing a surgery or whatever they're doing. So your management institutes the faculty, the best faculty is always in consulting with corporates. So I'm a strong believer that anybody in the training profession should be a consultant or at least one or two clients. And from there. Otherwise it is not a wise idea to get into this. 

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Djagmo: Very insightful, I think. Very important point as well. Now, we spoke about, you know, student acquisition, but for a trainer, let us say, you know, who's listening and who's only training full-time, and then, you know, who's not consulting, what would you know, what things would you recommend to the trainer to start off with consulting?

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Gautam: See, you are obviously training because you've got something, you've done something. So start off by taking a few projects, take up,associate yourself with companies like us. We have got many projects. If you've got the skills, so you should associate with the industry and you start doing projects and from there, you, you'll, you will be at the cutting edge. So if you're training, if you're getting your training, let's assume there are 2000 working hours a year, I'm assuming about hundred into roughly 160 hours a month in project. Working, I would advocate, no trainer to do more than 800 hours. Of training, which is also high. If you look at the government, scale of any academic in the industry, any of [00:51:00] the institutions they have to reach for, I think 300 or 400. I might be getting your, my feedback wrong, but the way the policy is also set up, because they, the government also tells the, all the people are teaching in government and our higher educational institution. They should be doing consulting, they should be doing writing papers. So code teaching cannot be wrong. Plus remember, something teaching is a, it is a physically intensive activity. You're standing in class for eight hours, as a faculty you're not sitting 8 hours .So I am, I'm not in favor of anybody doing more than 800 hours of training and anybody who's doing 2000 hours of training, eight hours a day, five days a week, I mean, unless you're a superhuman, you will, I mean, burn out, it's, it's not fair.

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Gautam: It's not fair on yourself. I will tell Trainer, please don't do that. Do 800 hours. In fact, don't do more than 400 of teaching. Do a good quality, remaining time either you should be writing papers or you should be good consulting. The other thing, which I would very strongly put, not just to trainers but the, our ecosystem, see, irrespective of what you see in the market as three cycle, which happen day off, this thing, if you are in technology and analytics and machine learning databases, there is no shortage of in technology also, there's no shortage of work. So you'll always get work. So don't get, get it think that, okay, if I do 2000 hours of training and if I reduce it to 800, my income will fall short. It won't. First three, six months, it'll fall short. But once you do some good projects and will also go up. So it'll be a win-win. So you just need to take an investment of six months on yourself and do consulting and training together.

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Djagmo: got it, got it. Gautam, I think the point here is not about the yes, yes hours and all those things, but what, where you're getting at is, you know, by using 800 or 1200 and all those things, do not make training the only livelihood for yourself. You have to combine it with some real work as well, like what you teach. You also practice is what you're trying to say. You practice and you teach and you're gonna grow as a trainer and you're also gonna grow economically  

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Gautam: only for vocational trainer. I'm not talking about vocational. Yes. 

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Djagmo: It's not, it's not for school teachers and all these things. Yes, yes, of course. Of course. Vocational. Somebody who's teaching someone to specifically carry out a certain task which will help them earn money. You're talking about those particular skills? got it. And, just like how, you know, sports people are right. If you have to go and teach somebody how to play football or cricket, they see that where you are cricket or a footballer first, you just don't come off like theoretically. So I think that's, pretty much what this also is. So, Gotham, that kind of brought me to this, whole, set of questions, right? So you, just like how you kind of, you know, very, very simplistically brought the analysis down to, okay, look, you know what, you might have 2000 hours,that you can train, but you shouldn't be doing that much. You should probably look, look at somewhere between 400 to 800 and remaining number of hours. Please do consulting. Now how many, what should be the teacher to student ratio for a data science, trainer from a, not from a business perspective, from a quality perspective, from your experience. Is that even a question? 

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Gautam: Very, very important question. It's a very, very good question. Okay, cool. See, it's again about the segment you are going after, the segment of your students. Now, if the segment is, say you did corporate training where one of your clients has told you, okay, these are a set of engineers. You teach them, say SQL or a machine learning model. They're already in the corporate environment. They're ecosystem. They know the data, they know the client. You are just teaching them a tool. So there you can even go for one is 1: 50, 1 trainer to 50 students. Okay? Now, if you're training somebody, which is non-corporate, and folks, Who have a certain fear about the industry, they need to do it because of an employment reason. They did not plan to do it. So they're been forced into this for whatever reasons. Or the, the world has prompted them from them into doing that. I've had many students, many times I've, I've got students who have told it, your, you are much better at say, sales and marketing. Go and do it. Why are you getting into technical? You'll have a great career in sales and marketing. Or you're a very creative person. Get into concentrating on a movie, making, do an internship in that. So, but having said this, in our ecosystem, you have a lot of people who are kind of nudged into this profession that this is. And for them, I would suggest that you should have understand that. Those bachelors one step, it is the journey. And many of my students have told them that there are two things you'll get by joining ATI. Either you get a career in analytics or you'll know that analytics is not your passion. Both these are valuable outcomes together. Very Valuable. I teach students who are, I mean, again, there's nothing against anybody who are going for 15, 20 lakhs courses just because of brand name of an institution going abroad, spending very hard earned money of their parents or their families Who do data science course masters in data science, but they've not done a single analysis about their own locality. My next question, my what I tell them to dude you're spending 20 lakhs, okay, you're using this as a means to get into a foreign land, which is very good. Nothing wrong. I mean, you want to earn more money in, which is very good company then when you're spending 20 to 30 lakhs, if you're looking at getting a job in an international country, do an MBA, you end up getting a better network of people who are doing an MBA. If you're doing data sciences, then you, you can study on your own. You should. Your, you should, your heart should be in it before, and you have to ask yourself before paying 20 Lakhs. Can I learn the same skills on YouTube or with my local trainer? And get into the career. Because India has a very vibrant data science ecosystem. It's not that you want to be a aerospace engineer and only ISRO and NASA there's for you. So I would recommend all students.So this is where the ratio also comes in. People who are not sure of the [00:58:00] industry, they should not join a course where they are 50 student. Say, if I am new into this, I'm being nudged into it. I should find myself a mentor or a guide who can navigate this journey and tell me whether I'm in or where, or out. If I'm sure of this industry, you can join even batch of 50 or a hundred. No problem. 

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Djagmo: Got it. So Gotham, great. You actually touched upon three things that I would like to, you know, one is, I was also thinking of asking these questions, but you spoke about them. first one was, you said as a data science teacher, right. You need to, you said, you know, if you saw somebody who had skills, natural skills, that could take them way higher. Like say it could be sales and marketing or anything like that, you would tell them, you would identify it and you would tell them. Now, there are two scenarios here. Now, as a data science teacher, what would you say to somebody who encounters a student who's, you know, not, maybe he doesn't see a spark for the student, a spark in the student for analytics, but then he sees there are a lot of other things. So you think in the first week, second week, just tell the student boss, I don't think you are cut out for analytics. You probably pursued the other things?

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Gautam: You know, and another thing which is taught to us at school that we hardworking. That was always taught. But this hardworking thing, believe me, spoils a lot of decision making. It's a great attitude to have. Be hardworking. But what happens is when we work hard in something which we don't even like, we'll end up getting 60%, six out of 10. We'll struggle. Yes. We are a hardworking, we are hardworking. I'll work hard. I said, dude, it's not about working hard. Do you want to spend the rest of your life find something which you can beat 10 on 10? And believe me, your analytics industry and any new age industry bring a lot of people. To be told to get in because yes, they've seen the job, but very few data science trainers actually look at aptitude and it's important to tell because, and again I'm not touching on the fees or all, but we have all, I mean, when we were doing it, we always made sure that our fees are not more than two months of a fresher salary. So our highest fees that we ever charged was 40,000 rupees. 

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Djagmo: Okay. I was gonna come to the fees part of them. Yeah. 

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Gautam: my view on education is that, so at least that was part of ATI, maybe because we have a very large consulting business, which can offset some of it ,could be, I don't know. Maybe, see everybody's circumstances are different. So our thing about, so I don't want to comment on anything, but I was, I tell it all my students don't pay 20 lakhs, 30 lakhs, or even 10 lakhs for just the brand of a degree. If you're not sure that you are going to enjoy it, choose a training institute at a reasonable cost, which is under 40,000 or under 50,000. Understand whether you like it, not like it. Get into a job. For a year. See how you're growing in the thing. If you've got a spark, there's always a dirt of talent, you'll be picked up. But if you're still struggling, then maybe this is not for you. 

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Djagmo: And when you said 40, 50,000 rupees, look for a course of 40, 50,000 rupees.this or less.This is what course is this? This is the data analytics 1 on 1

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Gautam: up to up to machine learning modeling. Now you can get it free, of course, in Coursera, but then you need a software which can plug out the noise when you're learning it.

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Djagmo: So essentially you're saying that there is no difference between learning from Coursera and learning from an actual human being, except that when you're learning from an actual human being, you're able to focus a little bit more than what you do in Coursera. That's about it. That's all is the difference. 

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Gautam: And you have to have, take your own actual human being will also motivate you more individually. That's, it's like right, you know, going to a gym and having a gym instructor who'll call it five o'clock in the morning that come, you know So what you are paying is actually for a motivation you're paying. And Helping you focus your energy around, motivation and supervision. 

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Djagmo: Got it. Gautam. There is, there is another,aspect or angle to this, which I'll come back shortly before that. I just wanna touch upon another interesting thing that you said. You know, whenever students joined ATI: Analytics Training Institute, you told students that, you know, you'll get two things. One is either you'll become a analytics consultant or you will know that you're not fit for this particular thing. The reason, the, the reason I just brought up that part, and you said both are good outcomes and the reason I'm touching up on this again, is I see this podcast as, you know, I mean very parallelly, somewhere remotely like this. You know, where there are knowledge entrepreneurs watching this to do two things. Either they'll know what to do to become a better knowledge entrepreneur or realize no knowledge entrepreneurship is not my cup of tea. Both are good outcomes. Because you know, you're sharing actually what somebody has to undergo being a trainer because, after talking to you, what I understand now, I'm coming back to another thing. You were talking about fees from a student perspective. Now let me talk about fees or ask you some questions about fees from a trainer perspective. You said, first of all, if you've got 2000 hours for training or you know, time to work, you only do 800, 400 to 800 hours. So now this definitely puts a certain cap on the amount of money that you can make from training. Now, there are people, there's nothing wrong to aspire earning money from training if you're doing a good job, but you yourself very clearly said, boss look, 40, 50,000 is a maximum that you pay if you find a very good course. So that kind of puts the trainers income within a certain boundaries, and then only consulting is gonna help somebody reach their financial aspirations. how do you address this for trainers?  you know, so what is the number that they can expect from training

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Gautam: Training for about 800 a year and you're charging for 2000 to 3000 hours,rupees per hour for training. Roughly you're making 16 alkhs

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Djagmo:This is you're talking about not one-on-one,10 people batch

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Gautam: I think making 16 to 20 lakhs of income per year. Is not a bad income for some training, and then you're doing consulting also. So what happens is that you are 16 to 20 lakhs if you're making as a trainer. It is pleasant, teaching is always a pleasant profession. You're meeting Nice, you know people, the environment is nice.It's not a high pressure job, so 16 to 20 lakhs a year is not a bad income as a trainer provided you are good at it and you can command those premiums. So, and if you've got 10 students in a batch and you are only charging  the student about 300-400 rupees, it'll be, I mean, mean maximum between 200 to 400 bucks an hour, even if you've got five students in a batch. Training is a nice way to run a decently comfortable life. And this is only, you know,you are, you're just looking at only 40% of the time that's available for you to work. You're saying 60% spent on consulting. So it's gonna be more than that is. You know, you can do more, but Yeah, so, the thing is that when you imparting education, a teacher has to be very relaxed, very comfortable.You are, it's,I take it as a matter of privilege that if I have got students I need to do justice in that class. I should not carry baggage of what is happening, what is outside, you know, at work not like that. So to be able to detach your heart and soul into the class, you cannot do it 5,000 a day.Because that is what I tell Trainers. So it's very comfortable to, teaching is beautiful profession. If you're comfortable about making an X amount of money. [01:07:00] If you want to make a hundred X, then obviously the game has to be differently played. I mean, you have example like Physics Wallah and when you know who, who built Unicorn Byjus.  So those guys are brilliant at what they're doing. So they've done the YouTube channel, they've done all, I mean, whatever they're what? Bujus or Physics Wallah, you know, those are different. So I'm not comparing, I'm talking about an average, an above average person who is passionate about his or her industry. Who wants to impart knowledge to either his student or a consulting can make a very comfortable income on teaching.

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Djagmo: Great. I think this is great insight for people who are looking at data science teaching as you know, future.

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Gautam: because you're doing this. You will see today you are making about, say, 16 lakhs to teaching a few consulting assignments. So you're making about 20, 30 lakhs a year, but your peer group will be making one crore. So you have to be very comfortable about the fact that yes, if I was working in a ABC company in a big 4 or whatever, my immediate peer with the same set of will be earning a core and in half. So if you're okay with about that then, but they all, that also comes with a lot of pressure. It comes with a lot of travel, it comes with a lot of onsite. So I think it's a live position which a trainer has to take. 

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Djagmo: analyze what, what is the cost that you pay for each one of these things and what you lose, what you gain. And then, yeah, you make a decision. And,  just a small detail. I just want to cover when you said, you know, a trainer, if he's charging good enough to charge 2000, 3000 rupees per hour, you meant it for his one hour, not, it need not have to be for one student. And then depends on how many students you have in that batch. You have 10 students and you end up charging and how many hours, should it take,typically to a trainer to complete this entire course from, you know, you say until machine learning, right? So how many should he be able to do it? 

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Gautam: when we were running, until the world has changed pre covid and post covid.So I would always encourage, and it really depends on what the segment of students that trainer is charging, targeting. If the segment is starting actually new to the industry, it should take about hundred hours. Hours. Hundred, hundred hours. Okay. That includes at least 300 hours spent by the student studying at home.

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Djagmo: got it. Out of the 300 hours, 300 hours, hundred hours is goes away in getting trained. 200 hours the student has to spend by himself. I would say hundred hours.

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Gautam: If there, and if you enjoy your subject, you'll not, you'll lose sign of the clock. So, whereas if you focusing on a set of people who already know a lot, then you can have short modules of 20 hours each. That's absolutely fine. And that can be priced even higher. See, deploying, you can just have a course and say, deploy machine learning models on banking data or deploy machine learning on marketing data. And those you can charge a huge amount. 

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Djagmo: Got it. Gautam. Great. Gautam, when you did ATI right,you said, you know, if the student did well, you had enough consulting projects yourself to have them deployed there. But, was there a situation, you know, where the students, became 

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Gautam: trainers in Yeah. Yeah, A lot of them. And then they got projects, trainers plus consulting, and then they, so the consulting firm they worked in and they got placed jobs in all the big 4 of IT companies and analytic companies. 

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Djagmo: And, when ATI was at its peak, what was the number of students and the number of teachers that you had at any point?

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Gautam: 500 to 700 students a year. Five to seven teachers. 

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Djagmo: Five to seven teachers. And this was, classroom led training. Okay. This was at the peak, so when the pandemic hit, right? So how did you deal with that situation? What happened? What happened with you? 

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Gautam:  the chronology of events was even on March, February end, the advisory which were coming out was that whatever the knowledge that, okay, you can't have air conditioning and all that. So what I started doing was we removed our air conditioners. We removed, we spent a lot of money revamping our infrastructure that we could teach, teach Suddenly March 23rd, lockdown was announced. So we had spent a lot of money removing our air conditioning, changing our classrooms into open classrooms because the advisory which has come out from the health authorities was, you know, you can't have closed environment. Then March 23rd, May 20th, I'll never forget that evening when the lockdown was announced and it, we just had to shift completely to online now in, so we ran online. So obviously we reduced our enrollments. I think it was at a peak just before the pandemic. So all our students have studied, we completed their bachelors online, and I think from March 20th, March 21st, we would've enrolled not more than 30 students. And in March 21st, we decided, you know, I think our last class happened a couple of months. But yeah, we are still open to if any student wants to reach out on some help, we give that. But, that is how we, because we realize that two things, number one, online without these enablers is challenging in the meantime, but more than the online part, our consulting business suddenly boomed because, just to give you a perspective, till March 20th, it used to take me at least 10 meetings to get a client. Signed up for a small boutique. So we had a consulting practice, a boutique. We used to take up a few projects, student to work. So we were like an R and D company. Suddently after pandemic, we started getting a huge number of orders, and because everybody wanted to do digital, everybody had to go online. So those, we, the, the demand was huge. So all our leadership time and including my time, I could not devote that time to ATI and that's when we decided by March, 2020 first. So we started reducing the number of enrollments. We just kept one or two batches running .Earlier we started two batches, one batches a month. We reduced the number of batches and we finally, I realized that it was not we wanted to do consulting in the, I mean, as a growth, and we built out a digital platform and on our focus is consulting for the small and medium enterprises and large financial in institutions.

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Djagmo: Got it, got it Gautam. So it was, pandemic definitely did play a role, but,from what I understand, it's really that, you know, if you really wanted to continue even after the pandemic, you could've definitely continued. 

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Gautam: but it's just that we don't have the bandwidth.

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Djagmo: You don't have the bandwidth. And as you say, you know, you, you certainly, there was an upsurge in the consulting opportunities and then, you know, you thought, that is something  

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Gautam: so taking out from a leadership team and me taking out 400, 500 hundred hours to do training. It's there is a, you know, revenue angle there because our consulting projects are obviously, you know, higher margins, significantly higher margins.

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Djagmo: Got it. Got it Gautam. And so as of now, you are full-time into consulting and you're not doing any training at all. And then till the pandemic hit, you are only doing classroom training? No Online training was going on

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Gautam: We did one or two. And then, one or two. We tried online to, in 2014-15 with, I mean you can edit this out. With TeachEdison also we tried to do some online.But then there was bandwidth issues. This was way back when you guys were just started and what happened? Yeah. Students were not accepting it. But now it's totally different now with TeachEdison. So I'm now in favor of I love education field, but now I'm in favor of platforms. So, I mean, you can edit this part of it. So I strongly, you know, having been a teacher. Seen both sides of it. Virtual education and, you know, is going to be the biggest industry in the world now, one of the biggest. Provided you're able to match, get the education into the person's head at a very low cost. We, that we can discuss this offline separately, but I'm passionate about education and one still I'm happy to be associated, but in building platforms. Not in one-on-one teaching or one to many teaching.I think those days are over. 

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Djagmo: Got it. Got it. you have a different approach to solving this whole virtual education. 

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Gautam: I don't have, I mean, I'm not into, but I can, you know, I know that there is a need to do it because you know, classroom training, it requires a lot of how do I put it? Passion to keep it running. Now, what the scale which a virtual school virtual education gives, is it finitely more? You guys know it better than I do. The question is, how do you identify the best faculty to earn 1.5, two crores, three crores, five crores through the platforms, maybe through your platform. You know that because the moment you're able to do that. See, like a YouTube or a Facebook is able to run the, you know, or a Twitter. You've got say 10,000 followers or 20,000 followers. Similarly, if there's some trainers where one can say that, okay, these are are rockstar trainers and the platform is like a Youtube platform, which customize has a setting. And some kind of an offline kind of mode, Some, but, you have 10,000 plus students and you're giving the trainer 200 Rupees per two student. So then, then the game can be very different. It's like Swiggy and Zomata, no matter, I mean, though they're making losses now, now it's, no, there is no more room for somebody else to come into the food delivery business. Not easy. Not easy to 

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Djagmo: Not easy. Yes. Yes, yes. So Gautam, coming back to now, Redwood Algorithms, right? now it's called Redwood Algorithms. So, do you wanna talk about redwood algorithms because, you know, what exactly do you do? Who are the people that can reach out to you and anything that you wanna share about Redwood Algorithms

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Gautam: so, we are, as, as I mentioned, we are basically an analytics company, which is focusing on digital. So what we do is we help people. Acquire, engage and retain customers. So anybody who is looking to set up an online business or already running a business, which they want to go online and make profits,we are not, most of our clients, none of our clients are, very few of our clients are funded companies and who want to make profits. They should reach out to us and we'll help them make money online. So that's when ask about a trainers, how should we acquire that? Let them reach out to me. So anybody who's serious about making money online and acquiring, engaging and retaining customers online, they should reach out to us. We help, we do the digital footprint, we do the automation, we do the marketing funnels, and we do the pricing and the promotion strategies also. And we've got multiple ways in which we engage. So we are focusing on this business. That's it. So

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Djagmo: Great. And, hat's your team size now? Do you have a fixed number of employees working or you have 

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Gautam: you got six people, 50 people, and we're adding about five to six people a month and roughly, so we should be a 100,150 people in the company by end of this year

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Djagmo: and the five, six people that you add every month are data scientists 

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Gautam: so creative, digital automation, data science one, one of each. So because we do a lot marketing, we do a lot of digital marketing, so we add a lot of creative people also, and we use a lot of AI for creatives also.

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Djagmo: Okay. Gotham, I'm so sorry. I'm gonna come back to those four things that you said, you know, just for creatives, you said four things

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Gautam: Creatives, automation, automation, data [01:21:00] scientists, and  account managers.

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Djagmo: account managers. Okay. When you see, when you say creatives, what are the kind of skills that you're looking, from these people?

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Gautam: writing, visualization.

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Djagmo: got it. So creatives. It is linked to writing creatives

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Gautam: And then we, because we do a lot of digital advertising. So we need  Video editing. Video writing. Visualizing, copywriters. 

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Djagmo: Got it. And then, when you, when you say automation, what do you mean? what are the kind of skills that you're looking for??

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Gautam: We look at a lot of the customer engagement, see the whole thing about virtual. Versus physical, which, okay, let me make it simpler. What is AI? Artificial intelligence, I call it. Automating human intelligence. So we use a lot of platforms like Salesforce and Zoho and any, there are lots of automation platforms. Every client has different automation platforms. So we try to make, use our skills and our creative skills to create customer engagement funnel, which are as human as possible using these automation funnels. So that is where, that is where we look at people who know Zoho, you know Zoho, right? Even your teacher isn't for education clients. We can talk about that, you know, but I'm saying we are always looking at automation software, which we can implement. We do a lot of work

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Djagmo: Got it. So this is links to marketing. The automation that you're talking about is linked to marketing 

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Gautam: focused on customer acquisition, engagement, retention around marketing

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Djagmo: Got it. Data scientists is pretty self-explanatory for those who are listening. If they're a data scientist, if they have any expertise in data [01:23:00] analytics, they can reach out to you. And

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Gautam: finally you spoke, I will not, I will only recruit people who demonstrate curiosity of running data and not just running for it

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Djagmo: Got it. I think that you made very clear in the start of this podcast, so I think they know what they need to have to reach out to you. And the fourth one you said was account managers. So is this, got do with sales or this do with 

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Gautam: relationship managers who they should understand process of our solution, our tech team will implement, our creatives will implement it, but there are people with a strong communication skills plus typically management and good understanding of marketing and digital, typically MBAs.

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Djagmo: Oh, typically MBAs. Okay. So MBAs is a is a filter, is a must? 

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Gautam: See, it's I don't, personally speaking, I don't use that as [01:24:00] a essential requirement. If somebody writes to me a very nice mail, it shows that they understand branding and marketing. I don't look at the qualification, but in the absence of that MBAs is a filter So I don't want people to think that, we'll only, but if somebody, because see knowledge and creativity is beyond degrees, that is a very important point. So  if somebody can demonstrate. Same thing for automation, same thing for creative. I'm not looking for somebody with a communication degree, if somebody can write a nice mail. We need talent. Right. Anybody who could communicate really saying that why we should hire them, and that should not be because I'm hardworking and honest. That is a given. 

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Djagmo: that cannot be a major, criteria.

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Gautam: I am a fresher and I want to learn. These are not acceptable because we learn we learn till we are 1995. And if somebody is old enough to vote and get married, you're no longer fresher. If you are old enough to start a family, if you're old enough to be a parent, why you calling as Fresher? So anybody who's hearing this, please don't say, I'm a fresher, I want to learn, I'm hardworking, I'm honest, I'm committed. Say how we can both can make money, that I can help you with your creatives. I can, your automation in your website is not good enough. I can improve it in 10 ways. Anybody who writes to me, my email is very easily available. Anybody who writes to me with a nice mail, I always talk to them. Two people write proper mail. Otherwise people just, spam. So one thing, you know, I would tell everybody, and I tell myself also, technology has created ability to reach out to a lot of people, but right as resulted in people closing the doors to a lot of engagement and relationships. So that is, changed. So if somebody can write a very good quality email, which they may be writing to 20 other employees, that's not a thing. But I need to get impressed. Hey, I need to meet this. 

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Djagmo: Okay, Gautam, I'm just gonna take some more, a little bit time and then, you know, touch up on this. This is not the agenda of this podcast at all, but I think this should not be ignored by anybody who, no matter what podcast they're doing, especially when a guest is like, literally. You know, reaching out, saying that, you know, just write to me, to all the job seekers out there, if somebody who's watching this who know job seekers, now here we are talking to an entrepreneur, you know, who's at a 50 employees right now, and by the end of the year he is looking to grow to 150. So there are like a hundred positions vacant and then 25 each from creatives, automation, data science, and then account managing right, literally, people are listening, are literally looking at so much vacancy at the point where we are hearing layoffs. Now, here you look this person is not asking for degree or whatever it is. He's simply asking to write an email. If you know his business, you, all you need to do is just write to him saying that what have you done so far in your life? And then how you can use those skills to help is contribute to his company or his business. That's pretty much what you need to do. When he says, write a nice email, it's not about the beautiful words or the jargons vocabulary and all those things. It's very simple what your skills are and how those skills can add value. That's about it, I think. Is that that's what you mean?

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Gautam: I want and we want to grow the market is huge. Don't get distracted by these layoffs because those layoffs are based on business models, which were in, what an economics term, in a money printing era. In a low interest economy leader. People who had access to money were able to hire and then layoff that. Those are not, those are different business models. I would ask for any of the audience say, have you heard Ashok Leyland? Have you heard Tata

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Djagmo: I don't think I've heard of them. Interesting. I have not heard. If you are only hearing the companies that I've heard of late, Gautam is Microsoft and all these tech companies, the huge tech, Google, Facebook, and all of these people

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Gautam:  Maybe a real scenario, but there is also an opportunity. I ,you know, I can't obviously name any of a client, but I've got a client, which is in a one of the absolute corners of India, and I never imagined I would be working in that part of India. There was need for our services there. Okay. Wonderful. I feel, I feel actually blessed that thanks to the team, I'm able to, [01:29:00] and this way I see the opportunities are huge. All that one needs to do is what needs to just say what is it's and just not about me. I'm sure, if you reach out to any entrepreneur who's all entrepreneurs and all people in business, not just entrepreneurs, anybody in our company Everybody wants to grow, right? Nobody's in a company not wanting to grow you, whichever. Wherever you reach out properly and telling how you will add value to that person, believe me, you are increasing your probability and odds of getting a response.We had reached out once in our early days to the CEO of a global, multinational, global top five company in the world. Now whether it was him okay. Or his staff stuff, but the reply came from his ID. We were introduced to the Indian CEO , and we continue. Where we, we bought that software. There was a problem we were facing with I don't know, I don't [01:30:00] want to name it, but one of the largest companies in the world, and the CEO must replied. And there was a follow through message. I still kept that email. 

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Djagmo: Right. Okay, great. You were like using one of their products and you had a problem. And then they reached out to you with the solution

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Gautam: and how did I write to him? I wrote that we are one of the first customers for your product. Recently launched. These are the five challenges I thought about it. What will appeal to this CEO and solve my problem? And we got a huge discount also. 

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Djagmo: Great, great Gautam I mean, I, you know, even though, we are in the frat of the podcast, I feel, you know, there is like the whole important stuff to talk about. And then I totally want to like, you know, pick your brain on this because especially now, everywhere you see, you open your phone, you're talking, you know, you're hearing about layoffs. Nothing else is happening today, especially for the people in the software thing. Right. You also said that one important thing, these layoffs are executed by these companies based on a certain model or whatever it is. Now, how real or how true are these things? And should people really be worried? And people who are scared of these layoffs, you know, what would you, how should they think? 

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Gautam: See the answer to this? We can spend at least two or three days discussing this. I will tell you But I will tell everybody who is watching this to do three or four things. Number one. Look at which companies are laying off and look at which companies are not laying off. Okay. Check the financials of each of these companies. You can, any publicly listed companies, you can check the financials on the stock market downloadable. See you, it'll be absolutely obvious. I don't know why Google and Facebook is laying off and Amazon is laying off that. Yeah. Maybe. I don't know why I can't comment on that, but most of the companies are laying off, are laying off, which are thousands of crores of losses. Right. I don't know why Google, Facebook, and Amazon are doing, but that one can investigate and there I have some thoughts on that, but I don't want to obviously comment on that. But anybody who's doing, you should do your research on whether the company you're joining is making money or losing money. There is too much of print, which is going into loss making companies. There are thousands and thousands and thousands of other companies which need your services, which are not as glamorous as the companies are laying off. And all you need to do is write to the department saying that, okay, I can come and help you. So actually there is no problem. I don't think it is a real problem, but of course there is an impact. Because just not today, we read Swiggy laid off,Zomato laid off. Obviously. Now what happens is whoever's getting laid off it, their consumption In terms of they'll buy some less items for at least two or three months till they get another job. So it'll impact the economy little bit. But next question is the people who are getting laid off, ideally, my sense is they will get jobs in 15, 20 days or a month, provided they're flexible on the salary. Now why do, should they be flexible on the salary? Because they should it is so important. Unfortunately, it is not taught in schools. We all need to be taught finance from the time we are class eight. Now, what happens is if I'm earning hundred rupees from a company, which is making thousand rupees loss when I go to the market should I be benchmarking my salary at hundred rupees or should I be benchmarking my salary at 50 rupees? See, these companies basically have, many companies have raised the tons of money, price salaries that got inflated.Not salaries more than because they're funding losses. But that's surreal. It is like I've gone to some kind of a party. I've had too many chocolates. Should I have?That same number of chocolates every day? You negotiate with your employer that, okay, I'm coming in at 50 rupees, but I want to reach a hundred. Please tell me what I need to do to reach a hundred rupees, get back to my old salary in the next six months. So I would urge everybody, don't get unemployed. There's enough jobs available. If you're flexible on salary. Obviously many companies will not be able to match somebody who's raised money from Silicon Valley.US is printing money. U.S. used to print money. So they have all the, and dollar is a reserve currency, so it's a it's not a very fair model for both employer and employee. Great. So if you go to any company and say, look, I'm coming in, I was earning a hundred, I'm coming in 50, I want to earn 100. I'm not telling anybody to reduce their aspiration. In fact, if you were earning a hundred, you should earn 200 tomorrow. But. Generate it from profits. If your wealth and your income is getting generated from a loss making stream, it is not sustainable. The party may last for one year, two years, three years, five years, but at some point it's not forever. So this is what everybody should understand. Take out your money from profits. Don't take out your money from losses because it's gonna stop.  

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Djagmo: Even for the employees who have no control over this, they need  to think about it atleast

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Gautam: Yeah. Come on. I mean, if you, especially for the youngster, I can say you will find if you're planning to marry a person also, you'll do some homework. Right. So same thing you have to do for employer, same thing you have to do. That's what I'm saying. Most people unfortunate and I'm not blaming, you know, it's important for people especially who are in the tech field because we are all from the tech field. I tell all the right that guys, you guys can learn the best of technology, but you know your maths, you know your numbers. Why don't we just download the financial report? It's the same number. It is written in English. Understand the number. If it is making a loss, it is not complicated. Now, if you're getting into a loss making industry, that is also good. Nothing wrong in it. But be aware that for three years, like that saying in English is gonna make hay while the sunshine. Be aware of the party will not last. Let me make my money and let me save up money. So if you're joining a loss making company and you're joining at the salary of, say you were earning 50 rupees and you're joining at hundred rupees, please remember this 50 rupees will not last forever. So that plus what you're getting,  please save. Or use it to start

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Djagmo: The hundred rupees won't last forever. 50 rupees is a reality. So 50 rupees, you might as well save. So this is the bottom line. Look, if you happen to get an amazing pay at a company that's lost making, just be aware that this is not gonna last long. Don't plan too much

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Gautam: Don't take home loan loan 50 rupe income. Correct the remaining rupees. Use it. Shorten your timeframe. Suppose you're taking a home loan on a 50 rupees income. Say you're getting a home loan where you have to repay in 20 years, the extra money you're doing. Cut short that time and repay in 10 years because you, those kind of things. Use that money as a bonus. Treat the increment you're getting as a bonus. Don't treat it as a salary. 

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Djagmo: Got it Gautam. One last, which, topic, which I think is really gonna add value and very important. You said so many times, you know, write to me. Write. Write . Write . Write a good email, not spamming. What is the difference between spamming and writing a good email? Is it to do with the language or is it to do with the thought process? If it is both, can you please shed some light?

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Gautam: I think it's nothing to do with the language, as I mentioned, to nothing to do the language in terms of, okay, I really don't care whether your English is correct or not correct. But what I do care is, okay, how when you're writing if the content should be right, why spamming? Suppose you're writing to me. Okay. There's not for job seekers. See, I get a lot of mail for computer hardware sellers. You know, on my LinkedIn. Then we got 20 computers lease buy. To my finance manager. I'm not going, this is a lesson for salespeople. Somebody applying for a job. In the job ,one you can write to the HR or you can write to somebody in the tech team. If you're applying for tech job. Or if you can definitely write to me saying that, look, I've seen your tech I, this is 1, 2, 3, 4. I can add this value and I'm really good in this. So in interviews, again, maybe this, you know,we are told to be modest. Many times I get into an interview and ask to set them for a particular skill set, please rate yourself. So they'll rate themselves a five out of, yeah, five out of 10 or six out of 10. I say that, will you go to a doctor who rate themselves five out of 10 and say, God forbid you got a fracture. I've gotta the chance of you run from that clinic, even though you're in pain. Some of the doctors said, I'm trying to learn on your fracture. Doctor will be learning. Obviously you're a new case, but give the confidence, you should say, I'm nine out of 10 in this, and don't get into, you know, have to give some confidence. So if you can say that I'm nine out of 10 in communication. Fine. So it [01:40:00] is all about content, not grammar. Not grammar.

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Djagmo: Not grammar, not language. It's, it's about the content. Got it. And you said four things. I didn't hear sales from you. So you don't need sales people?

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Gautam: Of course I need, of course I need.Yes. Yes

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Djagmo: And they fall under what category of those four things? Creative ,automation, data science, account managers

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Gautam: Yeah. Why I didn't say sales is maybe because so many, such few people apply. Very few people apply for sale, but sales is the most important. I would tell everybody it is the most important part of the organization. If you write to me saying, I can sell, you will get a call back from me in five hours. We need all the sales people.You're getting what I saying? If you write to me that I can sell. And I can get you business. Got, you'll get a call back from me in five hours. Super. 

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Djagmo: Great. I was just wondering, maybe we're talking about this, you know. Okay. I thought, probably Gotham is doing the sales himself from all his network. Okay. Okay. Gautam, you know, I had a lot of questions, but to be honest, you know, just the fundamental questions that I asked brought out so much value from you. So there's no need for me to break down and ask you all those questions like that. So,  this has been like one of the most, amazing, amazing sessions that I've had so much of information in such concise way. And,  I'm super excited. As I said, I'm gonna go back to Deepak and talk about that blocking thing that you said, you know, locking thing. I'm gonna say that and, it was a pleasure. All I wanna say is thank you so very much for taking your time out, for this podcast. And if there is anything, after this you think, you know, you'd want to add to this is there's anything that you want to, you know, use this platform for, please feel free to like, reach out to me and I wouldn't hesitate, you know, like I will like have you another session. If you, if there's something that you wanna show, share anything, I'd be happy to have you again. So. 

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Gautam: Sure. Thank you so much. Thanks a lot. Thank you. This is really, thank you for having me over. And, you know, on this, on this podcast, I really enjoyed it and I really appreciate your, the way you've structured it. I think you asked the right question and you got me to think and, you know, you also took me back on our journey. So it's a wonderful way of spending a Saturday afternoon. I really, you know, it made me reflect a lot on my journey also. So you've proved all very well, so appreciate it and thank you to TeachEdison team.

‍Djagmo: Thank you so much. Thank you so much for this positive feedback. This podcast is brought to you by EdisonOS a no-code EdTech platform to operate an online education business. Knowledge Entrepreneurs can use EdisonOS to sell online courses from their own websites, manage online masterclass, launch mobile learning apps, sell online practice tests for competitive exams, run online learning communities, digitizing their offline tutoring business, use it as a learning management system, and a lot more cases in the domain of knowledge commerce.