The 6 Key Skills Every People Analytics Leader Needs

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In this episode we are joined by Arun Chidambaram, People Analytics Leader at GE Healthcare to discuss The 6 Key Skills Every People Analytics Leader Needs

Episode Highlights:
- The 6 Key Skills Every People Analytics Leader Needs

- The current state of people analytics and the opportunities that lie ahead

- The current analytical dexterity in HR and what skills are critical to raise the bar

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Chris Rainey 0:00
Aaron, welcome to the show. How are you my friend?
Arun Chidambaram 0:02
Thanks, Chris are doing great to see always. I know getting it's springtime here in New Jersey. So looking forward to it. Nice. Nice winter this year
Chris Rainey 0:12
is still freezing cold or
Arun Chidambaram 0:15
we've actually we had only one day have not even you cannot call it's no.
Chris Rainey 0:21
I'll take lucky.
Arun Chidambaram 0:24
It messes up some statistics for some folks, but not for me.
Chris Rainey 0:29
Yeah to Rp analytics, do you have a predictive model for the weather? No one has won even though wherever people always wrong. I'm like, wait, wait, that's your job. That's all that's
Arun Chidambaram 0:44
doing good. I'm looking forward to the spring right now. Beautiful.
Chris Rainey 0:48
Nice, nice. Let's just jump straight in. Because we could talk forever. So what do you think is the I'd love to talk about the current state of heat and lakes right now? And some of the opportunities that we have ahead of us? So let's jump in there.
Arun Chidambaram 1:01
Sure. Thanks for its nice question there. When you look at the state of current state, I just want to go one step on what is people want to say what is the goal of a typical people annex function. And as you know, I've been secretary, the 16th year for me in this space. And in a nutshell, like what our functions job is to, you know, make better talent decisions daily, using data to create value for our customers, and shareholders in our case, our patients, and more importantly, our colleagues, right? That's in a nutshell. And when you double click one more into that, what is that goal? Where do I spend, where do my awareness, my team spend? Where does this function spend time is mainly with HR folks, for sure. And business leaders, right? And when I reflect on my 15 years journey in the space within within HR, two words are coming to my head gross, Minh, I call this an effectiveness of this function. And the second is efficiency. And in a nutshell, like what is effectiveness to make it simple, effective nurses? Are you doing the right thing? And efficiency is are you doing the thing? Right? Right. And you'll be surprised for the longest and even me being in this space. I thought we were doing the right thing. And there is always a challenge to the efficiency, which is like, you know, do I have the right resources, the right tools? Can I scale this function, etc, those pieces came in. And I always thought that it is efficiency where the challenge is, and I'll give you a simple example. So in the current role at GE Healthcare, we in my first couple of years, I was working on a very good, great project on getting engineering efficiency, right, as for a large business segment for us. And we spent about eight months, working very closely with business leaders, great HR team. And as I started getting into the project, the bigger challenges was not having the right data in place, right. But we leveraged bringing on network science, we did all kinds of this, what we call the sexy, cool analytical thinking, to solve a big problem. But as I started coming out of the project, the things with really did not work were you know, not having data in the right form, or not having a governance. So for the longest part, I thought those are all not the cool things are like, you know, things I shouldn't be doing. Then I said, doing the right thing. And now, an over the last 16 months, we've changed that mindset. So we started building this capability. And, and many of my analytical friends probably will not like this or on the when I call on maturity of analytics. There's a classic for you've probably heard hundreds of times, which is you start off with what is happening from a descriptive analytics to prescriptive and it has come in many forms in shape, right. But I think that is simple to explain to leaders like what is that maturity look like? And And the challenge was we were more excited about the middle part, the predictive analytics and like an all, although more deep dive analytics. What is happening is that the trend is coming back towards this space of descriptive analytics. Now there's actually a nice debate going around on what should be, which leads me to that. What am I doing the thing right versus doing the right thing. We said that we need to focus really in what is called I call it data ecosystem. In fact, there's a fantastic article by Capgemini on data ecosystem I'll give you a very simple way, you know, it's a very scary statistics. If you look at on one axis, you can think about technology and tools in the data space. And the other axis is process, which is your culture skills, discipline behaviours over another axis 70% of the organization's, according to that research are in that data, what they call low low, and you don't have the right process is called Data laggards. And only about 10 to 15% are in the data mastery, which is on the other extreme, when you are on the left side. And in fact, we are on the same side, we are part of the assembly, we are part of the norm. And and then you have like people who are great with the discipline, but they don't have the money, but they are they I think they call them like they are aware of that situation. And the day people who are high on technology, and tools, but don't have the behaviour is kind of another branding they gave for that group, but it's a very small percentage. So when you have, so when you talk about current state of people analytics, I think we want to spend more time on the space of data ecosystems, right. So in my current CHR, O, we are really nice. See, Chief People Officer. And she had a very good point, she said, Ron, we don't get the basics. And right. And she came from a different lens, which made absolute sense. It's like, we don't have the absolute basic infrastructure. And what happens in this game is when you're trying to and when you're trying to do like, you know, your team is doing great stuff, but it's like, what happens? Is this person not I'll stop here and probably get your thoughts do you do cool things. But you cannot scale it. And when it's called meaning you cannot adopt it. And when you don't adopt it, what happens? The HR leaders get frustrated, they said yes, all we do cool things, but I don't have you. And I cannot reach them of scale. And because I don't have this ecos it's not even a question of not having number of people, a lot of people have said oh, I don't have a small team team will come in, okay, you can have like 30 people in this scenario. And still, you will not be able to achieve or reach scale because you have the wrong systems in place. You are in the data logger. So if you focus on that space, you may not be doing the cool stuff. But on the long run, when you set that up, I think it's going to be extremely beneficial. So I think that's the space. Yeah, I feel is very, very important to me pause there and just see what it kind
Chris Rainey 7:33
of feels like you're being pulled pulled in two different directions. Because you know, the business wants you to deliver on those core projects, etc. and deliver at the same time, you also need to figure out the basics, build a foundation, get the data, right, so you kind of like in this correct to and fro back. And
Arun Chidambaram 7:52
right now, I'm getting more clear, because when I even looked at business, they have the same problem. They have their systems are also not the cleanest. And it's all on all the companies I've worked with as the same scenario. So it's not like an HR thing. But if I am able to throw my team and my leadership and that to the sponsorship, like hey, I have a discipline have standards of governance, you know, it's still we still have files which float through excels. And we have all we're trying to do Amazon's s3 and cloud, we do the but you also have this extreme. So the type of data sharing complexities is all over the we have Teradata, we have excels, we have box we have, then we have this ODP, we're going to do one day a platform. But what I'm saying is the behaviours are very, very critical. So I'm very clear that for our long term success, that space needs, and some of as I said is 10 to 15% of the organisation will do it right. And I don't know if you have chatted with guru Saitou. But even when he was with Capital One, and he did a really good job of spelling Sarang just want to spend time on the site has been extremely helpful for that organisation. But I've not had many cases where they spent their energy. And it was good when your leadership also supports that.
Chris Rainey 9:10
Oh, yeah, I feel like you have to. Yeah, Pfizer like what are you doing? Yeah, you know, doing all these cool projects and doing XY and Z for us? Well, we need to focus on this first.
Arun Chidambaram 9:20
Yeah, I think it makes absolute sense from where they're thinking. But initially, when about few years ago, I would have resisted this thought and said I want to do more cool things. And that's what my teams that my friends are doing. And I did cool things but when it comes to frustration when you think about your customer when I said deliver better talent and decisions to all the all pieces not which will work if you do not have the right data ecosystem.
Chris Rainey 9:45
That being said then and we've asked this question a few times, but I feel like it's evolved over the years. In order to achieve this. What are the skills and competencies needed to be a people to be three Successful wooden rollers are people next leader?
Arun Chidambaram 10:02
Um, that's a great question. Right? It's very personal to me. So let me explain I've been through my 16 years through like some really good companies I worked from Merck, ESPN and Pfizer, not GE Healthcare. And when you look at like the evolution of, you know, what is people analytics, like, you know, back then it was not the state come in the trance, it has transformed analytics in general has transformed, right? You did not have like a PhD in mathematics and like, you know, social psychology, neuroscience, etc. in that field? Absolutely not. But I think there are five or six things, I think the skills that people are like leader needs to have, and you'll be surprised, I'm not gonna say anything to do with data. Because a lot of people think that they in fact, are being brand or you're the cool guys do the smart ones, you the data forks, and these are some of the names we get. So I'm like, we're not even the data, but data happens to be part of our job, we just do it. Right, we need data to do our jobs. But I think I'll start with a nice analogy of t of an actor know. What we do is, we if you look at some of the work we have done like at admin, as working with ESP and work was like how does like you know, sales happen in ESP and what has happened in a production shop. So when you try to solve a problem, you really need to understand where what the business is how the business works. So you learn about like, how literally like the Monday Night Football show, how does that whole thing happened? So I had the opportunity to go through the at Pfizer is something that both at Merck and Pfizer, it's like how does you know drug discovery happen? What are the different stages? And then same thing with healthcare and GE Healthcare, like, you know, when you look at housing, Mr. machine made, why do they make fewer more machines, etc. So the reason, the reason I gave these examples is, and it's just like an actor, an actor, we have seen, you know, you need to act, you need to think about the history, if you're doing like a history kind of a role, you got to know exactly everything, right? So first, most important thing I would say, is having a learning mindset, you clearly have to listen and law, you cannot go that you do that I know everything. I am the data person, I know all this crazy data science model. It's a very, very bad thing. So yeah, and I've seen people like in particular, I can imagine that. And I, you know, you get excited. I mean, it's not that they're intentionally doing so I tell my team like, you know, calm down. Because when we run a project stress data does not even come up the first two stages, we don't talk about data, we like what is the problem? Because analytics is equally about asking the right questions as much as about solving. So it's not that we jump into data and start going crazy with it. So the learning mindset with that actor analogy I want to give it's like got to have absolutely a learning mindset, right. The second is an interesting one. The second is you will be tested some like things which are not logically making sense. And as you go closer to you know, leading teams, how decisions are made, you sit with, you know, your business leaders with even your, you know, Chief People officers in the HR leadership team, sometimes, you know, you call it a park data, and distinct like, you know, differently. So, the balancing is very important. So, you cannot be in two extremes, you cannot say that I had to work with God. Or you cannot say I'm going to be technical, every darn thing. And you got to come to a common ground, which means you need to have an attitude, which is saying that, okay, I'll let loose. So things like you know, directionally correct and precisely wrong is the solution. It cannot be like saying extremely in by having that behaviour. You don't alienate folks to because we try to do that, because people are sometimes scared to talk to us, because they're like, oh, they might ask me a question is that degree of my I've been in situation, I tried to train them. I said, just because my team knows data doesn't mean they're smart. Just because you don't are involved with data or your sign doesn't mean you're not smart. Create, it's about both sides need to engage, and you're come to the common ground, which leads me to like, the space of like, you know, the people and we need to be extremely tolerant. There will be things people will say, I think a very, you know, a long time, but 10 years ago, a good friend of mine from Denmark, Martin Anderson, not been super touch with him, but he did say something very good. He said, around cognitive dissonance. And what we do on people nowadays is a very bad cocktail. So what he said is when the moment you say no, your answer, you're wrong through data, they'll start like tearing that apart. So you cannot go super technical on that. You got to have a common ground so you got to be tolerant on that. You got to let To go certain things, you cannot try to solve every problem or prioritise. And that comes from awareness. So you've got to be extremely tolerant, then comes when you're when you're tolerant, how do you get things done right? Is you need to have this a very critical skill, which is having influencing skills. You'll be have to be assertive, we have to be reciprocal. We have to listen, like I said, and we have to keep the core value. Because I'll give a very good example. It's, it's, I will not tell which company but it's definitely not GE Healthcare right now. But one of my prior companies, we were involved in forecasting headcount, and what drives headcount always found it might be a good project, I think, my team at our project, it was all great. But it was actually a senior partner and very, you know, from a great consulting company, and the person comes in my team puts down the regression equation, you will not believe a person comes literally says he literally takes that equation and says, Can we literally say, okay, that constant out and take the coefficient on and so can you put something sent me as they said, text me, like, You handle this one, I go to the I go to the leader and say, do whatever you want, we are not signing off on that you just cannot. So you don't get angry on the user's be are very clear. Yeah, I'm not signing up, because I will not because that's the you don't lose value on that. You just can't say because the senior partner says, I'm going to do it, but you encounter it, how do you tackle it and the leaders get it says, Okay, fine, we'll take it offline and have another solution to it. But having the influencing skill is very important, because it buys you, you know, relationship with the teams. Then couple, three more, I will say like, patience is another one. Like I talked about the data ecosystem, even a project this morning was working on, you require a belt a patient, you just want because nothing will be right for you. All this data is in there. One other project, we spent almost two months understanding data, they've put you through all kinds of go to one go to this doesn't work. This is what we have blah, blah, blah. But when you when I was in the beginning of my, in the space of people analytics was very tough. But I tell my team, you don't have patience, this is normal. Couple more. And I'll stop here because one, this is very important to me. I call it humility. You know, I wish I had a coach who had taught me that before. Because when you are the data, it gets to logically You're right. Because you can you know exactly what the data means. And I've seen including me in my early years, I could have been better. But always there's always a learning, which is like you don't try to understand where the HR team is coming from, you know, they go through if you don't know what an HR business partner really does. And we I was very one track on all this is our data is Why is she getting it? Simple things? I'll tell you what engagement surveys, I used to, like, beat the engagement surveys as like, you know, it was like, why are you not getting you're wrong. And now I understand what happens when an engagement. So when you can pulse an entire organisation every single day, yeah, golden opportunity, they get the best out of it and make a decision, right? So this is the point I'm trying to make this as an analytical leader. You just cannot say that or data's wrong. It's you have to understand the other side. And I tell my team, like, you know, just because you know, data, don't try to like, be smart and say that I know. And I have a very fantastic team. But I've seen I'm on sales, my team here, but I've seen pockets, where I could have been better in my past, but I definitely teach my team to be, you know, more. It's a two way street. Yeah, the last piece which you've heard us around the story telling us I was
Chris Rainey 19:02
wondering when he was gonna throw that in.
Arun Chidambaram 19:05
And I think it's as it goes to the movie analogy I gave, yeah, on to it ties back. Because we take great we deal with complex data, you've got to make it very simple, because I'll give you this another analogy, you may be in a row with an audience, where one person will be a Jackie Chan fan, and other would be A Clockwork Orange fan, you might be you might be in a row, but you got to tell like, You got to please both of them. You cannot go on one extreme. So you got to so this is more than like, you know, I've seen a lot of things that aren't storytelling, visual representation, colours and all that stuff, which is great. I think you will be surprised. You will get a 10 minutes on a let's say on an executive leadership call. It will convert into two minutes. You got to tell your story in two minutes. Be ready for it. You got to anticipate what the next question is. So this law More in how you present how you train a yo, it's not about I did three months worth of data, they have to see everything paid on obviously, this is not the final story. So it's very critical that it's a combination of consulting steps, less storytelling skills, which is very, very critical. Especially those are some other spaces. And as I promised, I said, there's nothing about, you know, any mathematical modelling,
Chris Rainey 20:24
I was gonna say, like, that's the interesting part, every single piece of advice you gave was nothing to do with technical capability. And, and I've, I've obviously had multiple, I've led multiple functions in multiple roles, and you kind of don't find that out until you're in it. So you know, how do we prepare our people, analytics teams and our leaders, when you can't get taught those things in a university classroom? You know, these are all things that you have to learn on in the flow of work, if that makes sense. The hard way, the hard way, sometimes
Arun Chidambaram 21:02
is what are the leaders can do? I mean, the next generation, right? Yeah, how leaders might want my team members to be the best people on as leaders at least that's good. That's what many of my peers also do some fantastic peers. But that's the message I would give to someone who's coming to this function, one create a career out of it just to be understand the other side, not just be like data driven. Don't be that don't be branded as the smart guy. Don't be that smart
Chris Rainey 21:31
for you to understand both sides of the fence. What do you feel like? You know, we had this conversation for HR for years, like HR seat at the table? Do you feel like people analytics? People Analytics now has a seat at the table?
Arun Chidambaram 21:46
Um, it is? I'll tell you, it's a great question. Right? So I'll give you my perspective, and it will change with others. Over the last three years, I would say four years. See, I think I don't know if I had this chat with you. But let me know what you think. This function was a luxury, yeah, to for five years. And that's why most of us like we want to do the cool things, cool things, we never thought about the scaling, you never thought about efficiency and effectiveness. Now it's becoming a business. So and it does not become a business for all like me to get that. But most forward looking CHR OHS are thinking of this function as a business, that itself is saying that we have a seat at the table. It doesn't mean that as everyone has sure, because that's the trend, they're going in maybe another three to five years, it will become more like, you know, very crystal portable. We still don't like to be it's not 100% there. Because if you grab about 10 people, our next leader, and you have a very good network, chances are they're all at different reporting relationships. Yeah, we do. Or there are different levels, how they want to integrate your overall HR strategy is going to be different. So on that space, that's what that business need. Because more clear, I would say in another three to five years, it will be just like a business spot. We're very clear on where dysfunctions are. So
Chris Rainey 23:15
do you think that HR that Chief analytics should sit underneath HR? Or do you think it should be
Arun Chidambaram 23:21
separate bait I've had, I mean, I have two sides of the coin. Like I've heard some companies where it's part of a central analytical CoA. Yes. But But I think what wherever it's what is most important is you need to understand how human resources operates. You need to understand the mechanics of human resources. You just cannot be like, hey, it's not emulating, aiding human resources, also human resource helping you. And as long as DoD bridges the weather report on an analytical CoA, because there are pros and cons to both place. Because if you're an analytical CoA, you're surrounded with the right kind of resources, you might be able to shift, you might be able to gain the motivation to go across many other analytical, the learning power on analytics is high. But if you're part of HR, your understanding of HR is more, I would say staying under HR would be a better option, because many analytical leaders, because now that the field is growing, right, you have all kinds of leaders coming into the space, and which were analytically very strong. Where they have not strong is how human resource operates. So if you're under that umbrella, it works. So I think it is I don't pick one or the other. It depends on how much HR knowledge you have. But it's not about just giving analytics to HR. It's also taking HR into analytics. Yeah,
Chris Rainey 24:40
like you mentioned before, you have to have both sides to understand where do you see the future of Chief analytics? Yeah, three, four, I
Arun Chidambaram 24:50
did talk about the luxury as a business need, which is the future it is setting up kind of as the first part that in another five years the identity of this function will be be more clear to the audience exactly what we do. Even its simplest things like, you know, when an HR leader is I've started saying that when HR leader says, Oh, I have a background talent management robot, blah, blah. And I'm started seeing Analytics as well. But if you look, look, look at 10. Sure, and you still don't have it. So yeah, that'll happen. That's one. So the business needs, I think, this space of data governance, I think that will become a reality, because it's sliding back again, into the space where the functional selling is making sure that the basics is right, this effectiveness, efficiency space, I'm talking about data engineering, the Chief Data office, you know, building that data ecosystem powerhouse is very critical. So I think they will spend more time and that will become a norm for people on actually the thing that that is important to, you know, create a world class organisation and politics. There's a third one, which is very interesting, which I think is going to happen. Once you have the data ecosystem, what will go is what I call intelligent systems. So when you think about democratising data, you know, descriptive and diagnostic analytics will no longer sit with people analytics, because it will become into a product. And many people are introducing products, but we don't have our ecosystem, the product fields is nothing the product is strong, so you technically don't need Why is someone leaving, and you're only to do like your analysis behind the scenes using data centres. Machines will tell them and like you know, right now with all kinds of changes happening in the AI world. But I think it's very, very important. If you are thinking that oh, I am going to be running reports and I will be telling why someone left or what the some of the projections are forecasting is the machine will be doing
Chris Rainey 26:55
all my automatically you kind of schedule it to do it every quarter every
Arun Chidambaram 26:59
wherever it's from my job technically is at risk. So if I think that I am no, I'll be available for next 10 years, I think that will change you need to understand. So more time is spent on the top to like, you know what, like should happen? What could happen logic so
Chris Rainey 27:12
that, so it's even more more important to have those soft skills then Right? Correct. So that's
Arun Chidambaram 27:17
exactly right. So you know, the how to go at governance, what does it mean? So soft skill becomes very critical. So that is where the direction? And I think there's one more I would say is I think it was through I don't know, I'm quoting it right. But it was who was cb cb cb before gotten about what, what I think was Yeah, where they had some really good chart on I still remember it very importantly. And it's very important to do data. So they call it three zones. And I think it was on one axis is like the insights you can give another axis is the kind of data access you have. So what most organisations today have is they call it the zone of Commons, which is basically engagement surveys, that is simple things where you use an HR data known questions you. Then the second is the zone of debate. This is where you use metadata email, like Microsoft Workplace Analytics, you use, like, you know, calendar data, you know, text, daily text listening, daily listening and blog listening, which is happening, right. And the third is what they call the zone of reticence where, you know, no one does that pleased. Very few companies. I don't know, that's the right, in my opinion, are chips inside body and you know, tracking next days mode or deep dive. And I don't think that's my, my humble opinions will be there, and they shouldn't be there. But the zone of debate is becoming more common right now. Right, which is if you have the right, influencing skills, you have the right, ethical governance, if you have, you know what that whole structure when you said you've heard a lot are on ethics and privacy with this particular function, right? That when it becomes more robust, along with a great data ecosystem, then you can get into more this, this whole concept of employee listening will be much cleaner, and more powerful, because you are doing the right thing. And that's a space I think we have not touched you know, it's not like we don't use text data, because we don't even have a governance. So when you and I'm talking about this function as a whole, not at my company, I'm just on every company. Yeah. Once you have proper governance in the ecosystem, then you can leverage and I think that's direction I think it will head and that zone of debate will become more like the zone of comments. So that's another space I would say another five years. So those are some of mine. Thinking of where this field will be headed.
Chris Rainey 29:45
Interesting. Before I let you go I have a question which is something I don't normally ask but it just came to my mind. If because we're going to post this out, right you know, many of your peers are going to see this. It says one question you want to ask your peers and colleagues, what would that be? And then I'm going to post that and share it. With our community. If there's one question you'd like, I'd love to hear everyone's thoughts on this. And we can post it and see what people's feedback are. Because I'd love to see what reactions we get.
Arun Chidambaram 30:19
I think the thing which is coming, it's fuzzy, but it's coming to my head to be what is coming to my head is, I call it the last mile problem. And I think I said with your last podcast, that's what I would want to know, that we HR analytics of people are based in HR is a very output driven activity. So we drumroll on the output. Let me jump to the next one. And I call the last mile problem, because the real deal with the business really cares is from output to outcome. They care about outcome. So my question for my peers and you know, leaders in this space is like, what are they doing? And how do we like we want to get these thoughts. convert that from an outcome driven as people analytics, then an output driven? It's not an analytical only problem. Yeah, so that's where we really anything we do. Its output is important. I'm not saying it's not. But we stopped. That last mile problem is what I want to solve. Yeah, I would love to hear how people are searching like,
Chris Rainey 31:32
yeah, no, I love that. That's such a great. That's yeah. Such a great question. And again, it kind of goes in so many different directions, as well. Very last question. What advice would you give to those, the talent, the leaders that are coming into the space, there will be a parting piece of advice to be found next leaders and HR leaders coming into the PA space?
Arun Chidambaram 31:56
Yeah, as I said, on that part of what a people aren't a leader needs to have is come on the pieces, I will say is always from a team both side. One is this is this game has to cut to be played together. Number one, you need to have balance in this game. Because don't go on one side of track. But all data data data all the time start with data is not as there. But also try to understand the other side, which I've mentioned, have a lot of patience and tolerance, which I said and and have fun with this. Now it's not like and when you summon the leader says data cleaning data is is good. It's there's a reason why so appreciate the experiences of your HR teams. Yeah, they know exactly what they do. So that's kind of what some of the few pieces of advice like tolerance, patience, be a team player. And you know,
Chris Rainey 32:54
nothing else. Or listen, it's always fun having conversations here. And I always appreciate it was Wow. And so many great insights, super excited to share this with our audience. Where can people connect with you? If they want to reach out say, hi, where's the best places
Arun Chidambaram 33:09
still on LinkedIn? My email was straightforward as far as.last@gmail.com
Chris Rainey 33:15
No worries. Well, listen, I'm going to delete your email out so you don't get loads of emails. But connect with Aaron on LinkedIn
Arun Chidambaram 33:24
are much smarter on LinkedIn.
Chris Rainey 33:27
All right. So listen, I wish you all the best until next week. Thanks so much. Right.
Arun Chidambaram 33:31
Thanks for taking thanks