How strategy leaders can achieve their moonshot goals

Summary

In this conversation, Abhinav and Dan Lapporte discuss the intersection of data, AI, and people in the field of People Analytics. Dan shares his journey in people analytics, starting at Charles Schwab, and emphasizes the importance of connecting HR data to business impact. They discuss the evolution of people analytics over the years and its increasing ubiquity in organizations. Dan highlights the need for HR to be more data-driven and the challenges in bridging the gap between HR and other departments. They also explore the role of AI in people analytics and the potential for democratizing data and information. Finally, they debunk myths about people analytics, such as the need to be an expert data analyst and the belief that people data and business data should work in silos.

Key Takeaways

• Connecting HR data to business impact is crucial in people analytics.
• People analytics has become more ubiquitous and is expected to deliver insights and value.
• HR needs to be more data-driven and bridge the gap with other departments.
• AI can democratize data and provide natural language processing for easier access to insights.
• People analytics can elevate top performers and help businesses succeed with all employees.
• People data is business data and should not work in silos.
• Data does not need to be perfect to start with people analytics.

Full Transcript

Abhinav (00:02)
Often when we talk about People Analytics, it’s always about HR metrics, like headcount, attrition, or hiring. But how do these numbers actually connect to real business impact? What happens when we mix people and business analytics? And most importantly, how can we use this combo to help everyone in the company aim higher and reach bigger goals? Hi everyone, I’m Abhinav, and welcome to the Peoplebox Analytics Talk.

where we invite remarkable leaders to go deep into the fascinating intersection of data, AI and people. And today, I am thrilled to introduce Dan Lapporte. Dan has an impressive wealth of experience in people analytics, spending nearly two decades. His journey began at Charles Schwab, way back when people analytics went by the name of HR reporting. Over the years, he lent his experience to Fortune 500 companies like Chevron,

and Kaiser Permanente, and now heads people analytics at Lucid Motors, the luxury electric car giant based in Northern California. Welcome to the show, Dan.

Dan Lapporte (01:09)
Thank you, Abhinav Nice to be here.

Abhinav (01:11)
Dan, your journey in people analytics started by connecting HR data to business impact at Charles Schwab almost two decades ago. Could you share that story and why this piece is still so crucial?

Dan Lapporte (01:23)
Absolutely. When I was at Schwab, there was something of a war for talent among the brokerage companies, you know, Fidelity and Wells Fargo, there was something of a revolving door. And what I found is that when Charles Schwab lost top performers, we lost revenue, we lost income, we really struggled particularly if those employees went to a competitor. And so I began reporting on

top performer turnover, not just turnover, but top performer turnover. And I sent these reports out and they were just, you know, based in Excel. And one day out of the blue, I got a call from the CFO and the CFO said to me, you have my attention because it was so impactful when we lost those good people, those people who are bringing revenue or new assets to the organization.

And I just vividly remember it being a very powerful moment in my people analytics journey.

Abhinav (02:24)
Fantastic. And then that’s how a very, very impressive journey started.

And how is it different today, Dan? You know, it’s been almost 20 years with so many different companies, most of them Fortune 500. Today, when we look at the people analytics in the post-COVID world, is this any different?

Dan Lapporte (02:45)
It’s different because I think people analytics has become more of a thing. It’s ubiquitous. I think every organization either is thinking about or has a people analytics function and the expectation is that you are going to deliver insights and value. It’s no longer just about providing data. It’s about telling the story with data. So my example, a moment ago with

with Schwab is now table stakes. I think everybody should be reporting on attrition across performance levels.

Abhinav (03:24)
And in your experience, what’s really driving this shift? Why has it suddenly become so crucial and every company is actually looking at insights and not just tables and dashboards.

Dan Lapporte (03:35)
Yeah, Well, People are one of the most important or expensive items on the balance sheet. And businesses are typically in business to make money. I suppose if you’re a nonprofit, you’re not there to make money. But when you are able to convince people that, you know, our workforce is an incredibly expensive commodity or asset, and we’re not doing a very good job managing that asset.

people tend to sit up and take notice. And The analogy I like to use Abhinav is most companies have a better sense of how much money they’re spending on office furniture than they do on their people when you start to turn in, you know, include things like the cost of replacement or talent acquisition costs. Most businesses think of those as baked in costs, but they’re controllable and they’re manageable and they’re measurable.

Abhinav (04:33)
Could you walk us through a typical day or week in your role? As the head of people analytics at Lucid or in your previous companies

Dan Lapporte (04:42)
Sure, I would say my day is really mixed of three things. One is responding still to ad hoc requests. There’s always gonna be a need for a hiring rate or a turnover rate or maybe it’s around succession management. The second part is really strategically trying to engage with leaders, so taking the people function to the business and helping them understand what I mentioned before

about what cost people are and how they can perhaps run their business more efficiently with the right people and the right place, the right time at the right cost. And then the last piece of it is, you know, once you build a people analytics practice, it’s a matter of delivering on what you said you want to deliver. So that’s kind of the work of the work. If you’re going to be looking at attrition trends by, you know, gender.

it’s important to be able to provide kind of regular routine updates to people and what they might consider doing about things if they’re crossing some threshold that they’re concerned about. So those are the three areas I think people analytics plays in.

Abhinav (05:59)
And Dan, I’m sure in all these years that you have been into people analytics, some of the things might not have changed. For example, even two decades ago, the importance of attrition would be as high or at least would be critical as it is today. But walk us through, in these past 20 years, did you notice a shift in the type of people analytics projects that you tackled?

Dan Lapporte (06:24)
Absolutely. When I first started, it was miraculous to be able to calculate an attrition rate and provide some interpretation. Now there are AI chat bots or benchmarks that you can bring in and be able to give business leaders a sense of what does good look like? If our attrition rate is 15%, how does that compare to the industry? How does that compare to other functions within my business?

So it’s changed a lot and it’s become much more sophisticated and probably more valuable.

Abhinav (06:59)
It’s so interesting that you spoke about AI. You know, the world is like suddenly changing. It’s such a huge speed with the advent of AI. When it comes to people analytics, how do you see the best use of AI?

Dan Lapporte (07:15)
I think this idea of business leaders or HR business partners or any HR practitioner being able to ask natural language questions to a machine, right? This computer, what’s my current head count? How did my head count change year over year? Which organization is growing fastest within my business? Those sorts of basic business questions.

which typically come in over email and then you spin up an analysis and you send it off, can now be done automatically. And I would add, I think we’re kind of in the infancy there of bringing AI into people analytics. I think it’s gonna continue to grow and it’s a trend I’m actually looking forward to.

Abhinav (08:01)
Fantastic, we too, I think it’s a huge opportunity for companies like us to, you know, provide this power of analytics and insights in the hands of every manager, every leader, not just from people’s side, but also on the business side. And on that note, Dan, every time I speak with a leader or an HR head, everyone agrees that people inside should be aligned to business outcome. Yet, very few companies have been able to achieve that. You know?

And many people wonder that what are the specific challenges that companies face or especially you have faced in your journey to achieve this alignment.

Dan Lapporte (08:44)
People data is business data. The same way we need to account for people costs in our business, people data really needs to get up to the right hands. And a good specific example of that is when I worked at Schwab, it was right at the very beginning of the pandemic. And people were very concerned about frontline health care workers being unable to work.

And so one of the more interesting people analytics case studies was in concert with, you know, clinical practitioners and lots of different areas of the business look at how can we replace those people? You know, we’re not gonna be able to hire them because nobody’s gonna wanna be a respiratory therapist when you’re in the middle of a respiratory borne pandemic. But.

what we ended up doing was looking at different licensing requirements and then figure out if scenario A happens where we lose a lot of frontline employees, where can we find people who can still perform those roles within license and be able to meet patient needs? And again, I could talk for an entire podcast about what that effort involved, but that’s a good example of where data needed by the business.

was supported by employee information and people analytics.

Abhinav (10:08)
That’s actually a very interesting example. And I wonder that despite these amazing success stories, the first sort of destination for most of the business leaders to get any actionable insights is not people analytics. They go to their business analysts or the data analysts to try and find it. And most of the times that we have seen, the people data is not part of that. So I’m curious that…

There are many head of people analytics or head of people who always wonder that how do we bridge this gap between HR and other departments and how do we make business leaders excited about the people data and the actual impact of that on their business metrics as well.

Dan Lapporte (10:54)
Historically, when business KPIs became available, they were true business KPIs. They really were not related to people. And now you can’t find a business that isn’t looking at people metrics and people analytics. So it might be top performer attrition, it might be overall turnover rate, it might even be, you know, DEI metrics.

that are most important to a business. But that’s a way where the practice has matured, and as the practice has matured, it’s gotten me more visibility in regular business reporting.

Abhinav (11:27)
Interesting. And then in your experience, right, you have seen this in a large span of time. Do you see that HR is becoming more data driven today or is it almost always driven by the top, you know, who requires more people data and connected to the business data?

Dan Lapporte (11:46)
Ooh, that is a sticky question. So let me try to answer it with some political correctness. I think HR business partners are now expected by their leader or by their client groups to have some knowledge other than gut feel about talent. And I think HR business partners, hopefully I’m not offending my friends by saying we’ve sort of been dragged along as

data-driven decision-making has become more important across businesses, HR has been along through the journey. I will add that it’s not a natural talent for the HR function, and that’s why these tools, Peoplebox and others, are so important for organizations to invest in.

Because if you’re going to continue using technology from the 80s and 90s like Excel to try to generate decisions about people, you’re going to get into trouble very quickly. The questions will outpace the technology.

Abhinav (13:01)
Fantastic. And as the demand of both data as well as people analysts or the role of people analytics are becoming more and more in the companies, what skill and expertise do you believe, Dan, will be most valuable for the future people analytics professional in making sure that they are able to provide the right data and actionable insights to the business leader?

Dan Lapporte (13:29)
That’s a good question. And I think it’s a combination of sort of HR skills and knowing what matters about organizations and organizational design and business acumen. So I think the best people analytics practitioners probably come from other areas of the business. I didn’t start out like my career journey in human resources. In fact, 25 years ago, if you told me I was gonna be in HR, I would have chuckled. But.

I fell in love with the work because there’s such a power in being able to say, you know, all businesses talk about people as their most important asset. Well, then start treating it as an asset and effectively manage it and effectively understand it. And again, People Analytics is the pathway for that.

Abhinav (14:22)
Fantastic. And Any steps that you would recommend, Dan, for companies aiming to foster a really data-driven culture within their HR and the broader organization.

Dan Lapporte (14:38)
I have two or three that I think are vitally important. Number one, the messaging has to come from the top down. So if your Chief People Officer or VP of HR believes in data and being data driven, they need to reinforce that message so that the HR function follows their lead. I think the second one is, you know,

The only way to have a strong people analytics function is to guess what, invest in the people analytics function. So if you have an expectation of hiring someone and, you know, they’ll just jockey their way through Microsoft Excel or, or maybe Power BI even, and come up with their own formulas, that’s not a winning strategy. And then I think the third is being intensely focused on business problems. So when you’re coming up with.

people analytics metrics, focus on what it is the business is looking for. Is your business focused on growth? Is your business focused on controlling costs? Is your business focused on expansion? Maybe global expansion or something like that. And people analytics really does play a role in all of those things.

Abhinav (15:48)
Peace.

And it’s so interesting you said that I always start with a business objective. That’s exactly what we do when we help companies with OKRs. And one of the questions that I’m often asked is where to start?

Dan Lapporte (16:07)
I would say hiring a strong leader to make sure that you can sort of build a roadmap and a team. It’s not, you know, flipping a switch and you’ve got a people analytics function. You have to kind of grow from the ground up. If I was going to make a real recommendation, it would be look for someone who’s a star in finance or maybe a star in your business and help them.

to understand the importance of people and ask them to maybe take on as a stretch assignment, hey, we want you to develop this capability for us. And then, you know, that person doesn’t have to stay in that role forever, but getting a strong head start is vital.

Abhinav (16:54)
Very curious to know, Dan, in your long career and people analytics, what has been the most sort of mind-breaking or wow insight that you were able to provide

Dan Lapporte (17:10)
Yeah, that’s a really good question. And I’m going to go back to some of my work at Kaiser Permanente, which for people watching this internationally, or maybe outside of California for that matter, is one of the largest healthcare organizations in this country in the United States with more than 12 or 13 million members and a quarter of a million employees. And the question was,

Is there any correlation between what’s happening with our people and with Kaiser, it was specifically nurse managers. Is there any correlation with them leaving or their performance or maybe how they’re feeling about their job and patient outcomes? So we looked at business data from the quality side of the organization where we had hospital re-admittances or maybe even hospital

quality scores for patient satisfaction metrics, and then looked at nurse manager turnover. Because we know that when you have a lot of turnover in management, the employees under those managers tend to question, you know, well, why am I still here? My manager left or, you know, my manager moved on. Where’s my motivation? And we did that study and we… It wasn’t conclusive, I’ll be honest. But…

That’s the sort of thing where you can take very real business data, hospital quality scores, which lead to Medicare or Medicaid reimbursement rates, and people data, and marry the two together and see if you can find correlations. It’s fascinating work, actually.

Abhinav (18:57)
Fantastic. That’s fantastic.

Coming back to the wish on AI, Dan, you have seen now the introduction of AI and its capabilities.

How would you really like AI to transform the people analytics space?

Dan Lapporte (19:13)
I think bringing AI to the forefront of people analytics is really all about democratizing data and democratizing information. So if you build an AI model where security is in place, of course, because you’re dealing with sensitive data, but you give leaders the ability to simply ask a question or type a question and get an answer, that aspect of AI, that sort of natural language processing and…

machine learning and all those other components of AI, I think really will bring a sea change to how workforce data is democratized into business data.

Abhinav (19:54)
Fantastic. We now move to the last section of our podcast, which is my favorite, which is breaking the myths on people analytics. So I would share a myth, and I would love to hear your response on that. So the myth number one that we hear very often, you need to be an expert data analyst to use people analytics in a company.

Dan Lapporte (20:18)
I think what you really need is good cross-functional connections. You need connections with IT, you need connections with obviously HR, you need connections with the business. And if you know the right questions to ask, you can certainly find people or tools to build the answers for those questions.

Abhinav (20:39)
I couldn’t agree more. Myth number two, people analytics reduces people just to their data.

Dan Lapporte (20:47)
debunked. I think people analytics can elevate top performers. I think people analytics can help businesses succeed with all of their employees. So I think people analytics opens doors not close them.

Abhinav (21:05)
Fantastic. And the myth number three is that people data and business data need to be working silo. They don’t really need to be connected to bring an image.

Dan Lapporte (21:16)
Debunked. People data is business data. Again, I hate to sound like a broken record on this, Abhinav but most organizations spend a lot of money on their people between salaries and benefits and occupancy and provisioning. But we don’t do a very good job managing that resource. And I think the secret to…

for lack of a better word, selling a people analytics capability for a business is making that case and being able to present that business case. And there are lots of different ways to do it.

Abhinav (21:58)
And the last myth, which is we often hear, and it’s probably also one of the biggest blocker, is to start people analytics, data must be perfect.

Dan Lapporte (22:10)
No, you should never let the perfect get in the way of the good. So it would be nice for all HR data to be of the highest quality and the same as financial data, which requires reporting and monthly closes have to be exactly right. But it’s not. People are messy and therefore people data is messy. And to make a good decision, you don’t really need

The details, what you need is the trends and the interpretation.

Abhinav (22:45)
Thank you so much, Dan, for talking with me and for the incredible insights. You are one of the most experienced person I have spoken with when it comes to people analytics. The work that you have done in connecting people insights to the business impact is truly inspiring. Keep up the great work to make companies more data-driven and…

people-oriented. Have a great day and thank you so much, again

Dan Lapporte (23:09)
My pleasure

Summary

HR was traditionally considered a soft area with little quantitative data. However, with the advent of people analytics, HR has gained a stronger foothold at the decision-making table by providing data-driven insights.

Consultants like Japneet Sachdeva are critical in empowering HR with people analytics and developing new possibilities, such as generative AI.

In the People Analytics Talk episode, Japneet and Abhinav discuss the about:

– Why People Analytics is important in Consulting?

– How Consultants Use People Analytics?

– People Analytics Maturity in India

Key Takeaways

• People analytics is a core part of consulting work and has become a product in itself.
• Data is essential for people analytics, and organizations can start with imperfect data and iterate over time.
• People analytics provides quantification and ROI for HR functions, helps consultants stay ahead of the curve, and allows them to pioneer new practices.
• The maturity of people analytics involves a complete ecosystem, including data culture, structured insights, and automated actions. Data-driven decision-making is crucial in HR and can be achieved through people analytics.
• Attrition prediction models can help identify systemic issues and individual-level insights to improve talent retention.
• AI is transforming people analytics by providing actionable and hyper-personalized insights.
• Data democratization and conversational interfaces can bring people analytics to every employee and manager.

 

https://youtu.be/Gy8IhSW_NyA?si=Gv1AZZc9nyWr4sGP

Full Transcript

Abhinav (00:00)
When large enterprises face critical business and people challenges, they reach out to big consulting companies like Deloitte, Accenture, and McKinsey. But did you ever wonder how do these consultants, who have very little idea about the company, get up to speed and end up consulting about what the company should do? The answer is they rely on data. And often in many business problems and all human capital problems, they rely on people data, in short, people analytics.

People Analytics is a core part of almost every consulting company, be it McKinsey, Bain, or Deloitte, and lately it has moved from becoming an enabler to a product in itself. Hi, everyone. I’m Abhinav and welcome to the Peoplebox Analytics Talk, where we invite incredible leaders to go deep into the fascinating intersection of data, AI, and people. Today, I’m delighted to have Japneet Sachdeva on the show. Japneet, a partner at Deloitte, is one of the most experienced people in People Analytics in Asia.

She’s also the co-author of People Analytics Maturity in India Report, which we spoke about in our very first episode. In her previous role, she led a large people analytics practice for global clients at Accenture. Welcome to the show, Japneet.

Japneet Sachdeva (01:13)
Thank you so much, Abhinav. Glad to be here.

Abhinav (01:15)
Japneet, talk to us about your journey in the consulting world and how you get into people analytics.

Japneet Sachdeva (01:21)
Okay, so I’m an MBA from IIM Kozhikode, and then Accenture was, you know, I was a campus hire for

them. They were also looking to build their HR consulting practice. So I joined what they used to call Talent and Organization Consulting, joined fresh from Campus and somewhere along the line

Accenture wanted to invest in this whole talent and organization analytics capability. And we set up a team. We started from basic surveys that were very in, you know, like 10, 15 years down the line. But from there, we evolved into, you know, more of analytics around, you know, machine learning, using more of algorithms, Python and statistics and stuff.

And then I think India got me to Deloitte closer to home, closer to my heart. So the last two years, almost two years now, I worked on setting up the people analytics practice in India.

And that’s how the journey has been Abhinav.

Abhinav (02:14)
That’s fascinating, Japneet. A lot of our audience here are founders, are CEOs.

HR heads and people analysts understand that for a company it’s a very critical piece.

So Japneet helps us understand why people analytics is such a critical piece in consulting work.

Japneet Sachdeva (02:34)
So I’ll tell you, I’m going to have two, three things, you know, why it makes a lot of sense. Okay. Number one, A lot of other functions, if you see a finance, a supply chain, a consumer, for example, they were pretty solid on analytics as a part of their business, right? HR, I mean, fortunately, or unfortunately, we were always the softer, you know, practice.

So for me what analytics brought in was that opportunity to say we mean business as well. Right. So whenever I have discussions with CHROs, it was my biggest, you know, sort of pitch to them is that, see, this is what gives you the seat on the table because you can go back and say that, you know, there is a concrete investment that the quantification that HR function lack. I think that’s to me, the number one, you know, what I see enabling all organizations to do as a consultant for me, that’s the biggest shift I can bring to my CHRO. Right. At the end of the day, you know, what a consultant should be able to do is

as an HR consultant, I should be able to help a CHRO and a CEO to make a better case for their people. Okay, so that’s number one, right? To me, it gives you the power of quantification. It gives you the power of sitting on the table and saying, this is the ROI I’m going to get to you for any kind of investment in people. Okay. Number two,

As a consultant, just staying ahead of the curve. That’s what everybody else expects from us. If we don’t do that, we’re pretty much not sustainable as a business. If I can’t stay three steps ahead of everybody else, that’s the core part of being a consultant. We are ahead of the curve. To me, that’s another opportunity analytics is giving. Gradually we saw, that while we started with basic analysis and analytics and service stuff, we have moved to using Generative AI for a lot of stuff in the HR space.

We’ve recently done something on individual performance paths, and learning development paths using generative AI. But I think a little bit of it’s also me as an Indian feels good about it, Abhinav. So while, I mean, as India, we are pushing boundaries, right? We are really going ahead and doing stuff that nobody believed we could do.

So to me, that’s also like a close-to-heart right part of it being as an in-depth consulting practice to say I am pioneering stuff that you know nobody else has done. So maybe those are the top three Abhinav that as a consultant I feel very proud of.

Abhinav (04:51)
That is so fantastic. I love the phrase that you said that everybody expects us to be ahead of the curve. And just going back to the thing that I spoke about initially when you go into a company, you know, there are experts there. They are, you know, people who have so much experience. How do you get yourself up to speed, understand the nuances as an outsider and are able to bring some, you know, out-of-work insights to help them achieve the objective?

Japneet Sachdeva (05:07)
Thanks for watching!

As a consultant, I feel we succeed only when we have a strong partnership, right? So nobody knows the organization better than the organization themselves, right? So I think

how this whole partnership works right between the client and the consultant is one.

we are able to provide a little bit of outside perspective to say that sometimes it’s okay to just take a step back and say that I know my stuff very well, but what is it that I can learn from the outside world? I think that is what sort of a consultant helps in whether we like it or not, but we would have seen similar stuff happening across organizations. So that’s number one. Number two, I think…

as consultants, we also spend a lot of effort and time to understand the organization, right? How it works so that it’s both ways, right? I mean, they open up to say, okay, let’s see what outside can bring to us. But we also open up to say, let us learn on how your business is structured, right? What you are doing so we can also best tell you what will work versus what will not work for you. So stronger that partnership is right, stronger that to say that we are working on this together rather than a consultant has come in to help us do something or whatever.

The stronger the partnership is more successful the outcome is going to be.

Abhinav (06:35)
Fantastic.

So, 16 years you have been in the consulting world, almost a decade in people analytics. A decade ago, people analytics would most likely be an enabler for other human or business consulting projects. Today, it’s becoming a product in itself. Today, companies are asking you to solve their people analytics problems. How did this transition happen? And who do you contribute towards it?

Japneet Sachdeva (07:01)
So I think…

two- three things that have changed, which has contributed to generally the growth of analytics and AI. One is the whole tech transformation, the whole digital transformation that has given us so much data to talk about. And in the people space, I mean, you see the biggest business that’s happening in human capital right now is the HR tech stuff, all the HR tech implementation, whatever the HR MS may be. That’s the big one. Now, what it’s doing is it’s giving you a lot of data, which we didn’t capture before.

I think second, is the whole treatment of data itself, right? Moving away from simple, you know, just having a survey and asking people questions. There’s a lot of possibility now to use unstructured data, right? What I generally call proxy metrics, right? I mean, I’ll tell you culture is an example, right? So typically, how would you assess the culture of an organization? Go float a survey, okay? How are you feeling? You know, is it good? Is it not good? I’ll tell you the past two years, I have done 20 culture assessments, and one survey.

Only one survey. 19 of them have been non-survey based. You use proxy metrics. You use more unstructured data. So if you want to see a safety culture in a mining organization, don’t go ask people, do you feel safe? Look at the safety incidents. So look at just the treatment opportunities we have. How much we can do with unstructured data these days? Today, I can look at a worker working in a factory and see if his helmet drops. There’s an issue.

safety issues. So, I mean, there’s just a lot of possibilities right now on what I can do with data. So I think one, just the data that you start capturing, two, the possibilities and I think third, just the general business data progress that’s happening, right, has helped people data as well. Now, if you’re investing in a data lake, right, I’ve seen, while we did this people analytics maturity that you mentioned, two things we saw. We saw that

the organizations either were heavy in consumer analytics, or actually picked up people analytics sooner because they already have a data lake set up. They already have capability to do predictive modelling, which they’re doing for consumers. Easy to replicate to people. Secondly, so all of these big tech companies, right? Very good at it because the capability is there. The appreciation of using data and analytics is there. So I think the overall progress of data and analytics in the business also helped people analytics.

Abhinav (09:08)
Good. Yeah.

Japneet Sachdeva (09:29)
So to me, I think, these are some of the things that have helped us progress as well.

Abhinav (09:33)
I think this is so interesting that you talk about data and, you know, especially the use case of not using the surveys And I mean, being an employee engagement platform ourselves, we see that there are so many of these non-employee driven data that you have, whether in their one-on-one notes, whether in their goals, it’s hidden in their, you know, reviews where you can go and identify whether the

you know, employees engaged or whether he or she is more likely to leave. But coming back now to the use cases Japneet, you know,

Having said that almost every people analytics expert that I’ve spoken with has mentioned that, you know, your output, uh, and, and then, the objective that you want to achieve out of the people analytics exercise, all depend on the quality of data at the end of the day. You know, it’s the garbage in garbage out. So when you reach out to these large companies,

Japneet Sachdeva (10:06)
doubt.

Abhinav (10:28)
I mean, it’s hard for me to imagine that they have everything in place, all data, all historical data, all in one place. And just ready for you to just crunch numbers and get inside.

Japneet Sachdeva (10:39)
Spot on Abhinav and I have yet to come across an organization that has all the data in place. So it’s, I don’t think so it’s happening in this world and that’s the reality of it. Right. I mean, data can fall apart anywhere. Data can fall apart when you enter into data, data can fall apart when somebody is updating the data. I mean,

Even today, we find those simple errors. What is a date format? Is it MM-DD- YY, or is it? So generally, my recommendation for Abhinav to most clients is that now there are two ways to do it. One is you actually invest a lot of time in getting that data right, having a data governance structure in place. And to be honest, some organizations are doing that. I mean, the Data Strategy Project that I was talking about, we’re doing a six-month just Data Strategy Project, just getting their data right.

It is not even getting to the analytics part, just getting the data right. So that’s one way to do it, right? That you actually, you know, I mean, the criticality of data is so important to you that you spend time and money and effort to get it right. Okay. But…

a lot of organizations, I mean, you’re also going towards that whole, you know, agile mode and I want to see something in six weeks. So if I tell somebody, you know, I’m going to take, and it takes time, right? Getting data right does take time. Okay. So if I tell somebody, you know, I’m going to take five months and tell you, just get your data right okay, you know, what are you even doing? So

Abhinav (11:49)
Easy.

Yeah.

Japneet Sachdeva (12:04)
Then there are types of organizations where we’re not willing to invest and not, I mean, it’s not judgment, right? We want to be quick, right? I don’t want to wait for eight months to see a result, right? So there generally I’m saying is give it a first pass, right? Even if you’re 70% on your data, what the analysis will at least help you do and see is that, okay, you know, unless I see a dashboard in front of me, I’m not even going

Abhinav (12:19)
Yeah.

Japneet Sachdeva (12:30)
to be able to tell you also whether this data is right or data is wrong. Right. So you need to start somewhere is what I say. So, you know, a lot of, a lot of my recommendation is let’s go parallel. Let’s, let’s do something. I mean, 70% accuracy is better than 0%. Right. So don’t compare 70% to a hundred percent compare 70% to 0%. So start somewhere and then it’s an iterative process. Right. You will get to 70, you will get to 80. And I’ll be honest, I’m working with my own CHRO, right? Deepti Sagar Chief People and Experience Officer.

Abhinav (12:42)
Yeah.

Japneet Sachdeva (13:00)
We’re doing a people analytics project with ourselves as well. Okay. And exactly the same approach, right? Let’s get the MVP rolling out. So we’ve just rolled out our first dashboard to our business leads. Let’s get the MVP rollout, right? Let them also come back, and say this is not right. This is not right. And we saw some major glitches in data, right? Our iteration has significantly gone down from last year. So, but

Abhinav (13:13)
Yeah.

Japneet Sachdeva (13:23)
At least start somewhere, right? You will not get to the moon and the star the first time, but at least start, you know, so that’s that sort of my recommendation on data. Data is not going to be perfect, but that’s our life, right? Nothing is perfect. So let’s start somewhere.

Abhinav (13:37)
That is so fascinating. You can see the smile on my face because listening to these startup analogies of MVP that we use and practice in everyday lives to bring the, you know, V1 or V0.1 in the hands of the user as soon as possible, even though it’s breaking. And hearing that, you know, large, these gigantic consulting firms also follow the same. It’s so nice. It’s so incredible.

Japneet Sachdeva (14:02)
That’s the way to go, right? We are in an agile world, right? Let’s try fail, go quickly and you know get on to the next step.

Abhinav (14:05)
Absolutely.

100% I couldn’t agree more on that and on the maturity bit, you know, Japneet you author this Fantastic report. I think everybody in my company every one of our clients that are shared with this absolutely loves that So fascinating work there, you know along with Nitin The report, you know, you spoke about you know, maturity of people analytics in India Could you explain what you meant by it? and you know, what are the different stages of maturity that organizations

typically go through like very sort of powerful insights that you got out of that.

Japneet Sachdeva (14:44)
So I think first and foremost thing Abhinav that I touched a little bit while I was talking about it is just in the thinking of it, just in the approach and framework of it, we try to go away from just the analytics maturity of it. So analytics maturity is your typical descriptive and then exploratory analysis, then neuroprotective and neuroprescriptive. So one thing we focused on is just one part of your people analytics maturity.

the ecosystem around it that will define maturity. We discuss a lot about data. So that’s an important part of maturity. Some of the softer elements that we generally don’t discuss when we discuss an analytics maturity, are equally critical, right? What I said earlier, is user engagement, and business alignment. I can create hundreds of fancy Chat GPTs and dashboards and everything. But if my user doesn’t like it, if my business feels this is not useful, so.

Abhinav (15:33)
Thank you.

Japneet Sachdeva (15:42)
I mean, one of the dimensions we also looked at while we were discussing with maturity is how business and the HR analytics or whoever is managing HR analytics are actually working together. And I keep giving the Google example, right? Google at the time of Laszlo Bock they were so successful because the business and the team, the team of his managing HR analytics was so closely entrenched, it was almost like a, you know, this is my problem, solve it for me using data, right? So to me, that was

Abhinav (15:57)
Yeah.

Yeah.

Japneet Sachdeva (16:11)
One big thing about the maturity, right? We moved away from saying that, you know, any analytics maturity is not just about analytics, right? It’s about a complete ecosystem culture we spoke about, right? Do we have the data culture in the organization? So that’s one part of the maturity. Second, in what you asked how are we seeing India, right? So, I mean, if I broadly look at it, we define four levels of maturity, right?

Abhinav (16:30)
Yeah.

Japneet Sachdeva (16:35)
pretty much where you’re all over the place, you don’t know where data is. Two is at least where you’ve done some sort of sorting, right, there’s some basic dashboards that come through, whether it’s your HRMS who’s pulling it out, or at least you’ve done something, you know, hands-on. Three is where you’re more structured and focused in terms of saying, I know what my business problems are, I have some sort of data lake where the data is getting pulled up from, and I’m deriving insights that I find useful.

Abhinav (16:45)
Hmm

Power BI.

Japneet Sachdeva (17:05)
And four is where we say that the whole loop sort of closes. So there is almost like an automated machinery, where I say, let me take the example of attrition where I say.

so and so is going to leave because his skill group is getting paid higher in the market. That red flag comes in either it action-orients to the manager to go have a conversation or it automatically goes to the compensation team to say you need to give the person a hike in the next year. And it’s almost like a self-running machine of sorts. Whatever the underlying, I mean, in certain use cases, even analytics is enough, basic statistics is enough as well.

Abhinav (17:44)
Yeah.

Japneet Sachdeva (17:47)
in certain cases, you might need AI and machine learning and complete the whole loop together. So irrespective of analytics, to me, the final state of maturity is to say that

preemptive issues have been identified, action has been determined, the system or person who is supposed to take the action has been generated and the loop goes back to say we took the action this was the ROI. So to me, that’s sort of the state for I mean we’ve seen I mean we were also a little liberal in our rating because otherwise nobody would have leaned in it so we have given people who at least achieved the complete loop in one or two of their use cases.

Abhinav (18:10)
Hmm.

Bye.

Japneet Sachdeva (18:25)
was most critical for them. At least they were there.

Abhinav (18:29)
Fantastic. I truly hope that with experts like you and companies like Deloitte, who’s actually investing so much in this, we see more and more companies moving into the fourth category.

One of the most critical parts of business metrics, like you also mentioned is about, you know, retaining your talent. It’s about the turnover or the attrition. Our audience would be super curious to know what happens, what does Japneet do? What does Deloitte do when a large company invites them and partner with them to just solve one problem, which is talent retention? What are the, what are the steps you take? What all do you

go into? Would love to hear that.

Japneet Sachdeva (19:08)
So two, three things, okay, Abhinav. So typically, one is a little bit of the education process also, right? One is the analytics part of it, right? Okay, so we typically build an attrition prediction model for them. We’ll use some of their internal data to see, what are we seeing in terms of trends of people left in the past. Why have they typically left? We’ll add it with some external data. So for example, I’ll give an example of external data. So skill group level compensation. A lot of compensation benchmark sometimes happens only at a level, for example.

And let’s say if I give my own example as a consulting firm, right, we would look at an analyst versus an analyst, how is an analyst paid in Big Four or other consulting firms? But within an analyst, you might have somebody who’s doing org design versus somebody who’s doing generative AI. OK, now they are not being paid the same in the market, right? So for us, both of them are analysts. And hence, we are putting them in an analyst company. So some of that external data, we try to sort of marry with the internal data

Abhinav (19:37)
Mm-hmm.

Hmm.

Japneet Sachdeva (20:07)
two, or three things, right? One, we’ll tell them what are the systemic issues we are seeing in the organization. So systemic issues could be one I just mentioned, right? There’s compensation for a particular skill group. Systemic issues could be certain managers or departments as well, right? You will see, and we see both kinds of things, right? We see attrition because staying with the same manager for a long time, attrition because the managers move too frequently, right? So either of them could come up as a cause, but that’s another.

Abhinav (20:30)
Thank you.

Japneet Sachdeva (20:33)
kind of systemic issue, right? To say that, you know, either people are not getting enough stability or people are feeling too stuck with the manager. So some examples, that’s one kind of insight we’ll give them. The other we also most often go into an individual level detail to say, based on the model, what the model is saying, what is the likelihood of an Abhinav or Japneet leaving tomorrow? Right? I mean, and again, you know, you look at all the factors you look at, you look at the performance, you look at the demographics, you look at

Abhinav (20:55)
Yeah.

.

Japneet Sachdeva (21:04)
If somebody has a skill that’s going to get a 50% hike in the market, then all your party culture and experience is not going to hold the person back. So there are multiple things that you will look at, rules, and flexibility.

And this is that we’ll tell them at an individual level, what it looks like. So that’s the analytics part of it, right? Which says systemic issues in the organization, individual level. Then there is, now what do we do? How do we action it around? So that’s one part. We also educate clients as, okay, so how do you intervene? Certain interventions are easy, right? Maybe you are feeling like the onboarding training is very simple, right? You just go and bandage it and you’ll see the impact. Certain are more complicated, right? So every time, I mean,

Abhinav (21:43)
Yeah.

Japneet Sachdeva (21:49)
clients to say that just because somebody is coming at an 85% likelihood of leaving, just don’t pick up the phone and call the person, you know, are you going to leave? So there’s a little bit of education, right? What you want to do. Another thing I felt is that I’ve actually lost a couple of attrition prediction projects up enough because of that because the expectation is that if we get the model, the attrition will reduce. That’s not what’s going to happen, right? So you will have to do something, right? There’s an investment that’s going to go. I mean, what’s going to happen?

Abhinav (21:57)
Yeah.

Yes.

something.

Japneet Sachdeva (22:18)
model is going to help you is to say that what needs to be done, right? What do you need to do? So don’t just, you know, randomly just that’s, that’s one.

Abhinav (22:22)
Yeah.

Japneet Sachdeva (22:29)
we’ve seen is also start measuring the cost of attrition as well. Now there are people who are going to leave, which is okay, right? I mean,

Abhinav (22:34)
Oh god.

Japneet Sachdeva (22:38)
But like, what is it, what is the cost of, let’s say, replacing in Abhinav versus replacing in Japneet, right? If Japneet is an easy skill in the market, it’s okay, right? I mean, we’ll find another, find 10 other people to whatever does this work that she’s doing, then my energy doesn’t need to be focused on that. So there’s a little bit of cost and the criticality of skill as well, right? If there is nobody else who can do people analytics, then you know, you want to keep, I mean, one, the skill itself, second, the demand of the skill you are seeing as well, right? If it’s a growing business, right?

I’m growing at a 50% rate and I will struggle if somebody in the team leaves. So the criticality of the scale also is sort of a parameter. It’s not just about, you know, personally leaving or not leaving. So these are typically the things Abhinav that we sort of do in an attrition prediction project.

Abhinav (23:24)
Amazing. That’s this space is so close to our hearts and I’ll talk to you about that as well But just want to follow that when you go into a company to take an attrition project A lot part of your diagnostic must depend on data So do you only rely on the data that is available to them and of course the external factors? Or do you also create data through either surveys focus groups or doing things that you know give you some external insights as well

Japneet Sachdeva (23:52)
So generally flexible client by client. So certain organizations do a very, I would say very uncomfortable doing surveys and focus group discussions. And we’ve also, I mean, there is a mix. We’ve also moved to some digital platforms. So we’ve tried new things.

As far as possible, I mean, the objective is, as unbiased and honest feedback as we can get, right? The whole point of data is also that, right? That it’s telling me something that people are not saying, you know, if I may put it that way, is right.

There are organizations who still want to do a survey, but I’m seeing more and more hesitation towards, let’s not do a survey. And even if we are using, I mean, we still use survey data in attrition prediction, but typically what we do is we use whatever standard survey they had done, right? Something they would have definitely done, some engagement survey they would have done. So we try to use that, then again, specifically doing another round of, some, and again, case to case basis.

Abhinav (24:38)
Mm-hmm. Yeah.

Japneet Sachdeva (24:51)
help right I mean sometimes I feel they’re all over the place right people start cribbing about the food in the cafeteria and you know I mean so that’s not the kind of insight you’re looking right the food is I mean yeah maybe we can change the food vendor but I mean food doesn’t keep you in the organization or makes you leave the organization

How many sources of data input do we use by client? It’s also sometimes the availability of data itself, right? If there is a lot of unstructured data or a lot of non-intrusive data, let me put it that way, is available, then I’ll not do a survey. But at times there’s nothing like that. I mean, there is nothing. And what do you do, right? Then you just go and ask people. So.

Abhinav (25:12)
Yeah.

This whole word of turnover prediction is really, really amazing. And I have two follow-up questions. One is, how long does typically this whole engagement take, especially the prediction one? And I know there is a lot of things to be done after you give a diagnostic report. And second is, what has been the most accurate prediction that you have seen in all of these projects?

Japneet Sachdeva (26:00)
So I think it’s a one, it’s an iterative process, right? I mean, the first ML model, predictive model, yeah, you will get in seven, eight weeks, right? That’s not too much of an effort, you can do it faster depending on how many functions you are considering and so on and so forth. So you will get to something in six to eight weeks, right? As I said, right, it’s an iterative process. Maybe the first time around, you’ll only get to a 60% accuracy of the model, right?

The more you keep feeding in the data, the more you keep using, okay, this person left, this person did not leave or etc. The more data you keep in mind, I have seen it go up to 92% in one case, which I think is alright. Yeah, I mean, I’m not even expecting more than that. So I think it, it.

happens with time, but we just have to be patient with it to say that. And again, as I said, we’re doing it for ourselves, right? And the way we’ve thought about this, it’s 60% versus zero, right? Today, I absolutely have no clue who’s going to leave, right? I mean, of course, there is some, there’s something from the engagement survey you will get, but there’s something from what managers are feeling about it. But there is no systemic way to say that, you know when I look at my, you know, CHRO and when I look at my CEO,

Abhinav (26:57)
Go.

Japneet Sachdeva (27:15)
from their perspective, there is no systemic way to go and tell X business leader versus Y business leader, watch out, you know, watch out for these people. For them, 60% is also a great start. So I mean, you can start with a 7 to 8 week, you know, is a 6 to 8 week, you know, you will get a good model running like a 67%. But yeah, the more you invest in it, the better it will keep getting.

Abhinav (27:38)
it’s hard for me to imagine how incredible a return on investment would be for a company that is absolutely at zero like they have no idea who is about to leave till of course the letter comes to even achieving a 60% or 92% I mean it’s if they would save so much of money they would have so much of control they can have contingency they can have backup plans now they can take actions which is, of course, all business dollars.

Now coming to the most interesting topic, which it was so hard for me to control myself to come to, which is technology and AI. We are a Gen AI company. We are heavily investing in this. You talked about AI in the previous answers. I’m very curious about how do you see the whole advent of Gen AI into the people analytics world and some of the trends that you’ve already seen.

Japneet Sachdeva (28:35)
Great question Abhinav. And the reason I say, you know, the people analytics was the best place to, you know, kind of capture the AI and GenAI use cases. They quickly came to, you know, because ultimately people analytics was anyways doing a lot of AI work, right? Now I see, I mean, I see so many use cases out there, Abhinav, and the discussion that I’m having or the ones that I’m running.

Because the inherent part of it is again the same, right? It’s data and it’s data’s, I mean, just the form of data has changed, the ability that we have to process the data. LLM has given us, you know, a new sort of life, I would say, for data analysts to, you know, the kind of processing of data that we do. So I see it impacting almost everywhere, right? Culture assessments, for example, right? All this recruitment, CV screening, I mean, recruitment end-to-end, right? I was talking about candidate matching, CV screening. Earlier, I have…

I mean, we can still use the LLM and customize a little bit basis, my organization, my stuff, et cetera. But just the potential it gives me like, I mean, it’s almost reducing a lot of my effort. You know, from starting from a zero code, I’m starting from probably an eight and a half or a nine in terms of code if I were to reach 10. So to me, I’ve seen I mean, it almost has given people analytics and accelerator, right? I mean, a lot of stuff that I was doing manually, I’m quickly, you know, using GenAI to do it. I mean, a lot of skills work.

We used to, I mean, we’re still doing a lot of work because it’s very custom and stuff. But at least that validation, you know, Gen AI quickly helps you, right? Even role skill matching, for example, used to be a big exercise for organizations, right? Can I get the skill ontology for whatever 100 roles that I had? Now you just go to Gen AI, I ask you to get a basic list. So to me, I see it as a big source of data, a big source of processing the data. It’s opening up a lot of opportunities to actually do more than, you know, what

Abhinav (30:04)
Peace out.

Japneet Sachdeva (30:27)
I was able to do. So to me, actually, this opened up a lot of more opportunities to do things faster to penetrate into more use cases as well. Right. Because you need speed, right? You need speed at scale to impact multiple areas. That’s what I think Gen AI is giving me.

Abhinav (30:42)
It sounds so sweet music to my ears. One of the very interesting things that we are doing is to solve this problem of data democratization using a Gen AI with a very strong access control. We are bringing the conversational interface into the hands of every employee, every manager, every HR VP so that they can also ask any people analytics questions about engagement, about who is working on which projects, what are the different projects being run. I’m curious to know that.

How do you see the impact of AI on the, you know, bringing people analytics in the hands of rank and file and how do you see this will impact the businesses?

Japneet Sachdeva (31:22)
So I’ll give you a…

this first use case that you know, just people that we worked on. We called it a fact or insight generator. That’s what we called it. It was simple, right? You upload your Excel sheet of data. And that was the first one, you know, we worked in as you upload the Excel sheet with whatever data fields you have. And you ask them, okay, what was the attrition in the past X rate? Even the descriptive analysis. And I know, I mean, as we keep sort of customizing the LLMs and using more and more of them, we’ll go to predictive as well. But even basic descriptive.

analytics that is not in the hands of a lot of CXOs today, Gen AI provides that. I’ll give you another example. A lot of times the question we hear from CHROs is that

See, I’m not going to go look into a dashboard and do the cuts and slices and see what data is happening. Okay. And primarily for two, or three reasons, right? One is how the dashboard is designed. So I mean, as people, analytics professionals also, and that’s why, I mean, people keep asking me this question, right? Why do you sit in human capital and you sit in analytics? And the reason I sit in human capital is because I want to design a dashboard that the CHRO understands So, okay. So a lot of part was the user interface, but a lot of parts is also that who is

designed for right? A recruitment dashboard will be looked at by the recruitment lead. Okay. He wants to go into the cuts and slices. Maybe the CHRO does not want to. So to me, the Gen AI is also giving that one layer in between to say on top of the dashboard or whatever, on top of that data.

you can still have the dashboard because it helps. It helps with certain things. But for somebody who wants to look at 50,000 feet, or for somebody who has a very specific question on something, can I just ask that question and not go into the whole dashboard and cut slices lightly? Sometimes if I want to see.

you know, what was my manager attrition from this particular department last year, I might have to go to three different drop downs on a power BI on a Tableau to get to that. So those simple questions and even more complex questions that you know, you want insights into. I feel it’s kind of providing that user experience.

ease of me getting insights, hyper-personalized insights. That’s another thing. I feel it’s very powerful with Gen-AI It’s not standard. Before this, personalization was happening only at the level of personas. Now we are saying it’s you. It’s up to your level that we can personalize. So I see a lot of power. I’m just from the insights generation perspective. I think there’s a lot it can do.

Abhinav (33:45)
Yeah.

We have them.

Yeah.

You put it so rightly, Japneet, it’s not just data, it’s insights, it’s actionable insights, and it’s hyper-personal insights. I think that’s the thing AI can do really, really well. I’m really hoping that there are more companies who are bringing all that into the hands of the users and employees. Probably my last question for today, Japneet, coming out of this people analytics world and just sitting in the shoes of a CHRO who has so many other things to do.

There’s one thing that you believe that AI is going to make the biggest impact, you know, for a CHRO. What do you think it would be?

Japneet Sachdeva (34:45)
I think I’ll go back to what I said up Abhinav Their ability to be more efficient, more effective, which gives them a leadership seat at the table, which to me, you know, sort of has been an aspiration that all CHROs should have, right? Why don’t a lot of CHROs become CEOs? You keep asking that question, right? To me, this is the game changer that it’s gonna be, right? We’ve never had that.

where they actually can talk the business language with ROI, with concrete results. So to me, this sort of is the biggest change that CHROs can expect out of AI.

What we’ve seen Abhinav is it depends a lot on the organization’s priority. For somebody who’s hiring thousand people a month, let’s say, and we have organizations, they are going to look at how it’s making their recruitment more efficient and effective. For somebody who is, let’s say, more into innovation, that’s sort of their theme, they are going to look at it, how does this provide more research, more content in the hands of my people.

Abhinav (35:28)
Mm-hmm.

Japneet Sachdeva (35:50)
30% sooner. For somebody who’s into coding, are my engineers going to be able to generate a code, I don’t know, 30% time faster than normal? So again, business context, and organization context, but there is an ROI around efficiency or effectiveness associated with each one of them.

Abhinav (35:52)
Hmm.

Again, that’s so true. You said like, you know, one of the very important aspects of Gen AI is hyper-personalization And that’s so true that the impact of AI would also be, it depends on the maturity and, you know, the business of each organization. And I’m very hopeful that, you know, the speed by which we are seeing the change in the whole people analytics in Gen AI world, it will be a very different conversation, you know, if you have it in a few months.

But, Japneet, I really want to thank you for first taking the time to speak with me and give some incredible insights to the audience. But also I want to mention, thank you so much for changing this career path from being insight provided to the global companies to bringing your focus to India.

Indian organizations because this is so important. You know, as India grows so fast, as we bring Indian organizations to the world map, Indian leaders, both business and HR need to be more data-driven, they need to make more people decisions, which are, you know, came out of analytics and insight, and you are at the forefront of providing. And I hope as a technology and Gen AI company, we are able to provide that through the new technologies and all that. Fascinating to have you, Japneet Thank you so much for your time.

Japneet Sachdeva (37:26)
Thank you so much, Abhinav. Thank you so much for having me. As you know, this topic is very close to my heart, right? I mean, I believe in the potential of people analytics. So, and as you said, right, being able to do it for Indian organizations is sort of at the heart of it, right? So thank you so much Abhinav for having me.

Abhinav (37:42)
You’re most welcome.

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.