WEBVTT

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I grew up largely in the Gulf. So when I'd come

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in for my 11th and 12th, and we had a long interview

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for selection in my 11th grade. At the end of

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it, I remember my mentor, then Suvidhaka, who

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came up and said, I've interviewed kids over

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my 15 years of experience in school. And you're

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the first girl who came up to me after a four

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-hour interview and said, I want to do social

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work. I have never heard that answer. So that's

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where it started. It started with saying social

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work I have to do. I was a proper social activist,

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walked across this country. you know lived off

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highways lived off sleeping bags and you know

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in jantar mantras screamed on top climbed poles

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done all of that at one point of time welcome

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to another session of the my first job each week

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we look at a different career profile we look

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at a different field of work altogether i have

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a young vibrant entrepreneur. Rajashree Sai of

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Rarfana heads ImpactTree .ai, a company which

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is into data analytics, and she's examining ways

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in which data can be used to assess impact on

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the ground. There is one thing about planning

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and policies and money actually being spent on

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programs. But what happens to the lives of the

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people who are a part of this is really what

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Rajashree is looking at. Good afternoon. So great

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to have you on the show, Rajashree. I know that

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there's a lot you've been doing. So like you

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just said, I'm the founder of impactree .ai.

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And we started impactree with a mission of impacting

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about 1 million people way back in 2017 when

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we started it. So far, we've been part of initiatives

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that have impacted about over 2 million people

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across this country, and we operate in India

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and Saudi Arabia. And what we do is very simple.

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We build technology platforms that is focused

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at enhancing decision -making on the ground in

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two key sectors. So we largely operate in the

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sustainability space, that is that you would

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have heard buzzwords around ESG, you would have

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heard around social impact. All of this falls

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under sustainability today. But what we've realized

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through our experience is that, you know, this

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is all great at a mission level, right? At the

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founders levels, organizations say net carbon

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zero, someone is saying impacting 70 million

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people. What does it mean for the ground level

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operators? How do they translate that word net

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carbon zero or impacting someone in sanitation

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or water? How do they perceive it? Because these

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are the people who are implementing it on the

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ground day in and day out. If it's an NGO, it's

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a block level officer. If it's a corporate, which

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is a listed entity, which has set this mission,

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it is their factory worker, their operational

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heads. They are the ones who are actually going

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about implementing this mission. So our platforms

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that we build, SaaS -based platforms, we sell

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it and retain it for a license basis. It's a

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technology product that we sell to these two

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customers, whether it's a corporate or an NGO

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largely. And they fit it into their stream to

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collect data. So through their organization,

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they're able to implement it, collect matrices

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that make sense to them. And that way scale better

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programs. So that's what we are focused on on

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most part of our day is what we are working on

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is that. Of course, the other initiatives that

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we do run is Neti, which is our platform for

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women entrepreneurs in rural India, largely.

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So that's the second big initiative that we work

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on. What has led to this decision? In some time

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in college, did you have a problem or did you

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understand what is it that you wanted to be doing?

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If you ask me. When I was in college, did I know

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what I was doing or I would set up Impactry?

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The answer is absolutely not. In college, I was

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an economic student. One thing that I was very,

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very clear of right from the beginning is that

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I wanted to pursue a career in social impact

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or social development. And that was something

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which was like a no -brainer. It was something

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I wanted to do. So very much when I was I was

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doing my school with the school KFI, which is

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it was it was here in Chennai. I grew up largely

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in the Gulf. So when I come in for my 11th and

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12th and we had a long interview for selection

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in my 11th grade. At the end of it, I remember

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my mentor then came up and said, I've interviewed

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kids over my 15 years of experience in school.

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And you're the first girl who came up to me after

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a four hour interview and said, I want to do

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social work. I have never heard that answer.

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So that's where it started. It started with saying

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social work I have to do. I was a proper social

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activist, walked across this country, you know,

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lived off highways, lived off sleeping bags and,

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you know, in jantar mantas, screamed on top,

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climbed poles, done all of that at one point

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of time. And then along the way, I think what

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was my pivotal turning point is when I was doing

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economics, I said, hey, I want to do Indian economic

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services. So that's one thing that I always thought.

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He said, whatever you do, you excel. But you

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make sure it's very different, right? So even

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as I said, no IAS, I'll do IES. Why? Because

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no one knew IES. It was Indian Economic Services.

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No one did. So I said, I'll do Indian Economic

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Services. I did choose a career in arts for that

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also, that primary reason. But then halfway through

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trying to prep for that, I realized that I may

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not be very suited for a career in the civil

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services. And at that point of time, I did. Quit.

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And then the next thought that came into me,

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because I didn't want to pursue economics, which

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I was pursuing then, I told my dad, I'm going

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to quit everything. I'm going to become a lawyer.

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He said, hold it. No, you don't quit halfway

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through your graduation. You finish your graduation

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that I've paid for. So, you know, he said, law

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equals company secretaryship for CS. Pause. You

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have done year one of graduation. Finish that

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and do a CS degree. Again, I didn't have a clue

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why I did it. Took up CS. Did it. I wrote all

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the exams in first attempt. And like, you know,

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again, I like setting up goals. So that time

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the goal was clear CS. I will distribute sweets

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to everyone in the town and all of that. Cleared,

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got into a listed company, sitting in there.

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And then the question hit me. And now what? Let

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your done it. So that's where I think the most

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interesting turn happened for me. That's where

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I tell in many places I'm a recession entrepreneur.

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Because what happened is the year I got into

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my listed company, for which I struggled three

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years. you know, multiple exams was the year

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that recession hit. So in 2010, I remember around

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April, around March 10th is when I joined a very

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large listed company here in Chennai. At that

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point of time when recession hit, you don't,

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you realize that there's no legal work to do,

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right? So that was a very large, significantly

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large project for merger and acquisition that

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the company was, you know, actually had entrusted

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me with. there wasn't any work because if no

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business happens there's no documentation and

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if there's no documentation what does a team

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of 15 lawyers do other than sit and play minesweeper

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so those days we didn't have geo so we were sitting

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here playing minesweeper reading newspaper going

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for lunches i mean there's only that much you

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can do right after 10 months of recession i just

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told my dad you know i can do something i'm quitting

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i can't take this anymore so that's when i put

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a bet with myself I said that, hey, if I get

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through the Jagrati Yatra, that was again a way

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I'd applied for the Jagrati Yatra. It's the train

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journey where 500 entrepreneurs go on a train

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journey across India. This year, in fact, the

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applications for the G20 Yatra, I think, are

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still open. And it's pretty interesting because

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you get 500 perspectives of entrepreneurs, those

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who are career, you know, very senior AVP level

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people across the world to come on the Yatra.

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So I said, if I get through the Yatra, quit.

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So fortunately, unfortunately, I got through.

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So I quit. after that and that's when I think

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my real journey began when you know as a CS I

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started working with a bunch of people I came

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upon NGOs was doing compliance for NGOs and someone

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said hey I think you'll do way better as a partnership

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manager as a donor manager or implementer why

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you know compliance is paperwork you can't do

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anything so but then our advisory board member

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Prema Gopalan of course she's no more there right

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now but she had told me I feel you have way more

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talent and reading legal documents come on to

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implementation. So I did my first project. We

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did a very large project to implement in creating

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women entrepreneurs in rural India, selling clean

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energy products all across Maharashtra. We scaled

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that network across Indonesia and Africa. And

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that's where the story of the sector must be.

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So from there, it has been a journey where, you

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know, I dived into data with my co -founder Vivek

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and well, Impact Rewards won. That's very okay.

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So this Jagruti Yatra, by itself, it's a really

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different format, right? It's 500 entrepreneurs.

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And how long does the Yatra last? So it starts

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on the 24th of December every year. And then

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it goes all across. It starts from Bombay. And

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the Yatra is a period of 15 given 20 days. So

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it will be 15 to 20 days. So as young entrepreneurs,

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you will live off a trade. So it goes from Bombay

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to, you know, Kanyakumari to... then you're going

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to Bihar, UP, then coming to Rajasthan, and then

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you finally come back to Maharashtra. So it's

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a literal circle across the country in a sense.

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And it's very interesting as a format because

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I think what I chief took away out of it was

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that there are, and I'm sorry to use the word,

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but there are many more idiots like me who also

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want to jump and try figuring it out. So, you

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know, till then as a lawyer and a company secretary,

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you're pretty shielded, right? You're almost

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like corporate job, nine to five desk. You know,

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after that, most of the friends who are in traditional

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careers, it's relatively paved, right? You have

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your house, you have your two children, you have

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your vacations, you set it all up, right? It's

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a nice pack of building blocks. And then I think

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in the Yatra, what I took away was there are

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a lot more people who are trying to see what's

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the meaning of my life. Let me try doing something.

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And even if I fail, it's okay. I think there

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is a lot of wisdom in collective failure. You

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find more ways to fail faster. And then after

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that... Get yourself up and build yourself up.

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So I think that was my biggest takeaway when

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I went through the yatra, that there were more

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people like me aspiring to do something different.

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And I think more importantly, choose, find your

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passion. Like I remember one of my earliest mentors,

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Krishna sir from here in Chennai, he told me,

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we're all born with a purpose. What's your purpose,

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Lakshmi? And he told me this when I think it

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was about 20s, just out of college. For a very

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long time, I kept thinking, what's my passion?

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What's my purpose? What's my purpose? It has

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almost taken me close to 12 to 15 years to finding

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the purpose. But I think that, you know, the

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part is the journey that actually gets you there.

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And the other great thing when I was thinking

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about the Yatra is, I think all of us have had

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train journeys. Now, of course, the current generation

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doesn't really need to go on those 48, 72 hour

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trips anymore. But that is a time when you made

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another set of friends because you were thrown

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into very close quarters with people. And you

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got to know them better in three or four days

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than you would know some of your friends who

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met only in the evenings or on weekends and things

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like that. So I think whoever has come up with

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this idea, it's a fantastic idea to bring together

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extremely committed and driven people. And then

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Sashank Manitrapati is the founder of it. So

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you can definitely check him out. There's a whole

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TEDx talk that he's also given on the Chakrati

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Yadda and how he created it. I mean, he's a contesting

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MP from Uttar Pradesh and Purvanchal right now.

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But I'm in this phenomenon because I agree with

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you. In those days, and I remember till 2008

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-9, we still used to do the train, right? That

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was if you get into a train, most people just

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cover, you know, they go up to the bunker, cover

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themselves, put their iPods and, you know, everyone

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is streaming. And I remember, you know, I was

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in Chennai, Bombay sometime back. In 2008 -9,

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and you know, there were four uncles, very sweet.

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We chatted about politics, India, geopolitical

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situation, wars at that particular moment, economy.

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Then the uncles actually called his relative

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who was getting a breakfast in Pune and said,

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you know what, they have two youngsters here.

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They don't have food. Get them food. So, you

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know, those relatives bought the food the next

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day. It's a whole community you build, actually.

00:12:24.909 --> 00:12:28.450
Yeah, yeah, that's right. And those, I think,

00:12:28.450 --> 00:12:31.990
are the real learning points because it's learning

00:12:31.990 --> 00:12:35.370
by actually getting immersed. Now they call it

00:12:35.370 --> 00:12:38.669
immersive, but an evening of immersion is nothing.

00:12:38.929 --> 00:12:41.190
This, like you said, you have to be on the train

00:12:41.190 --> 00:12:43.570
and you have to get. The reason I'm going into

00:12:43.570 --> 00:12:47.909
this also is because I think apart from learning

00:12:47.909 --> 00:12:50.029
in a regular environment, which is like school

00:12:50.029 --> 00:12:52.309
and college, something like this, which takes

00:12:52.309 --> 00:12:56.230
two weeks is. defines, like you said, you were

00:12:56.230 --> 00:12:58.549
thrown into close quarters with people from so

00:12:58.549 --> 00:13:01.610
many backgrounds and so many different points

00:13:01.610 --> 00:13:06.470
of view that it's like that friction leads to

00:13:06.470 --> 00:13:11.950
another set of sparks, which you may not have

00:13:11.950 --> 00:13:14.730
actually, you know, at that point in time said,

00:13:14.850 --> 00:13:17.629
okay, that's the crazy part, isn't it? That you

00:13:17.629 --> 00:13:20.049
go for a program and then when you ask, what

00:13:20.049 --> 00:13:21.870
did you learn from it? It's very difficult to

00:13:21.870 --> 00:13:26.850
articulate. no but i wouldn't agree with that

00:13:26.850 --> 00:13:29.309
because i think it's the yatra the way it's built

00:13:29.309 --> 00:13:31.490
and at least it was you know at that point of

00:13:31.490 --> 00:13:34.149
time when i went i remember you know someone

00:13:34.149 --> 00:13:36.870
to i mean someone once told me that you set a

00:13:36.870 --> 00:13:39.470
lot of goals in your life yes i do because i

00:13:39.470 --> 00:13:41.509
remember when i went on the yatra i was just

00:13:41.509 --> 00:13:44.009
you know person who quit her job you know literally

00:13:44.009 --> 00:13:46.929
threw away a very promising career in legal and

00:13:46.929 --> 00:13:49.210
i possibly got there even my mentors then said

00:13:49.210 --> 00:13:52.820
why because you know First attempt, gold medalist,

00:13:52.879 --> 00:13:54.700
you're sitting here, you've got the position

00:13:54.700 --> 00:13:57.799
you want. At a ripe age of 22, they gave me an

00:13:57.799 --> 00:14:00.279
M &A project, right, which was just an acquisition.

00:14:00.320 --> 00:14:03.120
It's pretty large for a kid to handle at that

00:14:03.120 --> 00:14:05.759
point of time. So I really think that the company

00:14:05.759 --> 00:14:07.779
also saw potential. So even they didn't want

00:14:07.779 --> 00:14:10.100
to let me go and they said bye. But I remember

00:14:10.100 --> 00:14:11.860
when I was confused and I was sitting at the

00:14:11.860 --> 00:14:13.980
Yatra and, you know, it's something that Shashank

00:14:13.980 --> 00:14:16.299
kept telling me. He said, if I wake you up at

00:14:16.299 --> 00:14:17.879
two in the morning, you should be able to give

00:14:17.879 --> 00:14:20.230
me your five minute elevator pitch. What you're

00:14:20.230 --> 00:14:21.529
doing, why you're here, where do you want to

00:14:21.529 --> 00:14:25.409
head, right? And I did practice and he did ask

00:14:25.409 --> 00:14:27.970
us one day. So our one pitch that we do through

00:14:27.970 --> 00:14:30.009
train car sessions was something at ridiculous

00:14:30.009 --> 00:14:32.370
2 .33 in the morning, okay? Because there was

00:14:32.370 --> 00:14:34.269
no time in the rest of the day to do those pitches.

00:14:34.649 --> 00:14:38.409
But I think that, you know, those things also,

00:14:38.409 --> 00:14:40.889
the way it's structured, it helps you kind of

00:14:40.889 --> 00:14:43.149
also start, you may not know where you're going,

00:14:43.269 --> 00:14:46.429
but start to also putting to... what do you want

00:14:46.429 --> 00:14:49.029
to pull out of that experience, right? Like different

00:14:49.029 --> 00:14:51.129
people might pull different things out of it.

00:14:51.250 --> 00:14:53.490
But the more important thing is to also be able

00:14:53.490 --> 00:14:55.250
to direct to say that, you know, why don't you

00:14:55.250 --> 00:14:57.210
start thinking down this path? Because otherwise

00:14:57.210 --> 00:15:00.269
you can also end up going so broad, you may not

00:15:00.269 --> 00:15:02.009
really know where you want to start focusing

00:15:02.009 --> 00:15:05.330
on. So when you talked about the project which

00:15:05.330 --> 00:15:07.970
you helped set up with Swam Section with Prema

00:15:07.970 --> 00:15:12.230
Gopalan, what exactly did you have to do to set

00:15:12.230 --> 00:15:16.769
up this whole thing? So in that project, I was

00:15:16.769 --> 00:15:19.769
largely, so I was handling Swaim Section Prayog's

00:15:19.769 --> 00:15:22.590
legal documentation before that period of a year.

00:15:22.889 --> 00:15:25.110
Before Prema kind of, you know, took me aside

00:15:25.110 --> 00:15:28.389
and said, hey, so this is a project with USAID.

00:15:28.490 --> 00:15:31.090
And the whole mandate under the project was to

00:15:31.090 --> 00:15:33.750
create, within a period of three years, we had

00:15:33.750 --> 00:15:36.149
to create a network of thousand women entrepreneurs

00:15:36.149 --> 00:15:39.450
across six districts of Maharashtra and three

00:15:39.450 --> 00:15:43.360
districts of Bihar. who were capable of retailing

00:15:43.360 --> 00:15:46.059
clean energy products. So all the way from your

00:15:46.059 --> 00:15:49.100
solar lamps to your water pumps to your, you

00:15:49.100 --> 00:15:52.320
know, biogas units, you know, home biogas units

00:15:52.320 --> 00:15:55.600
of Kirloskar or up to, you know, even those times

00:15:55.600 --> 00:15:58.340
at that point of time, very high -ended home

00:15:58.340 --> 00:16:01.019
lighting systems with solar. So they had to look

00:16:01.019 --> 00:16:03.200
at the entire value chain of products and start

00:16:03.200 --> 00:16:06.419
retailing it to rural households. So I was the

00:16:06.419 --> 00:16:08.960
partnership manager and implementer that I went

00:16:08.960 --> 00:16:11.259
on in the project as a consultant. And my role

00:16:11.259 --> 00:16:14.740
was largely to see that all across the pipeline.

00:16:14.840 --> 00:16:16.720
So all the way from the training and initiation

00:16:16.720 --> 00:16:19.200
of these women to what are the roadblocks they

00:16:19.200 --> 00:16:21.299
face while they are scaling their businesses

00:16:21.299 --> 00:16:24.519
to actually creating the pipeline of partners

00:16:24.519 --> 00:16:27.139
who can keep pushing products to these women.

00:16:27.240 --> 00:16:29.340
Because, you know, creating a network is one

00:16:29.340 --> 00:16:31.299
thing, but the network doesn't have enough deep

00:16:31.299 --> 00:16:34.220
partnerships to sell. then you're not on all

00:16:34.220 --> 00:16:36.039
products to sell, right? Like, you know, for

00:16:36.039 --> 00:16:38.240
example, Amway has a bucket of products, right?

00:16:38.240 --> 00:16:40.460
So we did a bucket of clean energy products at

00:16:40.460 --> 00:16:42.779
that point of time, all the way from your 500

00:16:42.779 --> 00:16:45.940
to 24 ,000 rupees, right? Across the spectrum

00:16:45.940 --> 00:16:48.580
of products that women can retail. And how do

00:16:48.580 --> 00:16:51.100
you get them to retail it in total households?

00:16:51.259 --> 00:16:53.659
So that was the whole aim of the project. So

00:16:53.659 --> 00:16:56.960
at the end of three years, we had actually leveraged

00:16:56.960 --> 00:16:59.879
the project almost four times over in terms of

00:16:59.879 --> 00:17:02.789
partnerships. So partnerships. partners not only

00:17:02.789 --> 00:17:06.089
put gave products they put in money they gave

00:17:06.089 --> 00:17:08.630
grants but they also gave free products to these

00:17:08.630 --> 00:17:10.970
women they you know helped them set up manufacturing

00:17:10.970 --> 00:17:15.109
units off you know small small things like solar

00:17:15.109 --> 00:17:17.730
repair shops solar lamps all those kind of things

00:17:17.730 --> 00:17:19.650
that they were actually partnering with the women

00:17:19.650 --> 00:17:22.829
because they saw value to start looking at working

00:17:22.829 --> 00:17:27.109
with these women as their last mile point and

00:17:27.109 --> 00:17:30.329
that's what did you have Yeah. Sorry. So did

00:17:30.329 --> 00:17:34.109
you come across any particular incidents of women

00:17:34.109 --> 00:17:36.569
who stood out? How would you know that a woman

00:17:36.569 --> 00:17:39.910
is an entrepreneur, for example, or capable of

00:17:39.910 --> 00:17:42.529
being an entrepreneur? So I would think that

00:17:42.529 --> 00:17:45.609
there, see the, as I've seen, we know that often

00:17:45.609 --> 00:17:48.910
across is that most women in India are entrepreneurs,

00:17:49.049 --> 00:17:52.319
right? It is just that like. we don't recognize

00:17:52.319 --> 00:17:54.880
entrepreneurship or define it very differently.

00:17:55.039 --> 00:17:56.839
That's why if you see every day, you know, you

00:17:56.839 --> 00:17:59.019
have the unicorn, the decalcon, the sunicons

00:17:59.019 --> 00:18:02.240
and the endless cons that we keep throwing out

00:18:02.240 --> 00:18:05.059
there. These are all Silicon Valley definitions

00:18:05.059 --> 00:18:08.160
of entrepreneurs. And I don't think that's how

00:18:08.160 --> 00:18:10.660
Indian enterprises operate. So the women, the

00:18:10.660 --> 00:18:13.519
metrics that you cure everywhere, 14 % of India

00:18:13.519 --> 00:18:17.279
has women entrepreneurs. I think it's a very,

00:18:17.339 --> 00:18:19.299
very underreported number because our metrics

00:18:19.299 --> 00:18:23.619
are not right. In most of the Indian context,

00:18:23.700 --> 00:18:26.819
if you see across India, there are in every household

00:18:26.819 --> 00:18:29.839
entrepreneurs. Now, the thing is, most of rural

00:18:29.839 --> 00:18:32.700
India, 70 % of rural India gets their primary

00:18:32.700 --> 00:18:35.640
income from agriculture. Agriculture is one of

00:18:35.640 --> 00:18:37.980
the most hardest enterprises in this country.

00:18:38.539 --> 00:18:41.339
And the problem is we don't give it its due because

00:18:41.339 --> 00:18:43.359
you need to think of it just as an enterprise.

00:18:43.380 --> 00:18:45.480
There's so much that... People like you and me

00:18:45.480 --> 00:18:48.180
who run data or service businesses can control,

00:18:48.279 --> 00:18:49.859
right? You can control the client, you can control

00:18:49.859 --> 00:18:52.119
the delivery. In agriculture, you can control

00:18:52.119 --> 00:18:54.920
nothing. You have to wait for the water to fall

00:18:54.920 --> 00:18:57.940
or you have to use the reserve of water passingly

00:18:57.940 --> 00:19:01.039
at the right moment so that you hope for the

00:19:01.039 --> 00:19:04.259
right crop, right? The saddest part is you don't

00:19:04.259 --> 00:19:06.619
recognize this as entrepreneurs and you don't

00:19:06.619 --> 00:19:09.599
recognize this as an enterprise, right? So that's

00:19:09.599 --> 00:19:11.339
where the fundamental problem starts of where

00:19:11.339 --> 00:19:12.859
do you find these entrepreneurs? They exist.

00:19:13.390 --> 00:19:15.109
They're already doing something entrepreneurial

00:19:15.109 --> 00:19:19.329
way harder than what we all do regularly. Like

00:19:19.329 --> 00:19:21.670
I remember someone once I told someone, oh, I

00:19:21.670 --> 00:19:23.190
know agriculture in and out. I've been doing

00:19:23.190 --> 00:19:24.630
it for 15 years. They're like, yeah, have you

00:19:24.630 --> 00:19:26.529
tried growing? I said, no, that's where I don't

00:19:26.529 --> 00:19:29.609
know agriculture. I stand corrected. Right. I

00:19:29.609 --> 00:19:32.049
definitely think if I went to the field, I possibly

00:19:32.049 --> 00:19:35.750
will fail. Right. So it's one thing to have the

00:19:35.750 --> 00:19:37.849
theory. It's another thing to grow something

00:19:37.849 --> 00:19:41.329
based on a nature thing and and grow it up. So

00:19:41.329 --> 00:19:43.369
I think the first point is there are entrepreneurs.

00:19:43.690 --> 00:19:46.569
The second point is many of them are doing agricultural

00:19:46.569 --> 00:19:49.890
livelihoods, businesses. Many of these are family

00:19:49.890 --> 00:19:52.769
businesses. Unfortunately, in all your definition

00:19:52.769 --> 00:19:55.710
of unicorn, decalcon, family businesses is not

00:19:55.710 --> 00:19:58.470
counted. And who isn't a family business in this

00:19:58.470 --> 00:20:02.029
country? Everyone is, right? You name one top

00:20:02.029 --> 00:20:05.329
NSE company from Reliance to Aditya Billa, everyone

00:20:05.329 --> 00:20:08.150
is an entrepreneur since generations in this

00:20:08.150 --> 00:20:10.940
country. Even take Coimbatore, right? recall

00:20:10.940 --> 00:20:14.099
so many of them rnkvs all of these are family

00:20:14.099 --> 00:20:17.259
enterprises we are a nation of family enterprises

00:20:17.259 --> 00:20:20.500
so the reality is many of them do agriculture

00:20:20.500 --> 00:20:23.759
they have family enterprises where the definition

00:20:23.759 --> 00:20:26.299
goes against them is many times family enterprises

00:20:26.299 --> 00:20:29.460
in agriculture called subsistence by subsistence

00:20:29.460 --> 00:20:33.000
simply mean it just enough to survive and that's

00:20:33.000 --> 00:20:34.819
where we have taken a western definition and

00:20:34.819 --> 00:20:36.740
we have gone wrong we have taken a western definition

00:20:36.740 --> 00:20:41.140
and i remember a few republic days ago you know

00:20:41.140 --> 00:20:43.599
our chief prime minister modi had actually said

00:20:43.599 --> 00:20:46.140
we need to be proud as indians and have indian

00:20:46.140 --> 00:20:49.000
definitions to what we do he's right we took

00:20:49.000 --> 00:20:51.039
our definition subsistence we didn't understand

00:20:51.039 --> 00:20:53.000
it much and we put it in agriculture now every

00:20:53.000 --> 00:20:55.059
woman in who's working in agriculture and doing

00:20:55.059 --> 00:20:57.359
multi -livelihood which case she's doing poultry

00:20:57.359 --> 00:21:01.380
coterie vermicompost food businesses and agriculture

00:21:01.380 --> 00:21:03.539
is called subsistence level entrepreneur so she's

00:21:03.539 --> 00:21:06.180
not even considered an entrepreneur so the thing

00:21:06.180 --> 00:21:08.700
is when you change the definition And you say,

00:21:08.740 --> 00:21:10.359
remove the word subsistence. They're doing an

00:21:10.359 --> 00:21:13.059
enterprise to survive. They have their own family

00:21:13.059 --> 00:21:15.180
dynamics. They may not have external employees,

00:21:15.359 --> 00:21:18.859
but if I have been able to trust and get my husband

00:21:18.859 --> 00:21:21.480
in a job and make sure he leaves his job, you

00:21:21.480 --> 00:21:23.660
imagine the trust I have in my own business plan.

00:21:23.799 --> 00:21:26.220
I'm putting all my eggs in one basket. That's

00:21:26.220 --> 00:21:29.099
a huge amount of risk taking. So if they have

00:21:29.099 --> 00:21:31.920
their family in their enterprise, they're doing

00:21:31.920 --> 00:21:35.160
subsistence level work, but... Removing the word

00:21:35.160 --> 00:21:36.920
subsistence, they're doing multi -livelihood

00:21:36.920 --> 00:21:39.619
as we call them. They're doing multi -livelihood

00:21:39.619 --> 00:21:43.559
work. Is there a way to take them, add some more

00:21:43.559 --> 00:21:45.980
dimensions, teach them how to go to market better?

00:21:46.200 --> 00:21:48.660
The minute we just took that, we found many entrepreneurs

00:21:48.660 --> 00:21:50.759
across the country. Our job was only to help

00:21:50.759 --> 00:21:53.259
them get to market. And you'll see, if they implement

00:21:53.259 --> 00:21:55.839
something in clean energy works, you'll see them

00:21:55.839 --> 00:21:58.059
the next day implementing it themselves in agriculture.

00:21:58.299 --> 00:22:00.700
They will scale their private business also.

00:22:01.240 --> 00:22:03.660
So you don't have to worry. It's just about giving

00:22:03.660 --> 00:22:07.079
them the right skills to think of scale. Okay.

00:22:07.160 --> 00:22:11.619
Okay. So Impact Tree came out of an understanding

00:22:11.619 --> 00:22:15.759
of what needed to be done on the ground. Why

00:22:15.759 --> 00:22:18.140
did you think that you needed to measure impact?

00:22:18.839 --> 00:22:21.359
Well, it came largely actually also came from

00:22:21.359 --> 00:22:23.960
the USAID project. And I'll tell you why. Because

00:22:23.960 --> 00:22:26.480
what has to happen is that we used to have a

00:22:26.480 --> 00:22:28.440
lot of consultants who did very good work with

00:22:28.440 --> 00:22:31.099
us. But unfortunately, by the time these consultants

00:22:31.099 --> 00:22:33.019
would go into the field and give us a report

00:22:33.019 --> 00:22:35.640
of what was happening on the ground, and we would

00:22:35.640 --> 00:22:38.140
actually go about implementing it, the time to

00:22:38.140 --> 00:22:40.299
implementation sometimes would take six months,

00:22:40.359 --> 00:22:43.140
right? And to give you an example, right, if

00:22:43.140 --> 00:22:46.039
I've done a training in implementation and, you

00:22:46.039 --> 00:22:47.920
know, onboarded a particular women, you know,

00:22:47.920 --> 00:22:50.740
we onboarded women and we used to offer training

00:22:50.740 --> 00:22:52.680
in the morning, right? So we used to tell our

00:22:52.680 --> 00:22:54.059
trainers were available in the morning, they

00:22:54.059 --> 00:22:56.980
were employed with us. So we said 10 to 2 o 'clock.

00:22:57.309 --> 00:22:59.190
we would call you for training and after two

00:22:59.190 --> 00:23:01.069
o 'clock please go sell your product outside

00:23:01.069 --> 00:23:04.009
okay and i expect you to sell your product till

00:23:04.009 --> 00:23:06.470
six o 'clock when it's dusk makes perfect sense

00:23:06.470 --> 00:23:08.910
from an urban mindset you have four hours to

00:23:08.910 --> 00:23:12.990
sell give and take training you still have six

00:23:12.990 --> 00:23:14.789
hours to sell women should be able to really

00:23:14.789 --> 00:23:17.829
sell it was a massive failure i'll tell you why

00:23:17.829 --> 00:23:20.529
because we didn't factor into account how the

00:23:20.529 --> 00:23:22.970
women's clock worked So till 10 o 'clock in the

00:23:22.970 --> 00:23:24.549
morning, they generally would send their children.

00:23:24.630 --> 00:23:26.390
They would prepare all the, you know, whatever

00:23:26.390 --> 00:23:28.910
they had to prepare for the day. And then after

00:23:28.910 --> 00:23:30.289
that, when they would come out for training,

00:23:30.450 --> 00:23:32.369
they would be tired by 2 o 'clock. And 2 o 'clock,

00:23:32.369 --> 00:23:34.569
when they would go into households to sell, there

00:23:34.569 --> 00:23:37.309
weren't enough decision makers at 2 to 6 in the

00:23:37.309 --> 00:23:40.509
household. So there would be the wife or there

00:23:40.509 --> 00:23:43.049
may be old people or their children. None who

00:23:43.049 --> 00:23:46.009
can take concrete decision on anything more than

00:23:46.009 --> 00:23:48.960
200, 300 rupees to buy. And the products that

00:23:48.960 --> 00:23:51.500
we were trying to retail were close to 15 ,000,

00:23:51.519 --> 00:23:55.779
20 ,000 on EMI, of course, but sizable scales,

00:23:56.099 --> 00:23:59.160
right? So we started, by the time we got this

00:23:59.160 --> 00:24:02.019
very important input from one of our data partners

00:24:02.019 --> 00:24:05.220
on the ground, we had finished eight to nine

00:24:05.220 --> 00:24:08.160
months of training and implementation. Then for

00:24:08.160 --> 00:24:11.640
us to go back and recover the time was a real

00:24:11.640 --> 00:24:14.980
large challenge. The second challenge, for example,

00:24:15.099 --> 00:24:18.009
we said, We figured was that, you know, we said

00:24:18.009 --> 00:24:20.089
that women have to be empowered. So, you know,

00:24:20.109 --> 00:24:22.130
you break away from your families, you go, you

00:24:22.130 --> 00:24:24.869
sell, you become a retail salesperson. We did

00:24:24.869 --> 00:24:27.349
a lot of marketing campaigns around that. Most

00:24:27.349 --> 00:24:29.410
successful women, we realized after six months,

00:24:29.450 --> 00:24:31.750
were whose husbands accompanied them to sell.

00:24:32.269 --> 00:24:34.910
And the husbands started pushing them. So after

00:24:34.910 --> 00:24:36.730
six, the husbands would take the bike out with

00:24:36.730 --> 00:24:38.569
the women, give them ideas for marketing and

00:24:38.569 --> 00:24:40.569
taking them into the villages. Some husbands

00:24:40.569 --> 00:24:43.049
even retail their products in their own shops.

00:24:43.789 --> 00:24:45.869
And then the women would be given training by

00:24:45.869 --> 00:24:49.809
the husband saying, and try selling. These dynamics

00:24:49.809 --> 00:24:53.029
we realized very late, right? And we started

00:24:53.029 --> 00:24:55.789
off in hindsight. I realized that was also because

00:24:55.789 --> 00:24:58.369
we didn't have the tools to capture this data

00:24:58.369 --> 00:25:00.690
as it happened on the ground. So we relied on

00:25:00.690 --> 00:25:02.769
consultants who would come to us and give us

00:25:02.769 --> 00:25:04.710
this data periodically. But by the time the whole

00:25:04.710 --> 00:25:07.309
synthesis would happen, I would get it at a boat

00:25:07.309 --> 00:25:09.769
level. It would percolate further to the ground.

00:25:09.890 --> 00:25:11.869
It would just take months and implementation

00:25:11.869 --> 00:25:14.400
period would be over. So that's when actually

00:25:14.400 --> 00:25:16.440
Vivek, my co -founder, came into the picture

00:25:16.440 --> 00:25:18.680
because he comes from a manufacturing background,

00:25:18.839 --> 00:25:21.200
right? So for him, he was working at that point

00:25:21.200 --> 00:25:23.259
of time with Reliance and leading some of their

00:25:23.259 --> 00:25:25.960
biggest technology plants. And the data was everything

00:25:25.960 --> 00:25:28.200
for him, right? He would get mini -level, mini

00:25:28.200 --> 00:25:31.420
-second level data. And here I was not able to

00:25:31.420 --> 00:25:33.539
figure out, you know, how do I reconsider? It

00:25:33.539 --> 00:25:38.400
happened over months. Yeah. But then it started

00:25:38.400 --> 00:25:40.930
with a small thing saying. Can you, some of the

00:25:40.930 --> 00:25:43.690
things you implement in manufacturing, can we

00:25:43.690 --> 00:25:45.289
try it in the social sector? That's where we

00:25:45.289 --> 00:25:48.349
started. We spent an entire year doing that.

00:25:49.210 --> 00:25:52.049
Where we would collect data, Vivek and, you know,

00:25:52.069 --> 00:25:54.210
would synthesize it. He'd help us, give us models

00:25:54.210 --> 00:25:56.390
to build it up. And then we started seeing field

00:25:56.390 --> 00:25:59.190
level efficiency improve. Because the first thing

00:25:59.190 --> 00:26:00.670
that we understood, we realized it because of

00:26:00.670 --> 00:26:02.930
three nuances. And that's what we built our platform,

00:26:03.089 --> 00:26:05.089
Prabhav and Aathri, which is Prabhav, which is

00:26:05.089 --> 00:26:07.250
our social impact platform. Aathri, which is

00:26:07.250 --> 00:26:09.509
our ESG platform, is built on this basis, okay?

00:26:09.880 --> 00:26:11.779
First is we have realized Excel means a lot to

00:26:11.779 --> 00:26:14.079
me and you. It means nothing on the ground. 80

00:26:14.079 --> 00:26:16.599
% of this country doesn't understand Excel. It's

00:26:16.599 --> 00:26:19.220
only we talk 20 % who can keep learning micros,

00:26:19.420 --> 00:26:21.279
macros and all of that and figure things out.

00:26:21.779 --> 00:26:25.839
So we realized data has to be visual. So we took

00:26:25.839 --> 00:26:27.759
out data and we said, I'll give you a pie. I

00:26:27.759 --> 00:26:29.960
will give you a bar. I'll give you some dancing,

00:26:30.079 --> 00:26:31.920
you know, horizontal, straight pies. Then you

00:26:31.920 --> 00:26:33.839
start understanding data because your eye is

00:26:33.839 --> 00:26:36.180
able to perceive something. The second thing

00:26:36.180 --> 00:26:38.440
we realized very closely. Sabhi, can you go a

00:26:38.440 --> 00:26:41.720
little deeper into that strategy? What exactly,

00:26:41.859 --> 00:26:45.019
how did you visualize the data? So how we visualize

00:26:45.019 --> 00:26:47.519
the data is if I have to say a male and female,

00:26:47.700 --> 00:26:50.460
right? A number 24 and 25 doesn't mean anything.

00:26:50.559 --> 00:26:53.259
Your head will not automatically add it in most

00:26:53.259 --> 00:26:55.660
cases and say that, okay, these are the, you

00:26:55.660 --> 00:26:58.160
know, 49 people that we're talking about. But

00:26:58.160 --> 00:27:00.440
when you see a five, right? And you say, okay,

00:27:00.519 --> 00:27:03.799
40%, 50%, and it adds up or it shows you two

00:27:03.799 --> 00:27:06.940
bars. You're able to visualize more male, less

00:27:06.940 --> 00:27:10.900
female, right? Those visualization nuances changes

00:27:10.900 --> 00:27:13.599
for you, right? So that is the first thing we

00:27:13.599 --> 00:27:16.180
cracked. We said that let's make data visual.

00:27:16.339 --> 00:27:18.839
Can we have a platform where you can start visualizing

00:27:18.839 --> 00:27:20.859
data? You want to compare this year with next

00:27:20.859 --> 00:27:23.299
year? Compare. Everything will come to you as

00:27:23.299 --> 00:27:26.819
a visual. It will not come as a box in a four

00:27:26.819 --> 00:27:29.400
by four number. It is very difficult to understand

00:27:29.400 --> 00:27:32.240
numbers. The second thing we realized is. Remove

00:27:32.240 --> 00:27:36.900
this whole madness of English. If English is

00:27:36.900 --> 00:27:39.539
again known by only 2 or 4 % of the population,

00:27:39.619 --> 00:27:42.279
as fluently as we both are speaking here, the

00:27:42.279 --> 00:27:45.059
rest of the population speaks a vernacular language.

00:27:45.319 --> 00:27:48.000
So why can't data be collected and represented

00:27:48.000 --> 00:27:51.400
in vernacular language form? So most of our platform

00:27:51.400 --> 00:27:53.380
is built on a vernacular language base. You see

00:27:53.380 --> 00:27:55.720
it in the language, you understand. We will collect

00:27:55.720 --> 00:27:57.940
all the data in vernacular language. If you want

00:27:57.940 --> 00:28:00.799
for synthesis, yes, it comes in English. you

00:28:00.799 --> 00:28:03.019
know, at the analytical level of those who see

00:28:03.019 --> 00:28:06.200
those data. But everyone can visualize and look

00:28:06.200 --> 00:28:08.740
at data in vernacular language. And that's why

00:28:08.740 --> 00:28:10.759
we started looking at data. When we saw these

00:28:10.759 --> 00:28:13.200
two challenges, we started seeing a lot of field

00:28:13.200 --> 00:28:15.740
-level implementation challenges coming down.

00:28:16.039 --> 00:28:19.460
Only because of the fact that data was more something

00:28:19.460 --> 00:28:21.920
people could understand. They need not fear data.

00:28:22.279 --> 00:28:25.579
It's a friend, right? And then when that happens,

00:28:25.660 --> 00:28:28.400
you start seeing that how important it is to

00:28:28.400 --> 00:28:31.369
take decisions. You know, a lot of, I was reading

00:28:31.369 --> 00:28:32.990
an article somewhere where it said the world

00:28:32.990 --> 00:28:35.430
is data, AI, everyone is, you know, there's an

00:28:35.430 --> 00:28:38.549
old hype, chat GPT, we are going to lose jobs,

00:28:38.690 --> 00:28:42.430
70 ,000, 80 ,000, 1 million jobs gone. But, you

00:28:42.430 --> 00:28:44.410
know, when I keep reading all of this, I realized

00:28:44.410 --> 00:28:45.990
that five years back, this is where the social

00:28:45.990 --> 00:28:48.089
sector was. There was a lot of hype on data,

00:28:48.210 --> 00:28:50.589
system, you name it, everything we were implementing

00:28:50.589 --> 00:28:53.329
and still not getting through. But I know that

00:28:53.329 --> 00:28:55.369
the whole hype will be there. Then the dust will

00:28:55.369 --> 00:28:58.099
settle. And then at some point of time, we'll

00:28:58.099 --> 00:29:00.559
get realistic on what these tools can do for

00:29:00.559 --> 00:29:02.920
us. And at that point is when you will see the

00:29:02.920 --> 00:29:06.819
magic starting to happen. Right. So when you

00:29:06.819 --> 00:29:09.819
made the data visual, what is the change that

00:29:09.819 --> 00:29:12.200
you noticed on the ground? What were the differences

00:29:12.200 --> 00:29:16.539
in assessment that began to happen? So first

00:29:16.539 --> 00:29:19.559
is when we made it visual, someone who is just

00:29:19.559 --> 00:29:22.920
a 12th or a 5th or a graduate can start to understand

00:29:22.920 --> 00:29:26.450
it. Right. When it's a number. and there is no

00:29:26.450 --> 00:29:29.670
understanding of big or small, you start struggling

00:29:29.670 --> 00:29:31.910
if you're not educated enough to understand what

00:29:31.910 --> 00:29:34.410
is the association. When you start seeing it

00:29:34.410 --> 00:29:36.549
in forms where something is big, something is

00:29:36.549 --> 00:29:38.910
small, you start relating to it a lot easier.

00:29:39.170 --> 00:29:41.990
So I'll give you an example. We were running

00:29:41.990 --> 00:29:44.750
this project with one of our donors in about

00:29:44.750 --> 00:29:48.069
24 villages in Nanday district. This is a project

00:29:48.069 --> 00:29:51.650
with SSP only. Where the aim was to look at maternal

00:29:51.650 --> 00:29:54.779
health. And this particular... Donor and SSP

00:29:54.779 --> 00:29:56.519
had tried everything. I mean, they were seeing

00:29:56.519 --> 00:29:58.819
that this particular district, these 24 villages

00:29:58.819 --> 00:30:01.099
in the district were very backward. They had

00:30:01.099 --> 00:30:05.480
a lot of challenges where, you know, the girls

00:30:05.480 --> 00:30:07.920
are married very early. They had miscarriages,

00:30:08.160 --> 00:30:11.759
heavy amount of abortions, stillbirths, all of

00:30:11.759 --> 00:30:15.279
that. So they identified it to anemia. So then

00:30:15.279 --> 00:30:17.059
they started saying, OK, girls are very young.

00:30:17.099 --> 00:30:18.700
They're anemic. Let's start giving them supplements.

00:30:19.339 --> 00:30:22.259
You know, a lot of investment went into. Fortified

00:30:22.259 --> 00:30:25.240
products, fortified pills first. When people

00:30:25.240 --> 00:30:27.279
stopped eating medicines or didn't eat medicine,

00:30:27.599 --> 00:30:29.519
then fortified products started. So whether it

00:30:29.519 --> 00:30:32.359
is biscuits, whether it is daily items that can

00:30:32.359 --> 00:30:34.160
be fortified with iron, we used to give girls

00:30:34.160 --> 00:30:37.720
that. We did all of this, put about a year, and

00:30:37.720 --> 00:30:39.519
we were still not seeing results. Adoption was

00:30:39.519 --> 00:30:42.640
not happening on the ground, right? So then this

00:30:42.640 --> 00:30:44.720
data that we started collecting in 24 villages,

00:30:44.960 --> 00:30:47.559
this was, you know, it was made available and

00:30:47.559 --> 00:30:49.740
we sat down in a group with this data present

00:30:49.740 --> 00:30:52.680
open on our platform. with a lot of Anganwadi

00:30:52.680 --> 00:30:55.339
workers, ASHA workers. So in every village across

00:30:55.339 --> 00:30:57.259
the country, there are health workers that the

00:30:57.259 --> 00:30:59.599
government designates. Their main role is to

00:30:59.599 --> 00:31:02.460
help in the case of act as a midwife or get people

00:31:02.460 --> 00:31:05.000
to the PHC, etc. So you sat down with the ASHA

00:31:05.000 --> 00:31:07.579
workers and Anganwadi workers who are like community

00:31:07.579 --> 00:31:09.200
leaders in various villages. They, of course,

00:31:09.200 --> 00:31:12.220
operate the daycare system, but they are de facto

00:31:12.220 --> 00:31:14.480
community leaders. And we started showing this

00:31:14.480 --> 00:31:16.619
to them and saying, we don't even understand

00:31:16.619 --> 00:31:18.539
where we're going wrong. We've tried everything.

00:31:18.660 --> 00:31:21.549
Why is this not reducing? And I remember some

00:31:21.549 --> 00:31:23.630
Anganwadi workers in Ashabattas looked at the

00:31:23.630 --> 00:31:25.829
data and they said, you got it all wrong. So

00:31:25.829 --> 00:31:28.609
I said, okay, thank you. What did we do wrong?

00:31:28.710 --> 00:31:30.549
We have spent a lot of hours on this. Where are

00:31:30.549 --> 00:31:33.289
we going wrong? They said, you are going to the,

00:31:33.450 --> 00:31:36.009
you're collecting all this data and you're trying

00:31:36.009 --> 00:31:38.450
to find a solution without factoring into account

00:31:38.450 --> 00:31:42.470
decision maker on what women eat. So I said,

00:31:42.490 --> 00:31:44.829
okay, that is interesting. So what do you mean

00:31:44.829 --> 00:31:47.670
by that? So they said, see, the person. It's

00:31:47.670 --> 00:31:49.390
not the daughter -in -law who decides what she's

00:31:49.390 --> 00:31:50.890
eating. It's the mother -in -law who decides.

00:31:51.289 --> 00:31:53.809
And you're selling pretty much kids who are about

00:31:53.809 --> 00:31:55.910
20, 25 with a heart in the right place. They

00:31:55.910 --> 00:31:57.470
are social community workers. They are going

00:31:57.470 --> 00:32:02.910
to mother -in -laws and say, And the lady is

00:32:02.910 --> 00:32:04.769
humoring them because they're put in India. We're

00:32:04.769 --> 00:32:06.630
not going to tell you that, you know, you're

00:32:06.630 --> 00:32:08.210
bad or, you know, why are you wasting money?

00:32:08.369 --> 00:32:10.869
We're not doing that. They were just nice people.

00:32:11.009 --> 00:32:13.490
We're looking at the lady's story and go, please

00:32:13.490 --> 00:32:15.829
give me the packet. Thank you. Thank you. And

00:32:15.829 --> 00:32:17.829
all of that. And then she throws it on the side.

00:32:23.730 --> 00:32:29.490
That's where the problem was. So we realized

00:32:29.490 --> 00:32:33.549
that by the time the women ate, the husbands

00:32:33.549 --> 00:32:35.069
would eat, the family would eat, the children

00:32:35.069 --> 00:32:37.029
would eat. So even if it's a chicken curry, she's

00:32:37.029 --> 00:32:39.410
eating the gravy. The pieces have gone. So she

00:32:39.410 --> 00:32:41.450
has no nutrient value which is going into her

00:32:41.450 --> 00:32:44.130
system. And they said that this health awareness

00:32:44.130 --> 00:32:45.849
is not going to work. So I said, what will work?

00:32:46.140 --> 00:32:47.920
They said, it's clearly you're able to see the

00:32:47.920 --> 00:32:49.700
social dynamics in these villages. They are poor

00:32:49.700 --> 00:32:52.079
villages. Why don't we develop a campaign? And

00:32:52.079 --> 00:32:54.900
this the women came up with to say, how can you

00:32:54.900 --> 00:32:58.380
benefit by the birth of a better, healthier child?

00:32:58.819 --> 00:33:02.140
Right. We will show an economic angle to birth

00:33:02.140 --> 00:33:05.440
and then this will move. And I said, this was

00:33:05.440 --> 00:33:07.240
phenomenal. And they did a phenomenal job. They

00:33:07.240 --> 00:33:08.940
went down and sat with some mother -in -laws

00:33:08.940 --> 00:33:15.700
and said, Don't feed them eggs or chicken meat.

00:33:16.619 --> 00:33:19.099
Don't give her the good stuff. It's okay. So

00:33:19.099 --> 00:33:21.420
what will happen? She will go into a C -section.

00:33:21.900 --> 00:33:24.140
And then C -section will happen. Then the child

00:33:24.140 --> 00:33:26.759
will have a problem. It won't be solved in the

00:33:26.759 --> 00:33:28.940
PHC because such complicated cases they won't

00:33:28.940 --> 00:33:30.799
handle. So she will go to a private hospital.

00:33:31.180 --> 00:33:34.640
So you will get a bill of 1 .5 lakhs. Approximately

00:33:34.640 --> 00:33:38.240
when the child is born. Or you feed her now.

00:33:38.460 --> 00:33:40.619
You know, if they made a calculation and gave

00:33:40.619 --> 00:33:42.640
it to the mother -in -law, they said, egg for

00:33:42.640 --> 00:33:47.579
a week, chicken for a week, and vegetables for

00:33:47.579 --> 00:33:50.039
a week. After all this, you will get the maximum

00:33:50.039 --> 00:33:52.339
expense in about seven months. Because in nine

00:33:52.339 --> 00:33:54.240
months, she generally will go to her mother's

00:33:54.240 --> 00:33:56.819
place. Seven months' expense is somewhere around

00:33:56.819 --> 00:34:01.880
75 ,000. Then if she goes for a delivery, please

00:34:01.880 --> 00:34:04.119
note she'll have normal. And India is obsessed

00:34:04.119 --> 00:34:06.720
with normal, right? Rural India, everyone, even

00:34:06.720 --> 00:34:08.719
people who have never given birth, men will tell

00:34:08.719 --> 00:34:12.559
you, normal should be there, right? I said, that's

00:34:12.559 --> 00:34:16.260
the biggest achievement of all day. It's a normal

00:34:16.260 --> 00:34:20.099
delivery. So then 75, maximum another 20 -30

00:34:20.099 --> 00:34:23.880
you would spend. 20 maximum, right? So 20 -30

00:34:23.880 --> 00:34:26.840
in a PHC, it will get done. So in 1 lakh, your

00:34:26.840 --> 00:34:29.960
child is safe, your mother is safe. So you decide,

00:34:30.219 --> 00:34:33.940
do you want to spend 1 .5 then? Or spend it now,

00:34:33.940 --> 00:34:36.340
your daughter -in -law will go for 7 months.

00:34:37.159 --> 00:34:41.289
Literally, 8 -9 months. Kids were born in normal

00:34:41.289 --> 00:34:44.409
delivery. Two households got implemented. 24

00:34:44.409 --> 00:34:47.730
villages adopted it. It made socio -economic

00:34:47.730 --> 00:34:52.050
sense to actually outgift the women. And my question

00:34:52.050 --> 00:34:55.269
is, this insight, we cannot see. We are from

00:34:55.269 --> 00:34:57.989
an urban context. This insight, only an Anganwadi

00:34:57.989 --> 00:35:01.130
worker can tell you. Across India, when we have

00:35:01.130 --> 00:35:04.610
seen, if you don't believe it, about 46 % of

00:35:04.610 --> 00:35:07.559
this country operates in barter system. And we

00:35:07.559 --> 00:35:09.980
don't even have a way of measuring it. Just today

00:35:09.980 --> 00:35:11.679
morning, I had gone to a relative's place. They

00:35:11.679 --> 00:35:13.820
said, oh, you're going to Kutralam tomorrow.

00:35:13.960 --> 00:35:17.420
So in Kutralam, when I grew up, it was, if you

00:35:17.420 --> 00:35:21.239
wanted to Maligapu, the flower garden, we would

00:35:21.239 --> 00:35:25.780
give one tub of rice. And she's just a six -year

00:35:25.780 --> 00:35:28.139
-old. Forty years back, we used to measure life

00:35:28.139 --> 00:35:30.820
with one or two glasses of rice. Today, we are

00:35:30.820 --> 00:35:33.280
pegging it with the dollar and the rupee and

00:35:33.280 --> 00:35:36.300
all of that. It's not sunk that down inside.

00:35:37.039 --> 00:35:39.559
How do people understand these nuances? It's

00:35:39.559 --> 00:35:42.619
only when they see data. We cannot, because of

00:35:42.619 --> 00:35:44.960
our urban understanding, our urban upbringing,

00:35:45.159 --> 00:35:46.960
our exposure to the world, which has its own

00:35:46.960 --> 00:35:49.639
pluses. That's why it allows us to work at it

00:35:49.639 --> 00:35:52.420
with a different technology lens. But we can't

00:35:52.420 --> 00:35:55.179
understand what they go through. And who better

00:35:55.179 --> 00:35:57.320
to actually give better solutions than them on

00:35:57.320 --> 00:36:03.280
the ground? No, this is really informative, Rajshri,

00:36:03.340 --> 00:36:06.300
because... I don't think I was, you know, when

00:36:06.300 --> 00:36:08.519
it came to data again, it was the same thing

00:36:08.519 --> 00:36:11.039
that you spoke about. It's all bar charts and

00:36:11.039 --> 00:36:14.860
figures. And then suddenly you begin to see how

00:36:14.860 --> 00:36:18.000
the implementation of something on the ground

00:36:18.000 --> 00:36:21.360
changes. And then when you measure it in numbers

00:36:21.360 --> 00:36:23.719
again, people say, oh, and then the connecting

00:36:23.719 --> 00:36:26.300
link is gone. What is it that people actually

00:36:26.300 --> 00:36:28.340
did? In this case, you brought out the connection

00:36:28.340 --> 00:36:32.900
link that the Anganwadi people had the. you know,

00:36:32.960 --> 00:36:36.679
insight to say, link this to the health of the

00:36:36.679 --> 00:36:39.500
child, as opposed to saying, look after your

00:36:39.500 --> 00:36:41.699
daughter -in -law. I mean, it's such a sea change

00:36:41.699 --> 00:36:47.059
in how they think. Wow. So when you, are you

00:36:47.059 --> 00:36:50.360
finding these kinds of examples practically on

00:36:50.360 --> 00:36:53.219
every project that you work in when it comes

00:36:53.219 --> 00:36:57.219
down to looking at data points? Completely. Because

00:36:57.219 --> 00:36:59.719
like I'll tell you, it's just not an NGO perspective.

00:36:59.780 --> 00:37:02.230
And that's where... You know, I have a great

00:37:02.230 --> 00:37:04.650
management team and I have a great board. I'm

00:37:04.650 --> 00:37:06.889
very lucky to have them. When we were working

00:37:06.889 --> 00:37:09.469
on a corporate project in Saudi Arabia, we again

00:37:09.469 --> 00:37:12.329
understood that most of India, forget India,

00:37:12.429 --> 00:37:14.269
most of the world operates on socio -cultural

00:37:14.269 --> 00:37:17.530
nuances. As human, we collectively like to believe

00:37:17.530 --> 00:37:20.369
it's all economics. But the reality is most of

00:37:20.369 --> 00:37:23.489
our decisions are socio -cultural more than economics.

00:37:23.789 --> 00:37:26.530
And we realized that when we were working with

00:37:26.530 --> 00:37:29.469
a large, it's a huge multi -billion dollar company

00:37:29.469 --> 00:37:32.429
there. And they were having a large problem of

00:37:32.429 --> 00:37:34.690
attrition, right? So they used to have people

00:37:34.690 --> 00:37:37.150
who used to come as migratory population and

00:37:37.150 --> 00:37:39.070
then, you know, they would be able to put them

00:37:39.070 --> 00:37:40.469
through the process. They were serving certain

00:37:40.469 --> 00:37:43.110
things to the, you know, Saudi government. It's

00:37:43.110 --> 00:37:46.190
a service -oriented manufacturing company, installation

00:37:46.190 --> 00:37:49.030
service system company, to put it that way. So

00:37:49.030 --> 00:37:51.369
when we started using the same lens there on

00:37:51.369 --> 00:37:53.829
their data, we started realizing they had a unique

00:37:53.829 --> 00:37:56.150
problem, right? So Saudiization mandates that

00:37:56.150 --> 00:37:58.429
if they have to actually get X percent of government

00:37:58.429 --> 00:38:01.349
contracts. They need to employ people who are

00:38:01.349 --> 00:38:04.030
Saudis. Now, Saudis are used to a different work

00:38:04.030 --> 00:38:06.269
pattern than migratory workforce. So migratory

00:38:06.269 --> 00:38:08.809
workforce have come there only for a reason that

00:38:08.809 --> 00:38:11.269
they need to earn money. So they will pack in

00:38:11.269 --> 00:38:14.869
that 24 hours for four days and it wouldn't make

00:38:14.869 --> 00:38:17.530
a difference. A Saudi local is not going to be

00:38:17.530 --> 00:38:19.590
doing that because he lives there, right? He

00:38:19.590 --> 00:38:21.570
comes with a difference to cultural dynamics.

00:38:21.869 --> 00:38:24.550
So there my team was led by Vivek, were able

00:38:24.550 --> 00:38:27.789
to optimize work schedules around sociocultural

00:38:27.789 --> 00:38:30.579
elements. which allowed them to actually improve

00:38:30.579 --> 00:38:34.219
their efficiency as an organization by 37%. And

00:38:34.219 --> 00:38:36.659
it actually allowed them to increase a considerable

00:38:36.659 --> 00:38:38.820
amount of their turnover. Because we were able

00:38:38.820 --> 00:38:40.840
to look at the workflow and we realized it was

00:38:40.840 --> 00:38:43.340
all built to a migratory population and it was

00:38:43.340 --> 00:38:46.099
built by someone from the West. So everything

00:38:46.099 --> 00:38:49.380
was scheduled without actually thinking how a

00:38:49.380 --> 00:38:52.139
Sri Lankan or a Filipino or an Indian would work.

00:38:52.460 --> 00:38:55.699
And then adding an element, a Saudi managerial

00:38:55.699 --> 00:38:59.199
person, right? So even the whole... work process

00:38:59.199 --> 00:39:02.059
system we had to optimize from a point of view

00:39:02.059 --> 00:39:04.000
of what are the social cultural dynamics which

00:39:04.000 --> 00:39:08.059
are nuance to each culture or sector and then

00:39:08.059 --> 00:39:10.300
how can you optimize that into a system or a

00:39:10.300 --> 00:39:13.139
data point and that way we are now able to improve

00:39:13.139 --> 00:39:15.679
in efficiency so we see that in most of our projects

00:39:15.679 --> 00:39:19.940
it's not only about the fact you know how much

00:39:19.940 --> 00:39:22.760
economics you look at it you take away that lens

00:39:22.760 --> 00:39:25.579
of economics and you put in socio -cultural aspects

00:39:26.440 --> 00:39:29.179
And that is, which is endemic to that area. You

00:39:29.179 --> 00:39:31.599
will start to see similarity. And then you will

00:39:31.599 --> 00:39:33.940
see similarity, like from the Persian side to

00:39:33.940 --> 00:39:36.139
the, you know, till about China border, we are

00:39:36.139 --> 00:39:38.800
very similar. Most of us, socio -culturally,

00:39:38.860 --> 00:39:41.340
till even Indonesia. Then from Korea, it's another

00:39:41.340 --> 00:39:43.579
segment all the way up to Japan, right? With

00:39:43.579 --> 00:39:46.059
different ethos, different cultures. But if you're

00:39:46.059 --> 00:39:49.159
able to take that 80%, which is similar and standardize

00:39:49.159 --> 00:39:51.139
it, then you have to only work with the 20%,

00:39:51.139 --> 00:39:56.300
which is different to optimize. Amazing. I think

00:39:56.300 --> 00:39:59.639
we've hardly covered your career and talked more

00:39:59.639 --> 00:40:04.500
about a couple of projects, but I think it throws

00:40:04.500 --> 00:40:07.000
light on something that you started off with,

00:40:07.079 --> 00:40:09.559
the conversation we had just before where you

00:40:09.559 --> 00:40:12.880
said analysis is not just taking in figures that

00:40:12.880 --> 00:40:15.820
exist and trying to make sense of those figures

00:40:15.820 --> 00:40:18.940
because the background of how those figures came

00:40:18.940 --> 00:40:21.239
to be and what is the process that was put in

00:40:21.239 --> 00:40:24.409
place is not understood. So then try to draw

00:40:24.409 --> 00:40:27.289
inferences from that. Like you said, in the case

00:40:27.289 --> 00:40:29.849
of whether it was the Saudi European company

00:40:29.849 --> 00:40:33.769
or the village, without having that insight,

00:40:33.989 --> 00:40:36.230
how do you even begin to understand that this

00:40:36.230 --> 00:40:38.510
is how we solve the problem? Because you can

00:40:38.510 --> 00:40:41.329
say that they need nutrition, but that is like

00:40:41.329 --> 00:40:45.690
the most basic inference that you can draw. But

00:40:45.690 --> 00:40:49.150
how do you break this, Rajshri? How do you break...

00:40:49.480 --> 00:40:52.880
Some of these things which are so deeply ingrained

00:40:52.880 --> 00:40:57.380
in how data is collected that you have to bring

00:40:57.380 --> 00:41:00.400
about a change as you go forward. I think the

00:41:00.400 --> 00:41:03.980
first step of process is standardization, right?

00:41:04.039 --> 00:41:06.780
And then we have also seen everything is not

00:41:06.780 --> 00:41:09.860
very disparate. When you start looking at data

00:41:09.860 --> 00:41:11.719
and you start working on data analytics, you

00:41:11.719 --> 00:41:14.559
will start seeing similarity more than dissimilarity,

00:41:14.619 --> 00:41:16.920
right? Everyone likes to think they're very unique.

00:41:16.960 --> 00:41:20.260
We are not. And we are very similar, more similar

00:41:20.260 --> 00:41:22.360
than we realize, given the similar backgrounds

00:41:22.360 --> 00:41:25.059
we might come from. Because if you have similar

00:41:25.059 --> 00:41:27.619
backgrounds, you have similar networks, similar

00:41:27.619 --> 00:41:30.360
upbringing, similar ethical values, and similar

00:41:30.360 --> 00:41:33.480
exposure. So that way, these are broad benchmarks

00:41:33.480 --> 00:41:36.800
that help you to start condensing data, which

00:41:36.800 --> 00:41:40.179
might seem very all over the place, to a level

00:41:40.179 --> 00:41:42.260
that it is understandable. The first step is

00:41:42.260 --> 00:41:44.460
standardization. So being able to call out that

00:41:44.460 --> 00:41:47.449
standardization across data is important. The

00:41:47.449 --> 00:41:49.469
second step that, of course, that we've built

00:41:49.469 --> 00:41:51.829
in is using technology. So technology, once you

00:41:51.829 --> 00:41:54.730
standardize, is a very good medium to scale because

00:41:54.730 --> 00:41:56.530
you can take something which is standardized,

00:41:56.650 --> 00:41:58.710
process it into a system and the system will

00:41:58.710 --> 00:42:01.250
replicate. And hence, if you see, we took a decision

00:42:01.250 --> 00:42:03.429
of keeping it as a SaaS or a licensed platform.

00:42:03.550 --> 00:42:05.469
You use Google, you pay a monthly subscription,

00:42:05.630 --> 00:42:07.409
you can pay a monthly subscription for all our

00:42:07.409 --> 00:42:09.550
data platforms so that it's affordable and cheap.

00:42:09.989 --> 00:42:13.349
And then as the system gets more and more and

00:42:13.349 --> 00:42:15.329
more data that comes into the system, the system

00:42:15.329 --> 00:42:17.579
will start to learn. That's what ChatGPT did.

00:42:17.780 --> 00:42:21.059
And that is precisely how we are driving and

00:42:21.059 --> 00:42:24.000
looking at our data platforms growing at that

00:42:24.000 --> 00:42:26.280
sense. And with reference to, of course, like

00:42:26.280 --> 00:42:29.119
what we talked earlier in careers in data, right?

00:42:29.199 --> 00:42:32.219
I would think that, you know, I mean, there are

00:42:32.219 --> 00:42:34.420
and we hire a lot of interns in data analytics.

00:42:34.519 --> 00:42:36.780
We hire from economics. We hire from engineering.

00:42:37.239 --> 00:42:40.300
I think there is a three, you know, three step

00:42:40.300 --> 00:42:42.780
process that I might use for evaluation and maybe

00:42:42.780 --> 00:42:45.059
it might help some candidates for thinking of

00:42:45.059 --> 00:42:47.199
careers because. I now see that increasingly

00:42:47.199 --> 00:42:50.360
you open any YouTube academy, everyone is throwing

00:42:50.360 --> 00:42:52.840
a data analytical career of some sort at you,

00:42:52.900 --> 00:42:55.340
right? So everyone is saying this is the in thing.

00:42:55.519 --> 00:42:58.239
This is the only career where you will possibly

00:42:58.239 --> 00:43:01.139
never be let go. All kinds of tall promises,

00:43:01.260 --> 00:43:05.480
right? I think we use a simple three -state process.

00:43:05.639 --> 00:43:08.039
First is when you're looking at data, you need

00:43:08.039 --> 00:43:11.260
to love math. That's the basic. You need to be

00:43:11.260 --> 00:43:15.000
excited by numbers, right? Understand whether

00:43:15.000 --> 00:43:18.840
your numbers are naturally you want to pull them

00:43:18.840 --> 00:43:21.360
into things, want to compare them, see is there

00:43:21.360 --> 00:43:23.639
some trend that is going. You may not do it for

00:43:23.639 --> 00:43:26.099
numbers, but this causation habit, right? What

00:43:26.099 --> 00:43:28.559
caused this? Why is this happening? Are you a

00:43:28.559 --> 00:43:31.239
person who thinks at that level? Even if you

00:43:31.239 --> 00:43:33.400
start thinking at causation, math is an easier

00:43:33.400 --> 00:43:35.699
problem to solve. We have enough and more models

00:43:35.699 --> 00:43:37.639
which will help you solve math. You plug it in,

00:43:37.679 --> 00:43:40.380
it will vomit enough things at you at any engine.

00:43:40.809 --> 00:43:43.349
So it's not a problem. But do you want, are you

00:43:43.349 --> 00:43:45.849
a person who likes causation and wanting to keep

00:43:45.849 --> 00:43:47.789
on thinking? So the creative types, right, who

00:43:47.789 --> 00:43:50.889
like to find out why, those are very inquisitive.

00:43:50.929 --> 00:43:52.969
That's the first benchmark we look at. When we

00:43:52.969 --> 00:43:55.570
look at people, we hire for data. The second

00:43:55.570 --> 00:43:59.190
most important thing that we look at also, at

00:43:59.190 --> 00:44:02.090
the same time, how much of a big picture person

00:44:02.090 --> 00:44:05.469
are you? Okay, so when you look at data, it can

00:44:05.469 --> 00:44:08.289
get very micro, right? So I hear a lot of people

00:44:08.289 --> 00:44:10.590
saying, I want to do a career in data analytics.

00:44:10.829 --> 00:44:13.170
Why? I think it's cool. What are you doing right

00:44:13.170 --> 00:44:16.190
now? I'm coding somewhere. And okay, great. So

00:44:16.190 --> 00:44:18.489
do you want to code with us? No, I want a career

00:44:18.489 --> 00:44:20.329
in data analytics. Do you want to do operations?

00:44:20.630 --> 00:44:23.210
No, I don't want to travel. So I said, then what

00:44:23.210 --> 00:44:25.429
does data mean? I mean, I can't get you to code.

00:44:25.570 --> 00:44:28.070
I can't get you to travel. So which means you

00:44:28.070 --> 00:44:29.949
want to be in front of a system and look at a

00:44:29.949 --> 00:44:31.510
bunch of numbers. That's not data analytics.

00:44:32.289 --> 00:44:36.070
That's not how it works. Most people who do data

00:44:36.070 --> 00:44:38.579
analytics go to the field. You have to be able

00:44:38.579 --> 00:44:41.579
to talk to people, hold a conversation because

00:44:41.579 --> 00:44:44.219
the insights you get from there is where you

00:44:44.219 --> 00:44:47.239
can train a model. So the maximum money you will

00:44:47.239 --> 00:44:49.679
make in a career is when you have or develop

00:44:49.679 --> 00:44:52.860
the ability, use your causation skills to start

00:44:52.860 --> 00:44:56.400
going into the depth of what causes even through

00:44:56.400 --> 00:44:57.880
global things. So you should have the ability

00:44:57.880 --> 00:45:01.039
to read, right? So if you're able to go look

00:45:01.039 --> 00:45:03.860
at causation, communicate and read, it's very

00:45:03.860 --> 00:45:06.880
good to start a career in data analytics. Believe

00:45:06.880 --> 00:45:08.920
me, this is what I tell most of my team. I say,

00:45:09.000 --> 00:45:11.860
today Google does everything. So make sure you're

00:45:11.860 --> 00:45:14.679
able to read and get the right insight. So that's

00:45:14.679 --> 00:45:16.260
where you will make your money in the future.

00:45:16.940 --> 00:45:20.800
Right. And I think another thing that you started

00:45:20.800 --> 00:45:23.500
this off, that you're understanding that vernacular,

00:45:23.639 --> 00:45:26.960
a whole lot of data, like you said, is so how

00:45:26.960 --> 00:45:29.199
have you done that? You've managed to connect

00:45:29.199 --> 00:45:33.400
vernacular interface to most of the data that

00:45:33.400 --> 00:45:35.909
you display? And in how many languages? How do

00:45:35.909 --> 00:45:39.150
you see that change? So right now, all our platforms,

00:45:39.210 --> 00:45:41.630
so the data collection happens fully in vernacular.

00:45:41.710 --> 00:45:44.150
So that's like a survey link or a form or whatever

00:45:44.150 --> 00:45:45.829
it is that goes out. That'll be first in the

00:45:45.829 --> 00:45:48.670
vernacular language. We get that because Google's

00:45:48.670 --> 00:45:50.750
vernacular engines are not properly trained yet.

00:45:50.949 --> 00:45:53.929
So unfortunately, we can't directly Google translate.

00:45:54.050 --> 00:45:56.670
We use the help of people on the ground to refine

00:45:56.670 --> 00:45:59.429
that, you know, nuance on to get it contextually

00:45:59.429 --> 00:46:02.250
right. The second thing is that our display features

00:46:02.250 --> 00:46:04.610
in about 10 languages we've programmed it for

00:46:04.610 --> 00:46:07.050
it to display. So there we are using a bit of

00:46:07.050 --> 00:46:09.250
Google Analytics and our own question bank. So

00:46:09.250 --> 00:46:10.710
one feature that we've built is we have our own

00:46:10.710 --> 00:46:13.150
question bank and our platform where a lot of

00:46:13.150 --> 00:46:15.230
the data, a lot of the questions are already

00:46:15.230 --> 00:46:17.170
translated in vernacular language. So you can

00:46:17.170 --> 00:46:19.070
adopt some of that so that you get a constant,

00:46:19.130 --> 00:46:22.550
you know, context, actualization right. And that

00:46:22.550 --> 00:46:24.510
way, that is a longer haul thing where we are

00:46:24.510 --> 00:46:27.130
slowly building to. Because the reality is...

00:46:27.759 --> 00:46:29.599
Vernacular, it says there are very few companies

00:46:29.599 --> 00:46:33.199
in India who are operating on vernacular language

00:46:33.199 --> 00:46:36.019
mediums, right? And I think that will still take

00:46:36.019 --> 00:46:38.920
time because I still see it about two or three

00:46:38.920 --> 00:46:41.440
years away where we will actually have a recognition

00:46:41.440 --> 00:46:43.539
for Indian languages and then we are able to

00:46:43.539 --> 00:46:45.780
build on it. Because there is also a lot of dialect

00:46:45.780 --> 00:46:49.360
issues, right? If I go to an Ajmer, the way Rajasthani,

00:46:49.380 --> 00:46:51.900
Marwari or whatever the local dialect is spoken

00:46:51.900 --> 00:46:54.119
there, it's a form of Hindi, it's very different

00:46:54.119 --> 00:46:57.039
from when you go to Jaipur. So there is an element

00:46:57.039 --> 00:47:00.960
of localization there still. And that's really

00:47:00.960 --> 00:47:03.480
another aspect of this entire thing, because

00:47:03.480 --> 00:47:06.699
once you crack this in this particular field,

00:47:06.900 --> 00:47:09.280
there are so many other places, like you said,

00:47:09.400 --> 00:47:12.500
where it can be applied to and how it goes. OK,

00:47:12.599 --> 00:47:16.960
so how do you see Impact Tree changing over the

00:47:16.960 --> 00:47:20.940
next few years? What are the next milestones

00:47:20.940 --> 00:47:25.460
that you have kind of seen for yourself? So we

00:47:25.460 --> 00:47:28.480
are right now largely a B2B company. And as we

00:47:28.480 --> 00:47:30.800
operate, all our technology platforms are being

00:47:30.800 --> 00:47:33.059
used by either field level operators or at the

00:47:33.059 --> 00:47:36.460
board level. So as we scale and grow, we see

00:47:36.460 --> 00:47:39.719
ourselves very clearly growing much. We were

00:47:39.719 --> 00:47:41.460
one of the eight enterprises to go to the World

00:47:41.460 --> 00:47:46.659
Economic Forum. That was huge that you got to

00:47:46.659 --> 00:47:50.380
diverse. Wonderful. That's an amazing achievement

00:47:50.380 --> 00:47:55.219
at this early stage. Wonderful. Yes. Yeah. Having

00:47:55.219 --> 00:47:57.559
gone there, we now have exposure to international

00:47:57.559 --> 00:48:00.039
markets. So the idea is to take our social impact

00:48:00.039 --> 00:48:03.519
platforms and start scaling it vertically across

00:48:03.519 --> 00:48:06.480
countries. But at the same time, we are using

00:48:06.480 --> 00:48:08.820
a lot of the insights we built in our social

00:48:08.820 --> 00:48:11.179
impact to build a platform much faster in the

00:48:11.179 --> 00:48:13.900
ESG space called Aarti. So that is the ESG platform.

00:48:14.119 --> 00:48:16.880
So, you know, I've used ESG once or twice on

00:48:16.880 --> 00:48:19.530
this. you know the session so just to demystify

00:48:19.530 --> 00:48:21.809
it for people who are here ESG is environmental

00:48:21.809 --> 00:48:24.949
social and governance and now most companies

00:48:24.949 --> 00:48:28.130
in the world are mandating that people have sustainability

00:48:28.130 --> 00:48:30.769
plans companies have sustainability plans on

00:48:30.769 --> 00:48:33.769
how they grow largely because of the point that

00:48:33.769 --> 00:48:36.170
we all have realized resources are not infinite

00:48:36.170 --> 00:48:37.630
at some point we're going to start running out

00:48:37.630 --> 00:48:40.210
of crucial resources and somewhere companies

00:48:40.210 --> 00:48:43.329
need to be prepared for that eventuality if they

00:48:43.329 --> 00:48:46.030
need to survive at that point of time So we've

00:48:46.030 --> 00:48:48.250
started working on an interesting platform there,

00:48:48.349 --> 00:48:53.230
which we've already implemented. We're two companies

00:48:53.230 --> 00:48:54.690
and, you know, we're slowly scaling it up as

00:48:54.690 --> 00:48:56.610
a pilot. Largely from a point of view of seeing

00:48:56.610 --> 00:48:58.670
how does, you know, at the ground level operators

00:48:58.670 --> 00:49:01.010
and senior operational level managers understand

00:49:01.010 --> 00:49:03.630
data across ESG spectrum. How can they actually

00:49:03.630 --> 00:49:05.909
conserve in places? How can they, you know, spend

00:49:05.909 --> 00:49:08.530
better in certain places and that way neutralize

00:49:08.530 --> 00:49:11.269
their sustainability score, right? So that platform

00:49:11.269 --> 00:49:14.079
which we're developing very fast right now. We

00:49:14.079 --> 00:49:15.780
are actually scaling it through partners in Middle

00:49:15.780 --> 00:49:17.880
East and Southeast Asia. So the next two, three

00:49:17.880 --> 00:49:20.219
years is going to be really dedicated to scaling

00:49:20.219 --> 00:49:23.639
this B2B offering for us. And then beyond that,

00:49:23.699 --> 00:49:26.179
of course, we have certain B2C verticals which

00:49:26.179 --> 00:49:28.920
are coming up, but that is more, you know, experimental

00:49:28.920 --> 00:49:31.880
at this point of time. Like I said, we're building

00:49:31.880 --> 00:49:34.280
a network of rural women entrepreneurs and seeing

00:49:34.280 --> 00:49:36.960
what is the technology product that can help

00:49:36.960 --> 00:49:40.530
them scale social and economically. retaining

00:49:40.530 --> 00:49:42.610
intact their cultural nuances, of course. So

00:49:42.610 --> 00:49:44.829
then in that end, we are working with traditional

00:49:44.829 --> 00:49:46.550
value trade businesses, whether it's textile

00:49:46.550 --> 00:49:49.190
or agriculture. And we are closer to now coming

00:49:49.190 --> 00:49:50.690
to a point where we are starting to think what

00:49:50.690 --> 00:49:53.030
is the platform solution offering to a B2C space.

00:49:53.289 --> 00:49:57.230
So that's the large way that we are going. And

00:49:57.230 --> 00:49:59.309
we are very much, I mean, like I've said earlier,

00:49:59.510 --> 00:50:02.250
we are very much as a company committed to listing

00:50:02.250 --> 00:50:05.829
by August of 2028. And that's the benchmark we've

00:50:05.829 --> 00:50:08.320
set internally. You know, that's something we're

00:50:08.320 --> 00:50:10.780
working towards with a clear mindset that by

00:50:10.780 --> 00:50:12.500
that time we would have built a company which

00:50:12.500 --> 00:50:14.980
is capable of scaling on its own. And that's

00:50:14.980 --> 00:50:17.440
the core of what we want to leave as promoters.

00:50:18.500 --> 00:50:22.000
And you make it, I think it's obviously been

00:50:22.000 --> 00:50:24.380
thought through very well. I just wanted, one,

00:50:24.480 --> 00:50:27.579
the thing on what kind of conversations did you

00:50:27.579 --> 00:50:29.860
have in Davos? Was it more of a presentation

00:50:29.860 --> 00:50:32.480
of what you were doing? Or was there interest

00:50:32.480 --> 00:50:35.880
in, you know, how is it that you guys are operating?

00:50:36.539 --> 00:50:38.519
And what are the challenges? What are the conversations

00:50:38.519 --> 00:50:41.659
about? So I think that was a very interesting

00:50:41.659 --> 00:50:44.019
space for us, to be honest, because it was after

00:50:44.019 --> 00:50:46.059
COVID. So there was a lot of networking, right?

00:50:46.079 --> 00:50:48.400
After two years, people were like, we are finally

00:50:48.400 --> 00:50:51.780
here. We can talk, right? Enough of this. But

00:50:51.780 --> 00:50:56.000
I also think that it was very interesting because

00:50:56.000 --> 00:50:58.079
we were eight startups in the Tamil Nadu government

00:50:58.079 --> 00:51:00.539
at that point of time, you know, with another

00:51:00.539 --> 00:51:03.219
organization led by Amit. which was you imagine,

00:51:03.380 --> 00:51:05.679
they had, you imagine this, you know, finished

00:51:05.679 --> 00:51:08.900
its final event in Tamil Nadu. So you imagine

00:51:08.900 --> 00:51:13.119
a chosen eight of us ready to go to Davos. And

00:51:13.119 --> 00:51:15.480
the amazing part, and there was another counterpart

00:51:15.480 --> 00:51:17.280
there called Think Davos who helped us through

00:51:17.280 --> 00:51:19.139
the whole process. But I think the amazing part

00:51:19.139 --> 00:51:22.219
of it was that suddenly the bigwigs of the world

00:51:22.219 --> 00:51:25.699
were like, why did these eight startups who people

00:51:25.699 --> 00:51:29.219
barely knew about, I mean, how did they get to

00:51:29.219 --> 00:51:32.159
crash our party? So there was a lot of curiosity

00:51:32.159 --> 00:51:36.019
on that, on how did you get here, right? You're

00:51:36.019 --> 00:51:39.739
not a unicorn. You're not a unicorn. Like, how?

00:51:41.159 --> 00:51:44.619
What are you doing here? Exactly. So that was

00:51:44.619 --> 00:51:47.360
a really nice icebreaker because a lot of people

00:51:47.360 --> 00:51:50.059
wanted to know what you're doing. And I would

00:51:50.059 --> 00:51:51.739
think from an Indian government perspective,

00:51:52.179 --> 00:51:55.320
you know, it was very proud to also see the Indian

00:51:55.320 --> 00:51:57.599
startup scene. We had the largest attendance

00:51:57.599 --> 00:52:00.000
and divorce in that particular year. The largest.

00:52:00.519 --> 00:52:03.320
arena of Indian startups that is a very proud

00:52:03.320 --> 00:52:04.860
moment also for the government where they were

00:52:04.860 --> 00:52:06.900
interacting very open with you trying to understand

00:52:06.900 --> 00:52:09.219
okay can you solution for us can you work with

00:52:09.219 --> 00:52:12.219
us so I think a lot of that was very interesting

00:52:12.219 --> 00:52:14.119
from a startup in a learning curve perspective

00:52:14.119 --> 00:52:17.699
I think personally as a founder I would think

00:52:17.699 --> 00:52:20.960
how to create a large vision right and I don't

00:52:20.960 --> 00:52:22.920
think there is enough mentorship around that

00:52:22.920 --> 00:52:25.320
right when you interact with the richest of the

00:52:25.320 --> 00:52:28.380
richest you always see that plethora of things

00:52:28.380 --> 00:52:32.730
that they are working with And then how fast

00:52:32.730 --> 00:52:34.690
do they build these systems to learn and fail

00:52:34.690 --> 00:52:37.949
and learn again and be okay with failure? Often

00:52:37.949 --> 00:52:39.949
what happens in India is people are not okay

00:52:39.949 --> 00:52:42.690
with failure, right? Even when you hear the traditional

00:52:42.690 --> 00:52:45.010
talk on careers, they'll say, you know, engineering,

00:52:45.150 --> 00:52:51.190
everyone is like, and then you die, right? Like

00:52:51.190 --> 00:52:53.190
it's, it's just well planned out all the way

00:52:53.190 --> 00:52:55.449
till we go. And after death. Because you're gone,

00:52:55.449 --> 00:52:57.809
we will plan a certain X things you do for you

00:52:57.809 --> 00:52:59.849
after that. So we've got it planned to even an

00:52:59.849 --> 00:53:02.769
afterlife level right now, right? But how do

00:53:02.769 --> 00:53:05.349
you think that, what I learned from the rich

00:53:05.349 --> 00:53:07.750
is how do you perceive something to grow at a

00:53:07.750 --> 00:53:11.170
scale? I think that in India is definitely missing.

00:53:11.269 --> 00:53:13.929
How do you start planning for scale? I think

00:53:13.929 --> 00:53:16.289
that's the, you know, that's something I've really

00:53:16.289 --> 00:53:19.969
picked up in terms of, you know, we may not exactly

00:53:19.969 --> 00:53:21.889
hit our listening mark on that day and I'm okay

00:53:21.889 --> 00:53:25.360
with it. But if we can envision that every person

00:53:25.360 --> 00:53:27.780
in Impact Tree is thinking that every day and

00:53:27.780 --> 00:53:29.900
we all are committed to listing, it will happen

00:53:29.900 --> 00:53:32.559
at some point of time. It's just a question about

00:53:32.559 --> 00:53:35.940
envisioning that at a scale and working towards

00:53:35.940 --> 00:53:38.440
it and then hoping that the stacks will add up,

00:53:38.460 --> 00:53:41.179
right? So I think that is a skill I definitely

00:53:41.179 --> 00:53:43.059
learned in DevOps because when you walk through

00:53:43.059 --> 00:53:45.019
the streets, you realize the planning people

00:53:45.019 --> 00:53:47.860
put into that vision and that's why it works.

00:53:48.599 --> 00:53:52.329
Wonderful. So, closing thoughts, Rajesh, because,

00:53:52.510 --> 00:53:55.309
you know, I don't think we covered all the ground,

00:53:55.409 --> 00:53:58.769
but I think we've touched upon some of the things

00:53:58.769 --> 00:54:01.190
that really make you unique and how to look at

00:54:01.190 --> 00:54:04.110
a career from a point of view that is not the

00:54:04.110 --> 00:54:06.489
conventional one. Even though data analytics,

00:54:06.630 --> 00:54:09.769
like you said, is a very hot thing right now,

00:54:09.849 --> 00:54:12.929
but you've really shown why, what is it that

00:54:12.929 --> 00:54:15.070
you can bring about and what is the change you

00:54:15.070 --> 00:54:19.010
can bring. So, any closing thoughts and then...

00:54:19.559 --> 00:54:22.980
yeah so closing thoughts would i would i i would

00:54:22.980 --> 00:54:25.000
say for those who are tuning in who are really

00:54:25.000 --> 00:54:27.519
thinking what would be you know your careers

00:54:27.519 --> 00:54:30.239
i think the most important thing i learned is

00:54:30.239 --> 00:54:32.940
and i'll be honest so i started as an economic

00:54:32.940 --> 00:54:35.840
strat then i did my cs i did my law then i did

00:54:35.840 --> 00:54:38.699
my mpa and today i'm an entrepreneur in data

00:54:38.699 --> 00:54:41.559
right so there's no connection whatsoever to

00:54:41.559 --> 00:54:43.920
where i started that's the reality of what it

00:54:43.920 --> 00:54:46.900
is But what I've always learned in life is be

00:54:46.900 --> 00:54:49.159
open to the change. So embrace it. As you're

00:54:49.159 --> 00:54:51.039
changing, you may not know where it's heading.

00:54:51.239 --> 00:54:53.539
There is a purpose it will add up in the end.

00:54:53.639 --> 00:54:56.340
Where it adds up for me is that as a lawyer,

00:54:56.420 --> 00:54:59.039
I can today speed range huge amount of data,

00:54:59.119 --> 00:55:02.260
right? The skill I learned as a lawyer works

00:55:02.260 --> 00:55:04.579
for me as a data analyst today, right? Because

00:55:04.579 --> 00:55:08.039
I'm able to read 60, 70 pages in a matter of

00:55:08.039 --> 00:55:09.880
like, you know, less than 20 minutes. It's a

00:55:09.880 --> 00:55:12.119
skill I know it's very hard to translate to others,

00:55:12.179 --> 00:55:15.079
but it's something I'm trained in. Right. And

00:55:15.079 --> 00:55:17.139
causation is something I picked up as a lawyer.

00:55:17.320 --> 00:55:19.440
What is the cause? What is the cause? So that

00:55:19.440 --> 00:55:21.940
depth keeping in the larger perspective is a

00:55:21.940 --> 00:55:24.139
skill you learn. So understand that whatever

00:55:24.139 --> 00:55:27.119
career, whatever education you're picking up

00:55:27.119 --> 00:55:29.619
today, there are certain skills that you're picking

00:55:29.619 --> 00:55:32.760
up. And use those skills to the way that you're

00:55:32.760 --> 00:55:36.159
able to figure your career. We get enough people.

00:55:36.219 --> 00:55:38.219
My other partner, Shesha, is a chartered accountant.

00:55:38.619 --> 00:55:40.619
Today, she heads our operations when it comes

00:55:40.619 --> 00:55:43.539
to data analytics. There is no correlation to

00:55:43.539 --> 00:55:45.599
where she started with. But what is important

00:55:45.599 --> 00:55:48.320
is she went behind her passion and was willing

00:55:48.320 --> 00:55:50.900
to make the sacrifices it took to find an end

00:55:50.900 --> 00:55:53.260
to it. And she's still, you know, in the journey

00:55:53.260 --> 00:55:56.159
to find. So I would say that start looking at

00:55:56.159 --> 00:55:59.440
that and start speaking. That's the only way

00:55:59.440 --> 00:56:01.619
you will be able to figure out where you're heading.

00:56:01.800 --> 00:56:03.920
Because I often start seeing that when someone

00:56:03.920 --> 00:56:06.780
takes career, first thing, Google it. Next thing,

00:56:06.840 --> 00:56:08.780
read to a bunch of people who have given YouTube

00:56:08.780 --> 00:56:11.809
talks. Last thing, make a list to go and, you

00:56:11.809 --> 00:56:13.769
know, tell this to friends and make an informed

00:56:13.769 --> 00:56:16.170
decision based on this limited scope of not even

00:56:16.170 --> 00:56:18.530
going and speaking to a person who's living in

00:56:18.530 --> 00:56:21.969
that career. I think that bridge students should

00:56:21.969 --> 00:56:25.170
do. If you're thinking of a career, find a person

00:56:25.170 --> 00:56:27.550
who's working in that career, go and talk to

00:56:27.550 --> 00:56:29.710
him and understand what does his career entail.

00:56:29.789 --> 00:56:32.550
Because without doing that crucial step, you've

00:56:32.550 --> 00:56:34.650
got all the secondary data, right? But it may

00:56:34.650 --> 00:56:40.280
not be enough. Wow. On that note, I think which

00:56:40.280 --> 00:56:43.960
is you brought it full circle, started with where

00:56:43.960 --> 00:56:47.579
you were and ended it with how data can be an

00:56:47.579 --> 00:56:50.380
insight. Thank you, Rajashree, for coming on

00:56:50.380 --> 00:56:52.960
the show. And here's wishing Impact Tree the

00:56:52.960 --> 00:56:56.260
very best in the future. Thanks so much. Thank

00:56:56.260 --> 00:57:00.099
you. That was Rajashree Sai. And she told us

00:57:00.099 --> 00:57:04.260
about how impact can really be created through

00:57:04.260 --> 00:57:08.300
data once you have the insights. which is the

00:57:08.300 --> 00:57:12.059
important part. So next time, another guest,

00:57:12.340 --> 00:57:16.400
another show, another interesting career. Thank

00:57:16.400 --> 00:57:20.000
you for joining us. And here's wishing you the

00:57:20.000 --> 00:57:21.380
very best in future.