Summary
In this episode of Peoplebox Analytics Talk, Abhinav interviews Krishna Raghavan, former Chief People Officer of Flipkart, about the intersection of data, technology, and people in HR. Krishna shares his unconventional journey from software engineer to HR leader and discusses the importance of data-driven decision-making in HR. He highlights the evolution of people analytics and the role it plays in solving business problems. Krishna provides real examples of how people analytics can be used to predict attrition, improve candidate experience, and drive employee engagement. He also addresses common myths about people analytics and offers advice for HR leaders looking to build a data-driven culture.
Key Takeaways
- Data-driven decision-making is crucial in HR and can lead to better business outcomes.
- People analytics should be a strategic consulting arm of the company, not just an HR function.
- Investing in data education and producing noticeable wins can help gain credibility for people analytics.
- Data democratization is important, allowing everyone in the company to access and use people analytics.
- Data privacy and access control are essential to ensure employee data is not used for surveillance.
- Starting small and focusing on concrete use cases can help build a business case for investing in people analytics.
- People analytics can enhance fairness, transparency, and accountability in the company.
- Overcoming data fragmentation and ensuring data comprehensiveness and completeness are ongoing challenges in people analytics.
- Resistance to data-driven HR can be overcome by demonstrating the efficacy and value of data in decision-making.
- Data analytics can make HR leaders more effective and help them drive better people outcomes.
Full Transcript
Abhinav (00:00)
When was the last time you met with a Chief People Officer of a $40 billion company only to find out that before taking on this coveted role, he has never spent a single day in HR. When was the last time you witnessed the ascent of a senior VP of engineering to the position of chief people officer
at the largest e -commerce company in Asia. Sounds intriguing? Buckle up. Hi, everyone. I’m Abhinav, co -founder of Peoplebox, and I’m super excited to kickstart the first episode of Peoplebox Analytics Talk, where we invite trailblazing leaders to delve into the fascinating intersection of data, technology, and people. And to make it even more special, I’m delighted to welcome our first guest, Krishna Raghavan. Krishna, in his last role, donned the hat of
Chief People Officer of Walmart owned Flipkart, And unlike most HR heads, his journey has been nothing but a barrier breaking one. Welcome to the show, Krishna.
Krishna Raghavan (00:54)
Thank you so much. It’s a pleasure to be here.
Abhinav (00:56)
Krishna, you started your career as a software engineer, worked in tech giants like Yahoo, Oracle, became CTO of ClearTrip, then joined Flipkart as the SVP engineering, and then became Chief People Officer, a path barely taken. One question that probably would be top of everyone’s mind in our audience is that when you were young, did you ever imagine that one day you would be at Peoplebox Analytics Talk talking to us?
Krishna Raghavan (01:21)
Definitely not Abhinav.
Abhinav (01:24)
but jokes apart, it’s truly our honor, Krishna, to have you here. So let’s dive right into this. Talk to our audience about how did you end up snagging the top HR spot at Asia’s largest startup after a super impressive engineering career.
Krishna Raghavan (01:38)
Yeah, the story is an interesting long one, but I’ll try to keep it short so that I don’t bore our listeners. But I think the journey started way back before I can even realize that it was happening to me, which is I always, I think gravitated towards problems that involved people, culture, teams and building them to scale. Right.
And even my prowess as an engineering leader was always within that space. I didn’t realize that as much until probably much later in my career. And that’s when I went through a sort of a life transformation where I did a program. You can call it midlife crisis or whatever, but I did a program and I sort of discovered where my superpowers lie and where I should spend most of my energies in the coming days, weeks and months. Right. And the answer was.
extremely evident to me in front of me which was go and try to take on a role where I could do talent, culture, building at scale not just within my function within engineering. So that’s when you know lot of things came in place and the opportunity opened up at Flipkart applied for it and little did I know in about a month’s time I got approved for the post and I got there and
and I couldn’t have imagined in my wildest of dreams that the COVID pandemic would follow in a couple of months, but rest is history. But that’s really been my journey to get on to this particular role.
Abhinav (03:09)
That is amazing.
getting into the position of Chief People Officer of the largest startup and then COVID hit, which probably nobody prepared anyone for. And so before we go to the COVID one, I’m really interested to know what was everyone’s reaction? You know, your peers, the HR team, you know, getting somebody as heading the people function who has never got a single paycheck.
which writes title HR.
Krishna Raghavan (03:38)
there are lots of people that tell you, are you freaking crazy? That’s the question I got many a times. Some of my peers in engineering in particular, they said that you’re actually taking a disastrous move and you shouldn’t be doing this. You should be staying in technology. That’s where I think most of your promise lies. And,
Some of my well -wishers were obviously backing me, but I think there were lots of naysayers along the way. And I would say that even within the HR team, there was a lot of, I would say, suspicion of what to expect an engineering guy coming into the post of heading HR.
Abhinav (04:16)
let’s talk about engineering, you know, Krishna, you are an engineer at heart. I started my career as a software engineer. there’s one thing that I’m very sure about that. They love data.
And our audience must be very curious to know How did you leverage the engineering mindset and that data -driven culture in making the right people decisions?
Krishna Raghavan (04:35)
No, it’s a very important question Abhinav. I think one of the first things that I sort of brought into the role and many times I’ve been asked this question. What are the things that you actually brought into the role and what are the things that you actually jettisoned by coming into the role because these are completely different roles. But one of the things that I brought in was this data orientation or the mindset of looking at data for everything, right? And I’d like to start with basics. I think as I entered the function,
The most important thing became the question became what to even measure Abhinav because you know, often times there’s a lot of data out there and companies pride themselves on putting together 40 metrics on a spreadsheet and everybody’s pouring over those metrics. But actually do you need to look at 40 metrics to make decisions? That’s the first question to ask. So it became my, my sort of initial focus became.
You know what, what are we here to do from a people function perspective? How is that in alignment with our overall business strategy? And then go to define the metrics and the metrics, which were important. We had to actually come up with them. In some cases, the instrumentation was also not even there for the metric. And you had to put the instrumentation together. In some cases, the metric was already there. And in the other cases, wherever there is noise, where there were extraneous metrics, we actually just kind of, you know,
remove them from all the dashboards. So that became my like initial focus as I came in.
Abhinav (06:06)
But defining the most important metrics or OKR is one thing, but then getting the data, especially for such a large company, how hard was that?
Krishna Raghavan (06:14)
Very hard. Like some of the metrics were obviously there, instrumented like I said earlier Abhinav. But in some cases, there was a fair bit of data fragmentation and I’m sure we’ll speak to it at some point later in our conversation. But you know, disparate systems, systems used for different use cases. But when you actually look at a metric, the metric is actually a blended metric. It’s an output metric of many input metrics. But these input metrics are actually present in different systems.
So how do you actually get them together? In some cases, the systems themselves were not even talking to one another. So it became plumbing, like what I call data plumbing, which is like, okay, you know what? I want to instrument this metric, but the heck I can’t even figure out how to do it. Let me actually now put my head around this problem itself. Let’s instrument first. Let’s build a data pipeline. That became the first order problem. And I started to solve some of the instrumentation problems.
Abhinav (07:08)
I love the phrase data plumbing. I’m probably going to use it in one of the pitches we use. But coming back to the whole uses of data or say the whole function of people analytics, you know, for most of the companies that I speak with, it starts with hiring a reporting or a people analyst, you know, who would help creating reports primarily for the leadership or the HRBP. Is people analytics more than just creating reports? And if it is, what all does it entail?
Krishna Raghavan (07:36)
Yeah, there’s actually a pretty good paper on this Abhinav and I would urge our listeners to look it up. There’s actually a Deloitte study on a people analytics maturity model. And, you know, there are stages of evolution of how they look at people analytics. Obviously, many of these consulting firms do this, you know, as their primary gig, right? But I think just to summarize, initially, when you build a people analytics function, it tends to be
a data provider function like you aptly described it, which is, you know what data is not my problem as an HR functionary. It’s the people analytics is problem. Whenever I want some data, I send a request. I get data back. Neither does people analytics as a function know why I requested to this data in the first place, but they become more data service providers. Right? That is like, I would say.
you know, point zero on the scale of zero to 10 in terms of people analytics maturity. And I won’t elaborate on the entire evolution path evolutionary path Abhinav, but at stage 10, it’s almost like people analytics is like a strategic consulting arm of the company, not the HR function. You know, like the CEO says, you know what? I need to figure out.
Where do I need to invest in terms of my best talent in the company and what are the skill sets I need to build in though in that talent. Now that’s a very fuzzy question when you ask that at a scale of an organization that could be Flipkart size. It’s almost like people analytics has to anticipate that problem and like a strategic consultant going in tell the CEO this is what I think this is the decision or set of decisions or recommendations I can actually give you.
And in some cases, even short circuit and say out of these set of recommendations, by the way, this is the one I would pick. And the CEO has, you know, the ultimate veto choice to make that decision. But it’s almost like moving from data provider to decisioning for the company, not just the HR function. That’s what I see the entire evolutionary curve to be for people analytics.
Abhinav (09:48)
That’s super interesting. And you mentioned about the report by Deloitte. I actually absolutely love that report and I highly recommend everybody to, you know, all HR leaders to go through that report. Actually it’s authored by a very good friend who is the partner at Deloitte named Nitin Razdan So it’s a fascinating report. Coming back to the usage of people analytics, you know, I think what was every report and talk about that, how useful it is, but
Krishna can you give some real examples of how you use people analytics to achieve some real business objectives?
Krishna Raghavan (10:23)
Yeah, absolutely. I think there’s the holy grail that most companies want to get to, which is the churn prediction model that we called it in Flipkart or the attrition prediction model. We built a model with all the data that we had. This was after I think at least year three of the people analytics journey. You know, we are, we’ve kind of moved along in terms of the maturity curve and we have the data instrumentation in place and all of that.
Abhinav (10:31)
Yes.
Krishna Raghavan (10:51)
It actually turned out to be from a precision perspective. It actually was pretty accurate. Okay. In, in terms of percentages, I think we were able to get to 80 -85 % precision. we were able to employ this in particular teams in the company. Right. and we were able to give this data not just to HRBPs, but actually the line managers, and empower them to actually have these.
conversations with some of their employees that could be on the high risk prediction. So that’s one very, very strong use case. Second Abhinav is I think a lot of companies out there pride themselves on being a top destination for talent. But do you measure candidate experience through the funnel of hiring? And in Flipkart, one of the things that we realized when I was working with the team is that
You know, candidates that got accepted their candidate NPS scores was very good, but the candidates who got dropped at some point in the process because there was probably not a fitment their NPS scores was very less. Now you could argue and say, you know what? I don’t care about the candidates that got dropped, but that does not define a great company because end of the day, your promoters are the ones who also interviewed with you and won’t say, you know what?
I didn’t get through, but I had a great experience through the process. Now the people analytics team was able to give me this data that led to say, if I look at rejected versus accepted, my NPS obviously differs. And these, this led to a series of interventions to improve the experience for rejected candidates as well. Right. That’s my second use case. And the third is we moved away from this annual survey business, which is
once a year, I’ll check employee voice and I will take a set of actions. We moved away and we said we are going to do continuous listening and we are going to actually have a mood score and we’re going to ask you how you feel at a particular day at multiple points in time through a particular week through a particular month. As this data matured, what we found out, this is probably probably common -sensical is that there is a very strong data correlation between mood score.
as the leading factor for attrition. So how do you take action early on from an employee life cycle perspective? Because often companies talk about employee retention. I actually kind of hate that phrase. It’s almost very negative. It’s like you want to go, but I’m somehow trying to hold you back. But is there a reason to stay in the first place? And can I actually engage with you when you are starting to disengage and you’re showing signals of disengagement?
So flip the problem on its head and I think this was one of the biggest shifts that people analytics actually helped us make in the company. So these are two, three use cases that I wanted to talk about.
Abhinav (13:44)
Krishna, the way I look at Flipkart and pardon me if I use the wrong phrase, but.
I look at Flipkart as like the Amitabh Bachchan of business world, you know, the pioneer in setting innovation that everybody look upto And the reason I use the word business and not startup is because even the larger publicly listed companies want to learn from Flipkart. And I believe that the use of data and people analytics must not be an exception here. So give our audience some important learnings from Flipkart, people analytics culture that they can today go and leverage in their business.
Krishna Raghavan (13:52)
Ha!
Yeah, I think it’s a very important question and I wouldn’t say, you know, it’s just people analytics. I think data as a common theme across the company, the data oriented mindset is extremely deep across Flipkart. I think that is something which goes across not just HR, but every function out there. I would probably put across certain big learnings that we’ve had through the, through the journey, right? And in particular, people analytics.
The moment people analytics stays within the domain of HR, you’ve lost the plot. It is not just a HR function or a department that you need to set up in HR. The way you need to think about it is in the business realm, you often have an analytics organization, right? And this analytic organization typically is a horizontal that goes across the company. Initially,
is a data provider then becomes insight provider then starts to actually even recommend and make decisions on behalf of the top management in the company for everything that’s people anybody in the company should be able to access people analytics. So if it remains within the domain of HR, you have lost more than 80 % of the vision of where people analytics can get to. That’s probably the biggest takeaway.
The second I would say is invest a lot of time in data education and I cannot overemphasize this enough even within HR at least I find that the level of data proficiency is probably not where it should be because in today’s day and age there is an explosion of data.
and even within the people realm there will be an explosion of data. The skill actually lies in asking the right questions, connecting the business problem to what we are solving for and asking those pertinent relevant questions and then using the power of data to reveal answers out to you. So if you can’t ask the right questions, you will be barking down the wrong tree many a times. So
Data education is my second biggest takeaway and this has to happen across the board including within HR. Right? And third is produce some very noticeable wins in the company to gain credibility of the function. It should not be something like it’s a pipe dream in year two, year three, by the way, this is the roadmap we have on people analytics and this is what we’re going to deliver. It can’t be that.
Most companies in today’s day and age need answers yesterday, not today. And how do you actually have that business acumen, that urgency and agility in your operation to be able to land some very strong, successful outcomes early on will really establish the credibility of what people analytics can deliver for the company. These are three big takeaways for me.
Abhinav (17:22)
Krishna, I’m so happy that you spoke about the first one, which is data democratization. Because whenever I speak with HR One of the top wishlist for them is the, the ability to provide everybody in the company, even the employees, you know, the power of data. However, the major roadblock
is fragmented employees data in different tools and sheets. And like you also mentioned, right? Some data is in ATS and others on HRIS. ESOP data is sitting on another tool, performance and engagement somewhere else. And there is then tons of data on spreadsheets. How did you overcome that for a company of the size of Flipkart, which has tens of thousands of employees and I’m sure no dearth of tools and sheets.
Krishna Raghavan (18:03)
Yeah, I think if you ask anybody out there on a joking note, Everybody says that this software is the best for this use case and nobody obviously wants to adopt one common platform for all use cases. So what you land up doing is obviously buying lots of different products and services leading to fragmentation and everybody proposes promises the moon when you buy them. But, after that, you realize the actual truth, right? The harshness of.
data fragmentation. So I would say that this was a journey for us, Abhinav, and it’s actually still ongoing. I would say to that extent until very recently, you know. So the way we actually did it is, like I said earlier, we spent a lot of time analyzing what data we want, where does this data reside, and spent time in actually putting together the entire instrumentation pipeline for it.
So we had to build data pipelines across all our systems and all of them flowing into one common data warehouse. Then once we build, you know, the people domain model, the people domain model as in how should we represent an employee, right? The entire data model for us, if you think about it, there are multiple relationships between an employee, a manager, an employee and a skip manager, right?
How do you define the persona of a director and so forth? You define the entire people domain model and the instrumentation pipeline for it. And then what you do is you actually land up building adequate visualization for it because the power of data cannot be revealed, so to speak, or cannot be shown in its all glory unless you have great visualization. So in our case, we also had to pick a platform or a product to visualize.
And then the education thereafter followed. So that’s the journey we’ve kind of been on Abhinav to make sure it’s not been easy at all for sure.
Abhinav (20:01)
I can absolutely imagine it’s not easy. It must not be easy because one thing that we didn’t talk about, is the data sanity. a lot of time data is not in a consumable format. I was talking to somebody and say, you might wonder that it’s so easy to find out the last CTC of an employee, like from a previous company.
And you’d be surprised it’s not because it’s sitting, it’s sitting in notes. Uh, and those are like tens of hundreds of notes. So was that a problem that you also encountered about, you know, cleaning up the data and make it in a consumable or probably a quantifiable way. And then of course, you know, put it into your data pipeline.
Krishna Raghavan (20:41)
Absolutely Abhinav, I would say the two big things that you always think about when you deal with data is you think about data comprehensiveness. Do you have all the data in the first place? Then the degree of data completeness. They are very different by the way, like comprehensive means do you have all the data? Completeness is more the aspect of accuracy. So it’s not enough by the way that even if you…
Take your example, even if you got the CTC of the last employment into a system where you can consume this data, unless you actually refresh this data for future joiners that come in, the data becomes incomplete. So you also need to ensure completeness, not just comprehensiveness, right? So it’s a big problem. And in some cases, frankly, the data is so offline.
it takes a lot of effort to just bring it online. Many of the teams don’t even record this data.
Abhinav (21:38)
Now, Krishna, a bit of a controversial question. You are this round peg in a square hole, bringing this engineering and data driven mindset. Did you get any resistance? Generally, not a lot of companies rely on data when it comes to making people or HR initiatives. How was that going through the journey and was there any resistance?
Krishna Raghavan (22:04)
No, no, I think I would be lying if I said there’s no resistance. There was definitely resistance. Different forms of resistance, right? Sometimes you face resistance when you make a large change, passively or actively, correct? So the active pieces in places like learning and development where you need to define efficacy of your interventions. Sometimes learning and development will say, you know what, we have great participation rates, we have good satisfaction scores. Isn’t that enough?
Why do we need to measure quantifiable business outcomes of our learning intervention? That’s more like active resistance because the question is why? Why do we even need to do that? The passive resistance comes into places where people are not data aware enough and they believe in somebody else’s job to collect the data and take those decisions or they say these decisions are very intuitive. We actually take them based on intuitive thinking. Right? Why do we need to actually bring data into the equation for everything?
So there you face some degree of passive resistance as well. And what you need to keep constantly doing Abhinav is obviously one as a leader, you role model to you actually make sure that you keep communicating the efficacy of data and how it could lead to better decisioning across the HR function and the company itself. So that’s why I said earlier, producing some wins early is going to be important because talking in theory,
is one thing but actually in practice producing some wins and real examples is much more powerful.
Abhinav (23:36)
I love what you said that you need to be the role model to use anything, or I think to drive any change. But Krishna, tell me honestly, you’ve been an engineer, you’ve been an HR head, and obviously in both of the roles you’ve used data extensively. Do you genuinely believe that the usage of data makes someone a better HR leader or a better HR business partner?
Krishna Raghavan (23:59)
I mean, there’s, you know, it’s an emphatic. Yes, I’ve enough. It definitely makes you there’s no doubt in my mind about this. I mean, everybody would obviously say the answer. Yes to this one. But the degree to which it really helps you become a better leader. I think across the board and actually why only focus the point on HR, HR obviously yes. But the way you lead teams in companies today.
Gone are the days where you can just be very intuitive only as a leader and say, you know what, I think this person’s good. This person’s probably not scaling up enough. You have to now move to an era where you can use data to actually really power your decisions. And let me actually talk a little bit about just one small example. I think what we saw as the biggest transformational change in Flipkart.
is when we started to actually bring the power of data to business leaders around people, we defined what we call is a people dashboard. And we said to a business leader, you know what, you look at business, you look at top line, you look at bottom line, you look at all of this. What if I gave you a people dashboard in conjunction with your business dashboard?
And you look at it also to make decisions for your function. How would that look to you? And tomorrow, you know what? Both the CEO and me are going to hold you accountable. Like we hold you accountable for business outcomes, we are going to hold you accountable to those people outcomes as well. So if your attrition spikes, if your diversity doesn’t get to the target that we want you to get to, we are going to hold you accountable. It changed the game It helped leaders become.
better people leaders. At the same time, it drove the people agenda is not just an HR agenda, but it now becomes a company enterprise agenda. And that’s a very, very powerful shift. And this could not have happened without people analytics.
Abhinav (26:07)
Coming to the more challenges, Krishna, and I know you must, of course, be much more aware about them than I do, is the sad reality of HR world that they don’t get high budgets like a tech or sales or marketing would get. So how would you suggest to HR leaders building a business case for their CEOs or business leaders to invest more in people data or people analytics?
Krishna Raghavan (26:33)
Yeah, I think it’s a very, very, very important question Abhinav. I think that’s where probably most of the companies do not have adequate resources to invest in this particular area. I would actually start with focusing on two or three very concrete use cases for the business. Like it’s almost like when we think about building products for a set of consumers Abhinav.
We always think about product market fit, right? I would actually kind of think about it internally as a CHRO or an HR functionary in similar vein, which is we know it’s important, but how do we actually bring these stakeholders onto the table? The CEO to sponsor the investment in this area. Let’s take a big hairy problem that’s facing the company right now. And actually evidence.
how it can actually be solved elegantly through people analytics leading to direct better business outcomes. It could be around staffing. It could be around talent development. It could be around attrition, right? And you could actually quantify a before and after as well saying that this is what I implemented in a particular function. And so therefore you could have a control group established as well. So I would take this approach.
established two, three concrete use cases. And that I think will be a lot more powerful to sell to your most important stakeholders within the company.
Abhinav (28:04)
Do you see HR and people function in general
is now becoming more and more data driven and did the whole pandemic and remote and gig worker had anything to do with that? Was there a trigger? Was that a driver or is it still the same how it was 10 years ago?
Krishna Raghavan (28:20)
No, certainly not the same. Abhinav I think it is definitely improving as we speak. I think people have gotten a lot more data aware HR functions across the board. I think you have to realize that earlier data was more seen as, you know what I need to get data for a particular use case more as a service provider as I described it earlier. now looking at
How can data power decisioning is something which is dawning upon, I think, HR functions across the board. But they are grappling with data fragmentation as a real problem. So the awareness, the intent is there But now the problem is when rubber hits the road, how do I actually get there?
Abhinav (29:07)
Yeah, absolutely. So I want to now move to our last sort of a section or a round, which is about breaking the myths and as you speak to a lot of, you know, business people, HR people, HRBPs about the people analytics I see a lot of myths and I want to make quick answers from you to our audience about, you know, how do you, how do you react to these myths? So one of the first one that we hear very often is about you need to be
have deep data analytics expertise to use people analytics in the company.
Krishna Raghavan (29:40)
The answer is absolutely not. It all depends on the power of the tooling and the product that you actually employ to solve this. And products have evolved to such an extent where it’s about just a set of clicks. And like I said earlier in my conversation, it’s about asking those right questions. You can get the data that you actually want on your fingertips. So you don’t need to be a data scientist, a data analyst to actually use data.
Abhinav (30:06)
And the second one we hear is that People analytics reduces people just to their data and take the human element out of it.
Krishna Raghavan (30:14)
Actually, it’s more the opposite, right? Which is, I think of it as sometimes, particularly in the people realm, we use our own hidden unconscious biases to drive people decisions. And actually data bust those biases, right? Like, you know, one of the most common things is,
people who are not seen in the pandemic, they are probably not doing as much work and they don’t deserve to get promoted. You know, this could be a huge bias that could actually play out both consciously and unconsciously. But if data was there to our rescue, actually it would even make the company a fairer place to work in where these biases actually do not rule. So if you think about it, data can actually be your
I would say your biggest lieutenant, so to speak, as a leader, your biggest supporter to ensure fairness and transparency in a company.
Abhinav (31:18)
Very well said, Krishna. Another one. People analytics facilitate employee surveillance.
Krishna Raghavan (31:23)
Absolutely not. I think as long as you have standard, very, very strong data privacy rules around employee data, and you have very strong access control, determining who actually can view pieces of data, I think you’re in very, very safe hands. It’s not a surveillance tool at all.
Abhinav (31:44)
I couldn’t agree more. And the last one is to start people analytics, data must be perfect.
Krishna Raghavan (31:49)
Not at all. I think it’s a journey. The data completeness, comprehensiveness journey that I talked about is a journey. You don’t need to be perfect on day one. Start somewhere, start small, establish those wins and continue on your journey. Because what will propel you on your journey is progress, not stagnation.
Abhinav (32:08)
This is really, really helpful, Krishna. Krishna, you have moved on from Flipkart now. 31st December was your last date. And our audience will be very curious to know that after such a path -breaking career, what’s next for Krishna?
Krishna Raghavan (32:23)
Yeah, I’ve been still, you know, sort of thinking through, you know, and scouting for opportunities that I think will really interest me. My heart has always been in the realm of startups and I want to see how I can contribute to one or many. And that’s sort of where I’m sort of focusing my energies on in the coming days, weeks and months. I’ll definitely keep everyone posted, you know, what I’m up to next.
Abhinav (32:52)
I can’t thank you enough, Krishna, for this super insightful session. I enjoyed every bit of it, learned a lot, and I’m sure our audience will have so many insights and so many learnings to take from this talk. Just thank you so much and wish you all the very, very best for the next step in your career.
Krishna Raghavan (33:10)
Thank you so much Abhinav. It was a pleasure talking to you. Very, very insightful questions and thank you for a wonderful conversation.
