06 April 2021

The Skills Obsession: The Realities of Building a Tech-Enabled Skills Framework

Stacia Garr
Co-founder & Principal Analyst

TL;DR

  • This is the 3rd episode of our podcast: The Skills Obsession.
  • In this episode, Stacia Garr of RedThread and Chris Pirie of LITNW interview Madhura Chakrabarti, Global Head of People Analytics at Syngenta
  • Find out about the challenges Dr. Madhura Chakrabarti faced in creating an innovative cross-company skills framework supported by by a new learning platform implementation
  • What six skills were identified and used as pathways to develop and converted into that framework
  • How might organizations think about challenges as they move forward understanding the businesses needs for skills
  • A special thanks to our sponsor, Workday, for its support of this season!

Listen

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Guests

Madhura Chakrabarti, Global Head, People Analytics at Syngenta

DETAILS

Dr Madhura Chakrabarti is one of our favorite HR thinkers and doers, so we jumped at the chance to hear of the genuinely pioneering work she’s doing for the 29,000 people who work for her employer Syngenta, a leading Swiss-headquartered science-based agtech company that helps millions of farmers round the world grow safe and nutritious food, while taking care of the planet. Despite COVID, in early December Madhura and her small L&D team launched an innovative cross-company skills framework supported by a new learning platform implementation.

This episode is a great chance to hear about the real practical challenges of creating such a framework and how hard it can be to find the right partner to help, as well as the importance of people analytics in general: you’re really going to hear from the HR data and skills coal face here. Making this experience even better: Madhura’s charm, professionalism and fierce intellect. Truly, some great Workplace Stories this week!

Find out more about Madhura and her work at Syngenta here

Connect with her on LinkedIn

Webinar

Workday will host an exclusive live webinar towards the end of the season, where you can meet the Workplace Stories team of Dani, Stacia and Chris, and join in a conversation about the future of skills and skills management. Find out more information and access content at www.workday.com/skills. 

Partner

We're also thrilled to be partnering with Chris Pirie, CEO of Learning Futures Group and voice of the Learning Is the New Work podcast. Check them both out.

Season Sponsor

We are very grateful to Workday for its exclusive sponsorship of this season of the Workplace Stories by RedThread Research podcast. Today, the world is changing faster than ever, and you can meet those changing needs with Workday; its one agile system that enables you to grow and re-skill your workforce. Workday is a financial, HR and planning system for a changing world.  

TRANSCRIPT

Five Key Quotes:

I think it was a stretch or a learning even for myself. Because when we set out, in complete transparency, my mindset was, Oh, I know what they need, so I'll design it, don’t worry about it, so I could have just gone with it—but it's only after that listening exercise, I realized, Oh, actually there are very distinct needs; just because I'm interested in data, not everyone is equally interested in data, and in a similar way. We really have to cater to the user's needs.

The six skills we have identified are data fluency, employee experience, agile, tech savviness, partnering, and customer centricity. We said, let us be pioneers and let us come up with design actual pathways in the platform. So we took our vision of that framework and converted those to actual learning pathways on the platform, and we now have six of these learning pathways: we have many more, but for HR, we have these very six dedicated ones, all built in-house.

We were very clear, especially when we presented it to our HR leadership team, that it shouldn't be a laundry list of these 15 things that we think are important—it has to be realistic, and it also has to be achievable. If you want to upskill yourself in all of these, then 12 to 18 months should be a good enough timeframe.

I think we hit an extremely strong project manager who kind of brought us together. For a year, every Tuesday, 8 to 9.30am, we met as a team to discuss our progress and we used Microsoft teams as our platform to collaborate: we had a whole channel dedicated to it and all of the conversation that happened, all the decks we prepared, everything that we revised and the durations, all of that happened on that single platform.  I don't think how we could have managed it just through email or just through meetings; that platform really helped.

There's a set of metrics that are relevant for the manager, to understand how the team is progressing. Then there's an org level need, where as an organization, we need to understand which corporate functions are really leading the way in learning, or is this a business unit, or if you break it down through different demographic lenses. And then there's a strategic level of, can we connect learning with metrics that Syngenta as a company is poised to deliver.

 

Stacia Garr:
Today, we're speaking with Madhura Chakrabarti; she’s the global head of People, Insights and Analytics at Syngenta.

Madhura Chakrabarti:
I think it was a stretch or a learning even for myself, because when we set out, in complete transparency, my mindset was, Oh, I know what they need, so I'll design it, don’t worry about it. So I could have just gone with it and designed, but it's only after that listening exercise, I realized, Oh, actually there are very distinct needs–and just because I'm interested in data, not everyone is equally interested in data and in a similar way. So we really have to cater to the user's needs.

Stacia Garr:
Madeira is one of the smartest people we know she brings in academic perspective—she’s a PhD, she also has an extensive practitioner perspective, and has married those together in her recent work at Syngenta.

Madhura Chakrabarti:
This is Madhura Chakrabarti, I am the global head of People, Insights and Analytics at Syngenta, based out of Basel, Switzerland.

Stacia Garr:
So Madeira, welcome to Workplace Stories, our RedThread Research podcast; thanks so much for your time and for sharing your insights with our audience today. I'm obviously excited to have you on here—we work together, and it's so cool to get a chance to hear what you're doing today.

Madhura Chakrabarti:
Glad to be here, and honored to be here; thanks, Stacia.

Stacia Garr:
We're going to start with some quick questions to introduce you and your work practice to our listeners, and then we're going to go deeper on some questions. We really want to hear your perspective on here about your experience.

Madhura Chakrabarti:
Sounds good!

Chris Pirie:
Madhura, can you give us a quick overview of Syngenta—its mission, and its purpose?

Madhura Chakrabarti:
So Syngenta is a 29,000-people company, headquartered in Basel, Switzerland. We recently became Syngenta Group as a result of becoming a conglomerate of other companies, and the mission, or what the company does, is really an expert in crop science and seeds. And it provides digital solutions to farmers across the world so that they can make better decisions in their day-to-day lives.

Chris Pirie:
And what is the work that you do? What's your job title and how would you describe what a typical day looks like for you, if there is such a thing?

Madhura Chakrabarti:
Good question. I lead the global People Insights and Analytics team, and in very simple terms, because it's a fairly rapid section, our mission is really to understand how we can use data and analytics to make better talent decisions and talent related business decisions. It's a fairly new function; we’ve been roughly here for two, two and a half years. We are still trying to build it out.

Chris Pirie:
And what are the sort of forces at work on Syngenta, and you and your role as well? What problems are you trying to solve?

Madhura Chakrabarti:
Short answer is many! But at the start of our journey, we actually had identified three main pillars, the first pillar being strengthening the core of People Analytics, and that really entails things like how do we upskill ourselves—we are a seven-person team, and constantly be at par, be abreast of the latest and the greatest, and technically also constantly upskill ourselves.

The second pillar is around how we scale People Analytics and some of those sub-points or sub-bullets underneath that is the HR upscaling work that we'll be discussing today. The other big part in that second pillar is coming up with a Data Lake; a seven people team cannot really serve the entire company, you need to have a scaled mechanism. So these are kind of the two things that we are focusing on in the second pillar.

And the third pillar is really around embedding people, analytics and business and HR topics, so this is where our actual work like analytics in DNI, or doing a sales effectiveness study, or doing an org network analysis study, all of that comes in underneath this pillar. And it's contingent upon what the business needs and sometimes what HR needs

Chris Pirie:
And then since we're talking about skills generally in this season and specifically today, what are your skills—what are the skills that you need and your team needs to do your work and, and how did you acquire them?

Madhura Chakrabarti:
I think I would broadly break them up into two. One is more around technical skills, and that's more like table stakes; you cannot do it anything else if you don't have them. And that's more around pure analytics, statistics, construct measurement, survey building, I think I'll go back to mainly graduate school, but of course now LinkedIn Learning and other learning platforms to keep reinventing and re-brushing those skills. The other bucket is really around influencing others and stakeholder management that I feel like I've picked up on the way in the journey by working and just being in different roles and making some career moves. That's how I've picked them up.

Stacia Garr:
So one of the things we've noticed in this podcast series, which we're calling The Skills Obsession, is that there are kind of two groups that are obsessed: one is the learning folks who have made up quite a bit of this podcast series, but then there's also our friends, the people analytics folks. And one of the things I was most excited about with your story is you're actually bringing them together: you are a people analytics leader who's been focused very heavily on the learning aspect of this. So can you give folks a sense of the recent initiative that you focused on with launching a new learning experience platform combined with that HR capabilities initiative?

Madhura Chakrabarti:
Sure. So this works spanned quite a bit. I would say almost like 18 months from the very start of it to where we are today. And it really happened in two main categories. One was identifying what should HR as a function be upskilling itself on, so that was a project or an initiative by itself. And that started somewhere around the middle of 2019. We took the first six months—we gathered, we did an agile project team that came together across the globe within HR and determined what are those six capabilities? We actually looked at external research in past internal initiatives, we gathered some quick employee voices and came up with a list of, I think, 25, and then shortlisted and came to six at the end. And we shortlisted based on the fact that it needs to be fit for purpose, right? So all of a sudden, if I say 'AI in HR is important,’ there are a lot of other things that we as a company need to do before we go and start working on AI, right?

So it needs to make sense for Syngenta as a company, HR, as a function—so fit for purpose applicable to all roles in HR, because we didn't really want to go down the route of here are five, four for kanban and here are two for HRBP. So we wanted to have a generic set of six that will apply to the entire function.

And then you also talked about relevance in the next three to five years: you don't want something that might be a hot topic 10 years from now, and at the same time, you don't want something that's really hot right now to somewhere that will still be relevant in the next three to five years. So those are kind of the parameters we use to bring it down to six. And the six were data fluency, the part that I led, then employee experience, agile, tech savviness, partnering, and customer centricity. The first phase of the project, which was around six months, was around determining the skills. And then the next one year we actually spent, or almost eight months or nine months, to develop the framework. So if we were to design something around data fluency, what would that mean?

We did quite a bit of product testing around that, so after we developed the framework, got feedback from a variety of sources, we then designed the pathways. And at the same time—this is where it coincided with the larger learning experience platform launch in the company—we said, let us be pioneers and let us come up with design actual pathways in the platform. So we took our vision of that framework and converted those to actual learning pathways on the platform. And we now have six of these learning pathways: we have many more, but for HR, we have these very six dedicated ones, all built in-house.

Stacia Garr:
Great. So I want to start or dig in a little bit deeper on that first phase around kind of determining those skills. So did you all do any assessment of the level of those skills or capabilities in the organization today when you were making that decision around which, you said a list of 25 to a short list of, of six. So kind of what, what was the baselining and did that influence where you ultimately landed?

Madhura Chakrabarti:
The short answer I would say is we didn't do a survey or an assessment per se for employees, because we also had to always balance it out with other surveys that are going on, and do we really want this to have employees answer, way too much service, right? So there's always that reality that you have to juggle, right?

So I didn't do that, but having said that there definitely, I would say data, not so much quantitative data, but data from what worked in the past. So there were many models that were initiated or launched in the past that didn't quite work or there was feedback around why certain things stuck with the company and certain things never really stuck with people.

So we have that. We also had some external review of the HR function, and there was a lot of qualitative and quantitative data around an external party looking at our function and seeing what are our strengths and what are some of the things that we need to work on.

So we have that data and that very in-depth review from that external party. And then it wasn't a skill assessment, but we did some quick listening exercises where we asked people, what does 'HR 2025’ mean to you, and what are some of the skills that you think we need to develop that we don't have today? So we gathered some of those responses and there were also a bunch of quick polls that we did at various forums. We have something called the ‘One HR Week,’ which is where the entire function comes together virtually, and we have a ton of initiatives around HR and people and development for that whole week. So we gathered some quick data from those different sessions as well around skills.

Stacia Garr:
That absolutely makes sense. And then you mentioned you developed this framework: can you explain to us a little bit more in a detailed way, what that framework was?

Madhura Chakrabarti:
Sure. So what we did was, when we were in a position when, once we knew these are the six that we have to work on, and then we identified who's going to be leading, we decided we'll do something called a Yam Jam, which is a Yammer jam, for each of these. Actually we did it for four out of the six capabilities, and we gathered people, we did different sessions and we had specific questions, so two minutes each and everybody had to write in, we had almost 200 to 300 people in aggregate across all sessions give us feedback. /span>

So we gathered a lot of data. For example, for data fluency. I asked the question, what does data fluency mean to you? right, and gathered that feedback. There was another question around, if you were to learn data fluency, what skills would you learn, right, and why would you learn them? So things like that, and what really emerged similar things emerged for other capabilities as well, but I'll focus on the data fluency part as there were three distinct needs of users. One was, I want to understand the data enough so that I want to read the dashboards, I can influence decisions. I can talk about it, I can, I can add value to conversations that are happening about talent, but I don't want to dig my hands dirty or, you know, I don't want to go too deep. So they were very clear, like I really want to know data enough.

So that was one bucket. The second bucket was people who wanted to go a bit deep, but they were, we don't want to do PhDs. So it just makes sure that it's not too deep, but we definitely want to understand what are the data sources we can pull? How can we do some quick analysis to answer a question? So it was almost like a deeper level of the first persona. And the third was where people and to be completely transparent, there weren't a whole lot of them and I almost force-fed them a little bit, that these were people who wanted to go, so people like us in order to develop the people analytics team and make it sustainable. You want technical people: these are people who want to aspire to become data scientists or go deep along with having consulting skills. So it's a very niche skill and the smallest group of all, but those were the three user types or user needs that emerged. And then what we decided was we needed different pathways for these different people. So we went ahead and did a lot of external exercises, listening exercises, where we looked at data fluency: I think I spoke to seven different companies, just understanding what they have done. And there were some really good ideas that came up as to what has made them successful in launching these programs.

So we took those, the internal needs, and then we put together the framework around, let's say for the first set of user needs or first persona, if you were to call it, the name of the person, I think we call them the information consumer—all they want to do is to consume information, not to not do too much of analytics. So for them, we came up with a series of not activities, but it's a mix of, it could be LinkedIn modules talking about why is data fluency important mixed with something like an escape room exercise, where it is about understanding how to use data, how to differentiate anecdotal data from actual data: so more around fundamentals of analytical thinking, and how do you bring that thinking to the table?

Those were just two examples, but around six to seven concrete activities or learning activities that you could be doing, which will address your need for that particular bucket of the first user need or the first persona. And we did that for the second persona. And then we did that for the third persona as well.

Stacia Garr:
Out of curiosity, what did you name those other two personas?

Madhura Chakrabarti:
The third one we named the scientist practitioner. And the second one, I think we are still in the process of finalizing it; we don't have that, but the first and the third are finalized. It was a bit of an exercise.

Stacia Garr:
Well, what I love about what you shared there is that it's something that could apply to any competency, right, or capability; so kind of this bigger picture group, understanding the personas within what these people need to learn and then designing the learning pathways around what their particular needs are that's just replicable across, across anything that we would do.

Madhura Chakrabarti:
Yeah, and that's really important. I think it was a stretch or a learning even for myself. Because when we set out, in complete transparency, my mindset was, Oh, I know what they need, so I'll design it, don’t worry about it, so I could have just gone with it—but it's only after that listening exercise, I realized, Oh, actually there are very distinct needs; just because I'm interested in data, not everyone is equally interested in data, and in a similar way. We really have to cater to the user's needs.

Stacia Garr:
So it's not just the absence of the skills or the skills that need to develop, but actually the needs that they have—and within those needs also an underlying motivation that they have to acquire those skills.

Madhura Chakrabarti:
Absolutely.

Chris Pirie:
I love the design thinking approach of being customer-centric at the beginning; that’s interesting. I also wonder why six, was that a constraint that you gave yourself? Often good design comes from constraint. And I think a lot of our conversations around these skills and skills frameworks for me revolves around the appropriate level of granularity. Can you talk a little bit about why you chose six?

Madhura Chakrabarti:
I don't think there's any magic to that number, but we were very clear, especially when we presented it to our HR leadership team, we were very clear that it shouldn't be a laundry list of, you know, here's these 15 things that we think are important—it has to be realistic and it also has to be achievable. If you want to upskill yourself in all of these, then whatever 12 to 18 months, should be a good enough timeframe.

So I think that was our main lens to look at it. I think we did come to seven, but then two of them could have been easily consolidated. So that's some strong feedback we got from our leadership team. So we then ended up consolidating.

And as you can imagine, a lot of these skills are also overlapping, right? So digital technical savviness. I mean, do you really want to keep it different or is that… I mean, even within the six, to be honest, there's so much data fluency needed in being agile or an employee experience, but there's a lot that you can actually combine. So there was a consolidation exercise, for sure.

Stacia Garr:
And you mentioned, you had gotten feedback to identify these three different personas. Once you had done that and had started working on the learning pathways, did you all also get feedback at that point?

Madhura Chakrabarti:
Yes. So we did a set of, we call it product testing, when just the framework was done, and it was pretty intense because we recorded all six of the capability leads. We recorded 30 20-minute videos introducing ourselves, why this is important and then the entire framework, and then we had a bunch of 20 to 30 product testers across the globe that actually went through all of those videos and then we had sessions where we came together and they gave us feedback, and we did some quick NPS surveys as well. Like would you recommend this framework to others or would you recommend the skill to others? So a lot of good feedback came from there saying, you know, this is good. This is not going to work for me.

One of the things that people said was there's a lot of commonalities among the six capabilities, so if there's a way to guide me to something else while you're talking about a particular skill, that would be really helpful. So if you're talking about agility and hypothesis building is really important, then tie it to the data fluency pathway because that's how you garner interest in each other.

So a lot of feedback happened there—that was one round of product testing, and then when we actually designed the actual pathway on the platform, we did another round of product testing as well. But to be very honest, I think we got a lot of interest in the first phase; by the time we launched it already coincided with a couple of other big initiatives, so the amount of feedback we received on the platform when we road-tested it was less. But at the same time, now we have launched it as of December 3rd, we launched everything officially across the globe. We are just going to look at the feedback now what happens.

Stacia Garr: Right.

Madhura Chakrabarti:
So, fingers crossed!

Stacia Garr:
I understand that you said that it coincided with a couple of other big initiatives, but it also may be that you got a lot of the big issues out of the way early in the design.

Madhura Chakrabarti:
Absolutely, but what I would also tell others were on a similar journey is putting it on the platform is, is a learning by itself. Like when I was literally putting it on the platform, the kind of experiences or the kind of notes I had to put in—because it's not a bunch of LinkedIn modules, right? It has certain activities where you need to sign up; it has a community of practice that you can sign up for, so it's a mixed methods thing. So for some of them, let's say you're scrolling down, you've just gone through a few videos, and then all of a sudden it's an activity that you have to sign up for, but the actual activity will happen in the site that you are in. So how do you change that mindset that, well, this is not a module: you just need to sign up on this activity sheet so that your local HRBP can do this, right?

So because there was such a variety of things that we offered within each pathway, the actual platform experience is also important. And to gather feedback is also very crucial.

Stacia Garr:
Interesting. I want to maybe step back a little bit. So you were the global leader of People Analytics. How did you become involved in this? Like who was it led by an in, why were you a part of this?

Madhura Chakrabarti:
The first phase I was asked to lead the entire initiative, just identifying what the skills are going to be. It was very organic, let's have a project team together, let's have somebody lead it, I think I was in general passionate about the topic. So, it happened very organically and it happened really well.

The second phase, we realized that we need experts in each of these six fields or in these six capabilities, so let's have one person lead one capability and let's have one overall project manager lead the entire thing. So that's how we came together across HR. And I would say it's a mix; three of the six capabilities. So employee experience, agile and data fluency, we had people who actually lead it in their day jobs as well, so it only made sense for them to read these capabilities. The other six, I think it was more of people who were passionate about the topic and, of course, who had the capability to lead it. So it was a mix of your role determining who leads it, plus your passion. But of course, we had very strong sponsorship from our CHRO, who thought this is the top.

Stacia Garr:
One of the things we all know can be difficult is creating that alignment across these different areas of HR and these different teams and you even called out that one of the most important things was making sure there was a connection from one of these sets of capabilities to something else. I could see with there being six teams, it being hard to kind of maintain that connection. So what did you do in terms of maintaining that alignment and communication across these different groups so that you could kind of create this cohesive whole offering for folks?

Madhura Chakrabarti:
I think we hit an extremely strong project manager who kind of brought us together. I know it seems obvious, but I think her tenacity and her skills—kudos to her, every Tuesday, 8 to 9 or 8 to 9.30am was our meeting, and we had people from across the globe, from New Zealand to us, covering the entire globe. Imagine the difficulty of bringing everyone together, but we all pulled it together. So for a year, every Tuesday, 8 to 9.30am, we met as a team to discuss our progress and then we used Microsoft teams as our platform to collaborate: we had a whole channel dedicated to it and all of the conversation that happened, all the decks we prepared, everything that we revised and the durations, all of that happened on that single platform. So I don't think how we could have managed it just through email or just through, you know, meetings. That platform really helped.

Stacia Garr:
Now kind of turning, you said he launched it on December 3rd: what are you thinking in terms of the measurement? So how are you going to know if this has been successful?

Madhura Chakrabarti:
A couple of things. One is, of course we want to look at the platform, data, the metrics that come with it, so how many people have viewed it? How many people have started a more do which module is more popular, which mode is more popular—are people reading documents or are people watching videos more or are they signing up for activities? So we already have a framework that we have prepared for each of our pathways that we want to track the data; hopefully it’ll go up and not down over time.

We also have developed a dashboard, not just for the HR capabilities, but for the entire learning platform, where at an org level you get to see what are the top most skills that people are aspiring for, what are they signing up for? And you kind of get the business unit wide view or, you know, gender or other demographic view of the data. So that's another piece.

And then to be very honest, there's no death of qualitative feedback. So people will write an email and say, Hey, I couldn't sign up or what's this happening? Or have you considered this resource or that resource? So not everything, but we are trying to capture some of those qualitative feedback as well. So between the dashboard, the platform data and the qualitative feedback, that's our first approach, but I'm sure it will evolve and we have to put in some more. But let's see how we progress in the next two to three months.

Stacia Garr:
The dashboard is really interesting, because it basically is your way of enabling leaders to keep a pulse on, are we improving the skills of that? We've said we want to go out and improve. Was that something you envisioned doing from the beginning?

Madhura Chakrabarti:
We did actually. So what happened was, as I said, the HR capabilities was one part, or one stream; the other much bigger stream was launching the learning platform for the entire company, right? So my team was involved in one of the levers that they call the measurement lever. Of course, the question that was posed to us was how do we measure learning? How do we know this is working? So we came up with this framework of, there’s a bunch of metrics that we can track at an individual level. So me, as an individual learner, I want to know, when do I learn the best or how many courses have I taken in the last one week? Or is there a pattern, do I learn during a particular time of day, or things like that.

Then there's a set of metrics that are relevant for the manager, right, to understand how the team is progressing. Then there's an org level need, where as an organization, we need to understand which corporate functions are really leading the way in learning, or is this a business unit, or if you break it down through different demographic lenses.

And then there's a strategic level of, can we connect learning with metrics that Syngenta as a company is poised to deliver. So for high level metrics like anything to do with crop science or building a better life for farmers, are there things that we can correlate with learning? That's a very high goal—I don't think we are there yet. We are very much in the lower ranks of the pyramid right now. So individual, manager and organization-wide metrics. So in that organization wide leg, we had envisioned that dashboard—that this is what it'll cater to, and this is why we need the dashboard to look at them or look at an org view.

Now, one thing I will say is that we had a lot of debate for the manager rank of the pyramid, because there's a school of thought that believes, yes, managers should have access, because ultimately you want to see where your team is, and how your team is progressing. But then there's also another strong school of thought that said, we don't want managers to know—I don't want my manager to know what courses I took yesterday. So, you know, there was a lot of debate that way. We are not currently feeding anything to the manager, but so right now it's at an individual level, but it might be again, a journey.

Stacia Garr:
And I'm sure also there's a level of comfort that needs to happen with folks in getting this type of data and understanding what might go where, and, and the rest of it, because it may come eventually in place, and I just take whatever I want and my manager can do whatever they want with it, but it doesn't matter.

What I love about what you've shared—lots of things—but one thing is, you know, we hear a lot in our space about the democratization of data, about making it widely available, but we don't see a lot of practical examples. And I think what you've shared is a very practical example of how you've thought through how this data could be useful at the individual level. Certainly some thought on, on how it could be useful at the manager level, even though it's not available, but the thinking there. And kind of that pyramid that you mentioned, I think is really a powerful framework for other people to think through, as they're thinking about their data efforts and data dashboards and the like.

Madhura Chakrabarti:
Right, right. I think the other thing I will mention is as we went through that process, we also realized that ideally, it all sounds really good that you have you've thought through it all, but then there are realities around what can the platform deliver, right? So it's not magic that I want 17 metrics around in that individual layer and all 17 are available on the platform. And if it's not available on the platform, are you actually going to feed it individually to the individual? No. right; I mean, you can't do that.

So then we had to tweak our approach to say, what could be an MVP? So let's say we have identified 16 or whatever, 15 things that we want to measure at an individual level; maybe only five or seven of them are available in the platform or are kind of a ballpark available in the platform. So let's have seven as an MVP and let's do the rest in as phase two, so longer term. So then we divided each of those layers; what’s an MVP, what's a nice to have, or can come in future. And that was a good reality check, because otherwise we were on this spree of anything we think will happen and we can make all of this work, but that's not quite it, because you always are constrained with what the technology can provide.

Stacia Garr:
And I know that with some of the particular learning platforms the concept of measurement and kind of the measurement that we bring to some other aspects of our people world—and Chris might hate me for saying this, but they seem to be a bit behind. You know, we, I think that there's kind of been the learning spaces long-term, you know, focus on smile sheets and the like, and the rigor of what we've seen in some of the other aspects of people analytics isn't there.

So can you talk to me a little bit about what that conversation looked like for you all with your vendor ? Hey, you know, this is where we want to go, we can use the things you're providing us, but how did you approach that? What was their receptivity to that conversation? And what do you kind of see moving forward?

Madhura Chakrabarti:
I'll try to be as agnostic as possible, but I'll talk in generic terms. So some vendors were very, very rigid about what they have provided or what they will provide; it was a pretty difficult conversation to bring them from what their product to say, actually, our needs are a little different and we have identified these needs—only two of them are kind of matching with what you have, but what is your vision around the rest?

And they just kept going back to what they have, right? Those were actually part of our selection criteria as well. A lot of these conversations happened before we finalized the vendor, whereas a couple of others were definitely much more open, and they also gave us concrete examples of how they shifted their roadmap based on their client feedback. So testing that before finalizing the vendor is really important. And measurement was just one lever, right? There were other levels as well. So there was a process, there was integration with other stuff, things like that. So all of us were part of that discussion, and of course we had to keep it short. That's really important.

Chris Pirie:
As the L&D guy in the conversation. I couldn't agree more that we still seem to be on very foundational activity, tracking-type data and metrics in the learning space and though we're all very, very desperate and anxious to get to the business impact side of things, it feels like we're a long way.

Do you have any examples of things that you wanted to do? The vendors that you spoke to found it difficult to respond to or to your data scientist hat on,and tell me what you wish they could have brought to the table for you?

Madhura Chakrabarti:
I think the biggest one was in that final layer where we had business outcomes, you know, the top of the pyramid—and that's where I said that we are yet to go there, that’s where we were hoping that we'll get much more insight from the vendors or actual examples, but we didn't quite. So for example, one of the things that I know was pretty ambitious of us, but we wanted to say, ultimately, it helps us sell better to farmers because of the courses that we have taken, right? And I'm putting it in very simplistic terms, but if I take five agronomy courses, do I, as an organization sell more?

So any relationship that we could establish between learning and selling more, or influencing farmers' decisions more, we didn't end up getting anything there. And it's a hard problem—I don't want to say they can’t solve it and look, we have done it, we have done it either. But so I do want to recognize it's a very hard problem and it's so many factors involved, ultimately in the selling decision that you can't really pinpoint to learning, but if there was some way to directly establish relationships between the final outcomes that we're interested in as an organization and learning, that would be a deal breaker, I would say.</span

Stacia Garr:
Just to dive into the data side of that a little bit more, were the vendors able to actually bring in some of that data? Because obviously you'd have to bring in for this example of your sales data for different groups and then be able to kind of slice and dice based on what functions people were in, or what region or whatever, and whether they took the courses. So were they unable to bring in that data, or were they unable to share that so that you all could do that analysis yourself, see, during your Data Lake or in some external tools?

Madhura Chakrabarti:
I think with regard to those higher level questions about the business outcome relationship, they were just unable to bring in or show us examples. But with others, I think it was more around, yes, we have the data, but right now it's not on the platform, but yes, we think about that. So it was more yes, we can implement it if you want, or we can do it, but right now it’s we are not able to show it on the platform.

But, you know, Stacia, the other thing I'll mention is we also had a learning ‘aha!’ moment here. We thought the more the metrics, the merrier, right. But the vendors actually told us, and some of the external learning we did when we spoke to other companies was, t the end of the day, give the individual just three metrics, and that's what you can drive the maximum impact—don’t bombard them with like 15 metrics that they don't know what to do at every day. If it's changing at some point, I'll be like, okay, I don't care.

So try to consolidate and give them the bare minimum or two to three that you think are important. That way, certain vendors also helped change our thought process. Just because we can think of 15 doesn't mean we have to give 15, right: think about what really matters. And at the end of the day, or rather end of the week, what does an individual want to know about his or her learning path?

Stacia Garr:
I'd like to kind of lift up and think about what you're going to be doing moving forward, kind of taking this experience as an initial example of what could potentially be done. Can you talk about your vision for how people analytics could help with, skills, identification, or verification or talent redeployment in the future?

Madhura Chakrabarti:
Yeah, I think again, this could be an hour discussion by itself. A couple of thoughts: one is especially given the experience we had employee listening plays such an important role, understanding your population for which you're building the learning product is critical that we saw our personas and our user needs would not have come up if we had not listened. Right?

So people analytics, and again, to me, people analytics, employee experience, they go hand in hand—it’s not really a different team or different skill set But we can play a really important role in that process of whatever listening we do internally to understand user needs, beat quantitative or qualitative, to gather that data, to mine that data, to help that that's where we come in quite a bit. The second is the sources from which we get information about skills. That's just going to exponentially increase over the years. Internal listening is only one source. You also have, I'm sure the HR employee tracking system, every company has, there’s some amount of information there. Then there is, there are professional networking sites that you can get information on, there are learning platforms that you can get information on. So how do you connect the disparate data sources and come with a consolidated view of what are the skill gaps? What are the skills people are aspiring for? Managing those disparate data sources, analyzing that data. That's where people analytics can play a key role, and of course the end part of it, which is when you have launched a pathway or a learning platform, how do you measure that people are actually learning? So that's the third piece where people analytics is critical, and if organizations don't have people analytics teams in that space, then that would be a red flag. You absolutely need to involve the team there.

Stacia Garr:
You mentioned kind of this exploding or exponentially increasing, I think was the right word, sorts of data around skills. I can see that as potentially an opportunity, but it's also a challenge. So we'd love to hear kind of your thoughts on those different data sources and how folks might want to approach or think about that challenge as they're moving forward.

Madhura Chakrabarti:
The first thing I would say is really, what does the business need? Try to understand the business needs, and where the company is going. So I'm sure digital is top of everyone's mind, and everyone's going through a digital transformation—but what exactly is digital, right? I mean, is it data analytics skills, or is it becoming more technology-savvy? Like for us, farming equipment or farming digitization or technology that supports satellite data.

So trying to understand the business needs is really important—and what's the need, do we really need to up-skill our own population to make it prepared for the next five years? Or, can we do it with interim solutions? There are a lot of vendors these days that look at the current talent pool, the external talent pool, the gig economy talent pool, and a couple of others and they bring together a project team that will suffice for a particular project that you don't have the skills for, right?

We haven't quite implemented that, but we have actually looked into some of those solutions because some of the business needs are very, we need to put a team together next week to start on this project, but we don't really have these skills. So we need to think about what the company needs long-term, but not forget that there are many short-term and medium-term solutions available today, especially given the gig economy structure that we can avail of. That would be my call-out given some of the business problems that have come to my team in the past 1, 1.5 years.

Stacia Garr:
When I ask you, you've mentioned kind of being involved with the broader HR function and specifically with learning as you've done this work. When you think about addressing skills broadly, who else do you think needs to be involved in that conversation?

Madhura Chakrabarti:
Meaning, outside of HR?

Stacia Garr:
There could be other groups within HR, I could tell you who I might be thinking of, but I don't want to influence you, but I think both within HR and outside HR.

Madhura Chakrabarti:
So I think beyond learning and analytics, I see HR BPs as a key role, because their insight into what the business is thinking, what are the kinds of daily conversations that happen on the ground: that really helps give us a picture of what are some of the skills that we need to upskill ourselves on, so that we can have a more informed conversation with the businesses.

And also, a lot of companies, their HRBP population tends to be very tactical. So how do we move to being a more strategic input into the business rather than very operational? So, that itself was a good input for us to kind of look at what skills we need to look at in order to change.

Other than that, I think IT in general partnership with them, especially in terms of learning platforms or what do we have, where do we want to go, how can we integrate? That's really important. And I think once we have the solution, or maybe not once we have, but throughout, we need the business leaders at least to sponsor or to support. Some of the best conversations we have had was when we did these learning workshops; as a result of that learning platform launch our very visible business leaders came and addressed us and just talked about learning, and what does learning mean? And we ask them questions like, if you want to have one outcome of learning, what will it be? One of the things that repeatedly came from these business leaders was, I should be able to find what I want to learn easily—that was at the top of their mind.

The other thing, which to me resonated really well with me was people talked about learning can be just going to an orchestra or opera and listening. And to me, that's learning. So, you know, how do you look at learning and the non-work space as well? And people actually consider that very much a learning, but then that has its own measurement problems, because when we were working on the measurement, How do you actually metricize, going to the museum because that's learning?

So that has its own issues. But that was one of our eye-opening moments—when we heard business leaders talk about learning. That also really energized us to think about it in different ways. So that's important to make it successful.

Chris Pirie:
When I look at what's going on in the learning world today, there's two predominant sets of activities. One is very data-driven—the kind of work that you've been doing. How do we codify, how do we automate, how do we track? And then there's another almost, I don't know whether it's on a scale, but at the other end there's how do we build a culture? How do I create a culture where learning in my organization is something that is supported and good and encouraged. As a data person, what do you think about the culture side of things?

Madhura Chakrabarti:
Yeah. I mean, that's something we have been really thinking about hard, because this is not an easy problem to solve. And it's almost in every deck that we have created a learning culture, right? I mean, that's there.

I think the launch and the marketing of it is really important—it goes a long way in creating that culture So I would really have almost like a marketing team associated with the launch of the product, and treat it like any other product in the market. And we can see that the stronger the launch, greater the uptake in those areas, so creating that learning culture.

And then also, I think it goes down to the individual leader a lot. So I, as a manager of seven people, how much do I emphasize on development? I could have my own ways of emphasizing—in my team, we have this two-hour session, monthly development sessions. We just talk about one topic that we have either read or something that we need to upskill ourselves, and somebody presents. So I think leaders, it's up to them to create that culture within their team. So that's more of a bottom-up approach and businesses launching it, or business leaders launching it, is more of a top down approach. So between those two, it could be powerful in creating the culture. And constantly measuring it—don’t forget measurement.

Chris Pirie:
At Microsoft, what we used to say is, if it doesn't get measured, it doesn’t get done. That might be the culture link.

Madhura Chakrabarti:
Yeah, exactly. And you're talking to a people analytics person, so yes, everything is measurement.

Stacia Garr:
So just starting to wrap up; are there any organizations that you admire in terms of how they're approaching skills today—folks who you've talked to that you think they're doing?

Madhura Chakrabarti:
Some interesting work? I think it's the recency effect, Chris, because you mentioned Microsoft, I think some of the work they're doing, and I may be a little biased with my conversations that happened around the HR data fluency skills, so maybe not overall, but I know they have done some really good work and they have a team dedicated to it: there’s some very dedicated efforts around it.

Lloyd's Bank was another organization that we spoke to and they have done some really good work, especially the persona idea, even though their personas are completely different, but that idea actually came from my conversation with them where they had certain personas. And they made it a very fun way of identifying with the persona, and therefore going and learning certain skills because you are that persona.

I was pretty impressed with their work. And I think Unilever in general is always the leader in this, mostly because of all the stuff that I've read, I haven't personally talked to them.

Stacia Garr:
What else should we have asked you about that we didn’t?

Madhura Chakrabarti:
You know, with any initiative when it happens and when it's done, it feels like, Oh, it was wonderful, and this was all planned and everything happened as per plan. But I just want to give people a very realistic picture; there are many times where we just didn't know what we are doing, or if this is even going to launch—or when we had the framework, we had wonderful PowerPoint decks, but we didn't know how reality would look like, but it just so happened that the platform was launching at the same time.

But if that had not launched, I don't know if we would be here today with actual learning pathways, so there are a lot of coincidences. There are a lot of points in time where we didn't know what the next step was and it could have completely fallen flat and not gone anywhere.
So, you know, just keep at it and you just have to make things work as you go; it’s not always very well planned out. A year ago, we didn't know that we will be in this position today where we actually have launched pathways for six of our capabilities.

Stacia Garr:
You've shared a lot of really great information. Some folks might want to follow up and have some other questions: how can people connect with you and your work?

Madhura Chakrabarti:
Definitely, you can connect with me on LinkedIn; that would be great.

Stacia Garr:
And then wrapping up, final question: we’ve done quite a bit of work on purpose over the last year, and so we like to ask all of our podcast guests a question about their personal purpose—to really just want to understand why do you do what you do? Why do you do the work you do?

Madhura Chakrabarti:
Oh, that's pretty deep. I think there's this inherent need in me personally—that the need for connection with people, that's very strong. But at the same time, I think I also have an affinity for numbers, and so I think part of me is always asking but what's the numbers, what's the evidence, and what's how can you break it down, and how do you know this is true? How do you know this is not true?

So this fascination for facts and fascination for people, I think that's where I found them coming together and people analytics. And that's what I do, and ultimately, if this can make leaders make better decisions about people—if this can help an employee know what to do next in his or her career or what to learn next, you're actually improving somebody's life in the organization.

Stacia Garr:
So you're not just a scientist-practitioner, you're a scientist-humanist, if you will?

Madhura Chakrabarti:
That’s a lovely title.

Chris Pirie:
That should be a job title!

Stacia Garr:
It should be.

Madhura Chakrabarti:
Maybe that's what we call our third persona—we’ll tweak it.

Stacia Garr:
Well, thank you, Madhura, this was just wonderful; we appreciate all the really concrete examples and just sharing the details, and helping people see there's no one pathway to getting here, but it is possible.

Madhura Chakrabarti:
I really enjoyed the conversation as always, Stacia; thank you, Chris. Good to know you.

Chris Pirie:
Yeah, likewise—thanks so much!

Chris Pirie:
We are very grateful to Workday for their exclusive sponsorship of this first season of the RedThread Research podcast. Today, the world is changing faster than ever, and you can meet those changing needs with Workday; it’s one agile system that enables you to grow and re-skill your workforce. Workday is a financial, HR and planning system for a changing world.

Workday will also host an exclusive live webinar towards the end of the season where you can meet the team Dani, Stacia and myself, and join in a conversation about the future of skills and skills management. You can find out more information and access exclusive content at www.workday.com/skills.

ABOUT THE AUTHOR

Stacia Garr

Stacia is a Co-founder and Principal Analyst for RedThread Research and focuses on employee engagement/experience, leadership, DE&I, people analytics, and HR technology. A frequent speaker and writer, her work has been featured in Fortune, Forbes, The New York Times, and The Wall Street Journal as well as in numerous HR trade publications. She has been listed as a Top 100 influencer in HR Technology and in D&I. Stacia has an MBA from the University of California, Berkeley, and a master’s degree from the London School of Economics.

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