This week’s podcast guest is Anshul Sheopuri, Vice-President and Chief Technology Officer at IBM Workforce, talking about one of the key skills for HR professionals at IBM, data literacy. As both a business and technical executive at IBM, working in HR, Anshul shares his views with us on both the strategic and technological future of HR, with a particular focus on the application of artificial intelligence across the field. 

Throughout this episode Anshul and I discuss:

• The five pillars of trustworthy AI and HR: transparency, explainability, fairness, robustness, and privacy, as well as examples of trustworthy AI in action

• The applications of AI in HR, that Anshul is most excited about, including the multifaceted recommendation engine that connects IBMers to learning, mentors, job roles, and career opportunities, with skills data as the silver thread that holds it all together

• Anshul’s top tips for HR and people analytics professionals, to deploy AI ethically

• How to develop a data-driven culture at IBM by celebrating individuals who embody that culture, investing in the right tools and technology to democratise data across the organisation and making it clear which skills are needed, including data literacy

Support for this podcast is brought to you by Huler, you can learn more by visiting huler.io.

You can listen to this week’s episode below, or by using your podcast app of choice, just click the corresponding image to get access via the podcast website here.

Interview Transcript

David Green: Today, I am delighted to welcome Anshul Sheopuri, Vice President and Chief Technology Officer for IBM Workforce, to The Digital HR Leaders Podcast. Anshul, it is great to have you on the show, thank you for joining us. Can you provide listeners with a brief introduction to you and your role at IBM?  

Anshul Sheopuri: Sure, David. I'm so excited to be here with you, thank you for having me. I have the pleasure and the privilege of leading our work around the digital platform for our IBM workforce. And what this really means is, we all know data as the critical component for intelligent decision making and it is all about high quality decisions for the IBMer, for the manager, for candidates as they apply for jobs at IBM, as well irresistible experiences, consumer grade experiences that you have while you watch a movie online or hail a cab, that is the kind of experience we want to give you as a candidate or as an IBMer. 

Those two things together, the experience and the quality of the decision, is what my job is all about. 

David Green: I think your role encompasses a lot of areas. I think it encompasses people analytics, all the technology that you build and use for IBMers and, as you said, for those applying for IBM as well, is that right? 

Anshul Sheopuri: That is right. So it is a broad role, which is what makes it so exciting. I am also an IBM Distinguished Engineer, and as your listeners might be interested to hear, it is a technical executive role at IBM and it is a reflection of the times that we are in. Who would have thought a decade ago, that somebody in HR, would be an IBM distinguished engineer or a technical executive and I have the privilege of having both a business executive role and a technical executive role and that is what the new HR is. It is the digital and AI powered environment and it is necessary because that is what our users are expecting and demanding. 

David Green: Well, I know we are going to talk about a number of areas that are in your responsibility Anshul, and as I know from my own time at IBM, your experience in people analytics is vast. We are excited to talk to you today about a number of big themes, including AI and ethics, scaling analytics through productisation and enabling a data-driven culture.

Let's start with AI and ethics. I think we have to start with ethics when it comes to AI. As the number of organisations exploring or deploying AI continues to increase, ensuring the ethical application of this technology is vital. 

In your mind, what is trustworthy AI? I know you have written about it, that is why I'm asking the question, obviously. 

Anshul Sheopuri: Absolutely David. I will start my answer by saying this is an important area, it is also a nascent area. It is an area where we have been on a journey and have been applying this very thoughtfully, but it is also an area where many of you are evolving your understanding. It is an area which I would say is all about practical application of AI. And when you think about that, don't think about that as you are developing the AI and then at the backend of it, apply the checklist for ethics and close the door on that, but it is something you do from the very beginning as you co-create with your users. That is the approach that we take, a proactive, systematic, scalable approach. All those three words are critical. Proactive, it is something you do upfront. Systematic, it is not something which you do ad hoc and reactive, it is something that is programmatic that you do and scalable. It is not something that you apply in one program or two, it is something that you apply across all programs.

The way we approach this is through five core pillars that embody our trustworthy AI. 

The first is transparency. If you think about when you go and buy a beverage at your favourite coffee shop, you see the nutrition label, you see how many calories it has. What is that about AI? What is the help about AI, you should be able to see it, you should be able to delve into it as a user, not just as a data scientist, but as a user of the AI. 

Explainability. Why is the AI giving me this recommendation? When you watch a movie it has some sense of the genre of movies that you like, what other viewers are watching, and why the AI is giving you a certain movie recommendation. The AI in HR, should also give you that same sense of the why behind the what. The ultimate decision is yours of course, but you should be able to understand the why behind the what. 

The third pillar is fairness. This is all about bias identification and mitigation. So it is something you want to do upfront as part of the design of the AI.

The fourth, robustness. Having the right guidelines and operational principles. 

And the fifth, privacy. This is something we have all been practicing for a while, but it becomes even more important in the area of increased regulations in the space. People who have the need to know should know it and not others. 

So transparency, explainability, fairness, robustness, and privacy, the five pillars for trustworthy AI. 

David Green: And I think what that helps, to set that up with that firm basis as you said, you are thinking about that at the outset not after you have designed an AI application that you want to use. It is that fair exchange in value. So if you think, all people data is sensitive whether it is customer data or employee data, but I think it is even more sensitive when it is employee data. And we are thinking about the way you have described that there, is that fair exchange in value. So in exchange for an employee providing access to their data, to you and the team, they get something in return. Also being very transparent about the data you are collecting, why you are collecting it, minimising the people who have got access to that data, and as you say, if you are providing a recommendation around a career path within IBM, that there is an explainability behind it as well. This is why we are recommending this for you as an IBMer, or this is why we are recommending for you as a manager, why you might want to take these steps to engage your team maybe better than you are at the moment. 

Anshul Sheopuri: That is exactly right David, and what I would say for your viewers listening to this, is we all have the right intent on this, we all want to do this. And I know many of you might be thinking, how do I get started on this? I'm only on the journey. I would just say, I get asked this question quite a lot and many of you are feeling like you are in the same place, you are not alone on the journey. Reach out to your peers, talk to your friends and get started on this journey. It can be intimidating, but it is important. It is critical. And it is where, I strongly believe, we all need to head towards.

David Green: I strongly agree with that. One of the things we recommend organisations do at Insight222, and I know you have this sort of thing in IBM, is it's almost like put an ethics charter in place that is going to govern how you collect and use people data within your organisation.
And then, as you said, be transparent. Make it available so people can see it. 

I think it is so important that we do this, that you set that basis, and then by doing that you create the trust which is so important when it comes to using analytics and AI, particularly with people data. 

Anshul Sheopuri: Absolutely right. And once you create this charter it is not just keep the charter to yourself, but educate people on the charter. Be transparent, have a conversation. It is a journey we are all on and the dialogue helps you to evolve the charter into something that is much more effective for all of us. 

David Green: I know that you kindly, with Diane, contributed a case study to Excellence In People Analytics.
And we actually talked about some of the exciting ways you are using AI in HR, at IBM. But not everyone who listens to this has read the book, and that's fine. What are some of the most exciting applications, in your view, of AI in HR?

Anshul Sheopuri: Perhaps now they will read the book.

David Green: Well, you never know. 

Anshul Sheopuri: For me, perhaps the most exciting application of AI in HR is the evolution from point AI experiences, which are great, to connected AI experiences that are irresistible. And let me just delve into that more. 

When we started our AI journey at IBM in HR, five to six years ago, it started with YourLearning, a personalised learning experience for IBMers. It is great. It has got a lot of adoption. 98% of IBMers use it every quarter. It has a net promoter score of about 60, which if you are familiar with net promoter score, is a pretty impressive number. But it is one point of experience. it is personalised learning recommendations. Now if you think about yourself, David, how do you learn? How do you achieve your career aspirations? You probably have some digital learning platform, but you have friends and peers that you talk to over coffee, or you bump into them in the hallway perhaps at work, you are having mentors that you reach out to every now and then for questions that you might have. So we all have our own personalised journeys that we are on. And these journeys cross siloed functional experiences from learning. 

To me, what we have done at IBM is connected personalised learning recommendations, to mentor recommendations, to career recommendations, to the skills that you have as the underpinning of all that, and the skills which is the silver thread connecting all these pieces of the journey. That connectedness, that connective tissue, this is what I am most proud of, because it truly helps people achieve their aspirations. You may not know you have some of these skills that could get you to this career, but now that you took this course, that you took this learning, that you got this badge, you may get a job recommendation that is now based on this and helps you achieve your career aspirations.

So I always feel most humbled and I always feel really good when I get a message from an IBMer who says, I  just wanted to let you know that I got this job based on this job recommendation that I got. And I was going through this thought process. I would have never thought of myself for this role, but now I am here. 

That was just such a humbling experience to get that kind of note.

David Green: And as you said, skills is that silver thread that links all those, otherwise siloed elements, together. Which is a big difference from three to four years ago in any organisation, really. And I guess the benefit for the organisation is exemplified as well because it supports things like retention, mobility, workforce planning, all things that we are always asked about as HR professionals, but the technology helps enable all of this.

Anshul Sheopuri: Absolutely and David, you are very familiar with this more than anybody else, this is the single biggest shift that has occurred in HR, I would say over the last decade. People would be asked, how many people you have in a certain country? What is your head count? Now, the question would be, in Munich, how many designers who are experts, do we have? And, is that fit for purpose for our business strategy or not? And that is why it is just not a HR problem to solve, we are solving this because that is what the business needs. And that is the case in pretty much any industry, I am not just talking about tech but any other industry that you might consider, digitisation is forcing us to have a much more grander view of skills and embed that across the whole employee journey. 

David Green: Are there any other examples, I know you have got several, but any other examples you could share with us of trustworthy AI in action and the impact on the business at IBM? 

Anshul Sheopuri: Absolutely and the one that I would want to share also, is around pay. It is an area that is so important for everyone and the importance of getting trustworthy AI right from the outset, not at the backend, but from a design standpoint upfront, is so important. 

Today at IBM, we have pivoted from pay per performance to pay per skills. It is important consideration of our silver thread, our connective tissue, not just the way we hire, or the way we grow, or the way we learn, but the way we pay our employees. 

If you are the manager today, you will get recommendations of pay for every employee on your team. This is based on skills, comparativeness, performance, and potential. But it is so important, let me go back to the five pillars of trustworthy AI, transparency. We want you, as a manager, as an employee, to have clarity of all the data points under this so that you can grow with what we call, AI fact sheets. This is your nutrition label of the AI, you can go and look at the factors that go into the AI and understand what is working. How is it working around that? So that transparency is so important. Explainability. Managers get the reasons as to why they are getting these recommendations, it is not just not a blank box, why is this being recommended? Because you may have some personal information which the AI may not be aware of, about the employee.

The third one is fairness, detecting and mitigating bias. That may come into peer recommendations as a core aspect of what we do, in the context of our pay recommendations.

Robustness, this is just so important David, and sometimes perhaps forgotten as, yeah, the people who take care of the process. This is about putting in place the right operation control points, making sure that the right people have access to the right information, and making sure that investments are going in the right places. This is really about also changing the way work is done. It is an important point because when we did pay programs five years ago, the compensation professional was responsible for how many people completed the compensation planning. It was a compliance task that they were responsible for. Today, they are responsible for our managers making investment decisions that are in tune with our business strategy and our talent strategy, and that is a fundamental shift in the nature of the role of the compensation profession. 

So that robustness around governance and operational excellence needs to be embedded and taken in to account in work redesign. 

Then finally of course, privacy as the fifth pillar to round this up. 

That is the example I would share that is so important and so critical to complete our skills syllabus across the employee journey.

David Green: I guess it removes some of that subjectivity that can sometimes come into those pay and compensation conversations. And yes the manager would always get help from the comp and bens department, but then here is some data to back that up. So maybe the manager is making less of these decisions on their own, which can lead to problems because from what you said, some of the bias angles that might come into that discussion.

Anshul Sheopuri: Absolutely. And if you think of the manager right now and their persona, think about a company which operates across hundreds of countries, across multiple lines of business, with all kinds of skills across the globe. You are a manager of five or ten employees and you see in your silo, certain talent dynamics that a playing out. But how do we equip you with insights for all the teams of talent and managers across the enterprise, their skills, their silos dynamics, and share that information back with you, so that you can better understand the talent dynamics as they connect and the patterns as they connect, so you can make more informed decisions. So this was really about empowering the manager to make more fact based objective decisions.  

David Green: Let's move from pay to re-imagining work. So you have written quite a lot about this, this year, and I am glad that you are sharing some of your work publicly Anshul. I know we talked about that a couple of years ago when I was still at IBM. You have shared quite a bit on your own AI powered HR journey at IBM and creating automation across employee experiences to simplify work. So you said in one article, and we will put a reference to this article with the material around the podcast. 

“The first lesson of automation was to re-imagine work before we automate”

Can you tell us a little bit more about that and why re-imagining work is so important? And what were the main areas of work that you discovered were right for re-imagining? 

Anshul Sheopuri: Absolutely, David. And I will say at the start, it is not a new topic and it is probably not even my topic. If we were not re-imagining work and focusing on the new, we would probably be having a horse-drawn carriages powered by the strongest horses, fed with the best protein heavy diet, because they would push the horse the fastest. We wouldn't have evolved to mode of transportation we are at today. And it is all about the user need and the business need and solving for the intersection of that. In the example I just gave you the user need was all about, how do I help move my goods from point A to point B. Not to move the horse drawn carriage at the fastest rates possible. The business need in that example was all about optimising your supply chain to make the business the most profitable. That as context as an example, because that is what I think about how we evolve and transform in HR, that becomes the nature of how we think about it.

In that case as we embed skills across the employee journey, that is the implication of our business transformation that we are going through with digitisation. We really need to think three things, what we are expecting the manager to do? The nature of the manager's work and how that is changing and that has evolved from, let me do compliance work and make sure you have documented your annual performance reviews, through a talent magnet, who is responsible for retaining critical skills in the team and helping develop and deliver a thriving organisation. And if that is what we expect them to do, we need to equip them with the right insights. We need to help them to understand the business strategy and the talent strategy. This is an important nuance David, this is not about just about sharing the talent strategy, the silver thread, but it is something that needs to be internalised. And in order to do that, they need to be able to have conversations around, what does that mean? What is this skill? How do I understand what a skill is? Why is this skill critical? Why is this not critical? 

And so today, we have access to the bot, embedded in the compensation planning tool, where they can ask these questions and get answers to those questions. So it is about creating an interactive experience for the manager that enables more effective decision making, as well as gives them that opportunity to have that conversation.

You know, when the bot cannot answer the questions for them, it enables their ability to ask questions of somebody face to face. That also means the way the work of the compensation professional, or the learning professional, or  other professionals need to change. They need to have now more analytical skills because they need to be able to look at, are our investments going to the right places? They need to have new design skills as they need to be able to think about the managers user needs. 

So it is about changing the nature of work both for the manager and the IBMer, as well as our own HR teams, and the implications of that for skills. That is what I would say is how work is changing and how we are thinking.

David Green: And of course those techniques are going to stand you, in IBM, in good stead when it comes to re-imagining work from a hybrid perspective. Although I know again, from my time at IBM, that IBM is one of the pioneers of remote working anyway. But obviously the new hybrid environment, whatever that means, data and technology is going to be helping to empower that, but also empower teams and managers as well.

Anshul Sheopuri: Absolutely. If there is one thing that has really become clear in the last 18 months, during the pandemic, it is that digitisation is getting accelerated. That is undoubtedly what's is occurring. And that means much greater emphasis is on technology, data, and AI, in decision making as we go through this journey. 

David Green: Yeah, that is possibly a part two of the podcast around that.

What is the most important thing that you believe for organisations and then people analytics teams as well, to get right foundationally, to ensure the ethical deployment of AI across employee experiences and across all the workforce? 

Anshul Sheopuri: I will give you a two-part answer to that, David.

One is something that I already said. Don't think of this as something that occurs at the backend of the AI being developed, it is something that you really need to do upfront as part of the design. 

Then more importantly and perhaps connected to this, is making sure that it is solving for the right business problem and the right user problem. When I see AI projects failing it is because they are not operating at the intersection of what is the business problem that you are trying to solve for, and the user problem you are trying to solve. It is rarely because you don't have the right data or the data quality is poor. Those are all important ingredients, but it is mostly because you are not starting with the right business problem and user problem. 

David Green: And I guess what you mean by that is number one, is there an important business problem that needs to be solved? And then number two, we are designing it for users, will users use it? Is the user interface strong enough? How can we encourage users to use it, by making it friendly and giving them information?
It is those sorts of considerations, isn't it? 

So you could have a really important business problem, but you could design a piece of AI that no one wants to use. 

Anshul Sheopuri: That is exactly right. Maybe there is an important business problem, but there is no user or the user is focused on other problems, therefore this is not important enough for them to embark on this journey with you on. 

David Green: Yeah, it is funny, I have been saying and I am sure you have been saying it for a long time, never start with the data. Start with the business problem. But if you are going to build something, make sure that users want to use it and that it solves for the business problem and you can't, well you can still go wrong of course, but get those two building blocks in place and you are setting yourself up nicely for success.

Obviously creating all these products and getting users to get involved in building, and testing those, as well as iterating, this is a different way of working for HR. Let's be honest about it. So let's talk a bit about developing a data-driven culture. 

We recently published “the new model of data-driven culture for HR” and it was made up of three components. One of which is embedding data-driven decision-making. How do you approach this at IBM across the HR community? You could probably answer it from both, but particularly how do you get the HR communities buy in, to actually taking a more data driven decision making approach?  For example I guess, when HR are interfacing directly with the business, how do they take some of this information and work with the business to make it happen? And again, if you are going direct to managers, I guess is probably the second element to that.

Anshul Sheopuri: The most important thing, I think in my perspective, is actually not the tools or the platform or your webpage, however swanky it may look, it is about the culture in the organisation and do you have a data driven culture in your organisation, it is beyond HR it is certainly at the company level. 

Perhaps the one smallest but most powerful tactic that I have seen, to propel that culture a bit forward, is celebrating individuals for embodying what you want to be amplified across the organisation. So we try and celebrate individuals who are exhibiting this data driven culture in all hands call with our CHRO and other forums, to really point people towards the north star, so there is clarity around what the north star is. 

So that is what I would say is the number one thing, but of course you can't just say something and do something else, so it is about channeling your investments behind that. Whether it is consumable tools, AI tools, today at IBM, if you are a frontline HR partner and you want access to somebody’s information, whether it is salary, or salary ranges, or time in a band, how long they have been in a certain role, we can actually go to the bot and ask that question. So it is also about consumable experiences to the HR practitioner. While we’re telling them what the north star is, we are giving them the tools to get going on that north star journey.

And then the final thing I would say, is declaring what skills are strategic for the organisation. For IBM, we have declared to the HR practitioner that analytics is one of the key strategic skills we expect all of you to have. So it is not just some of us, it is not just the analytics organisation, or 20 to 30 people in the talent organisation. It is everybody that is expected to have these core analytics skills. We also actually do bootcamps so people can come in if they are a bit edgy about some tools, they can get their hands dirty live and have some experts to talk to. 

David Green: And I think that kind of approach of, I guess a little bit of top down there, I know it came from Diane and I presume it is coming from Nickle as well. As you said, analytics is a key skill that you need as an HR professional within IBM, and if you want to develop a career in HR in IBM, and if you haven't got the skill then you need to acquire the skill. We will give you the means in which to do that through bootcamps, we will celebrate your success and we will identify people who are embodying this so then there is an opportunity to learn off them. And we will give you the intuitive tools, so you can actually use it. 

So it is not just one of those, you have to do all of those together to really create that data-driven culture.  

Anshul Sheopuri: You have to, you have to. It has to be clear from the very top, that is important and strategic, and you need to embody that in all workflows, whether it is the tools, slack channels, or other collaboration. 

David Green: And again then, from what you said on the previous question, connecting all of that to the business problems that need to be solved, rather than just analysts going off into a dark room and coming up with solutions for problems that don't really exist.
I guess that really supports the work of your team because if on the one end you are orienting them on the most important business challenges that the organisation is facing, you are building stuff that is putting the user at the centre, so it will be used and then you will deliver the value from that as well. And at the same time, there is this whole ethic around creating that data-driven culture, as you said, which transcends HR, it is not just HR, but certainly if we think about HR then you are creating a very powerful vehicle to really keep perpetuating this. 

Anshul Sheopuri: Absolutely. David, the other thing I would say to your listeners is, different companies have different points in data transmission journeys, and that is okay. That is okay. Nobody is perfect. But also try and see if you can leverage the broader ecosystem. So at IBM we have set up an enterprise platform that tries to connect our people data, our financial data, our client data, and our product data. And this is an important triangulation to have because you are not solving for a talent problem in isolation, you are trying to solve for it in the context of the business problem, the context of your client needs, and that is where your people come in. So having this integrated foundation ensures consistency, but also consumerability because you don't get to consumerability unless you have a systematic approach to a data platform. And that is something called Workforce 360.

We are very proud of the progress we have made in that space, in the last couple of years, and it helps us really have a scalable approach to trustworthy AI because that has been the key accelerator for us to develop the scalable portion of trustworthy AI.

David Green: You have set that up very nicely actually Anshul, I was going to ask you about Workforce 360, so I would love to talk about it in a little bit more detail. In terms of quantifying the business value of productising analytics solutions in this way, and you might have a couple of examples of how Workforce 360 has really helped accelerate value in that sense. We would love to hear a bit more.

Anshul Sheopuri: Absolutely David, and I already talked about a few examples, all of them have quantified the value for those particular experiences. What is important for us also, as I shared with you, is a secure, scalable data platform. The reality is, and many organisations are in the same place, our data systems and architecture was never designed for insights. It was never designed for personalised experience. It was designed for compliance. It was designed to be able to answer, how many people are in a certain country, in a manner that could be certified and check the box off. So we have data systems, like many other organisations, that a modern legacy space is somewhere on the cloud, and having a hybrid cloud platform that enables you to connect across these experiences is what what our Workforce 360 platform gives us.

I would say the two most important outcomes of this, one is speed. Our ability to move at speed was hampered when things were sitting in different places. Think about your own experiences when you are dealing with any data that you might have, you are pulling data from some spreadsheet, trying to merge it with some other spreadsheet, there is a data correlation, and the amount of churn that can cause in your day to day. So alleviating that, taking that out, and driving the right quality, cross silo decisions, that is what this data platform gives us. In our experience, for every dollar that we have invested in this, it gives us $10 in return, over the past 18 months.

It is that critical foundation, it has a lot of indirect benefits because it is a critical foundation that really makes the lives of many, many people much better.

David Green: And as you said, by bringing people and talent data together with customer data and sales data, you are actually going to answer the big challenges facing the organisation. You want to access all of those data sources and more, finance data and other types of data, and actually by having that together, as you said, the speed in which you can then investigate and analyse that particular challenge and come up with a solution and a product to answer that is significant.

Anshul Sheopuri: The other thing I would say is this is not just about helping HR, this helps our sales professionals, because they can access the right people data for the right decision. It helps the finance professionals. And that might be a bit scary for some people, right?
But for the right use case, for the right business problem, the right user should be able to access it for the right decision making across the board. So it is a two way street and a culture shift that we all need to go through. 

David Green: We are coming to the last couple of questions Anshul. The first one, I think you have certainly covered, so you might want to summarise. But this is the question that we are asking everyone on this particular series of The Digital HR Leaders Podcast. How can HR help the business identify the critical skills for the future? 

Anshul Sheopuri: Sure, absolutely. This is one of my favourite questions, so thank you for asking that.

I always shared a bit about how skills is the biggest inflection point over the past decade and how that is helping us protect our employee journeys. But I would say the way in which we can help the business identify critical skills has two angles. One is demand and the other is supply. 

When you look at it from a demand perspective, the data is there in certain roles, whether it is financial pipeline or voluntary attrition of key skills, because those are areas where you will see in the marketplace where you have attrition, that is where a lot of demand results are coming up. So those are all examples of demand markers for the business. 

Then you talk about supply markers, supply in your organisation comes from a number of different markers.
It could be things like voluntary attrition because your supply is beginning to deteriorate, or the rejection to offers and in some roles you have higher rejection rates than others. That is a risk to your supply. Time to fill a role, that is a risk to your supply. The number of open jobs in the market for the a certain role, that is a marker of supply. 

So I would say both these angles together, the demand for a certain skill and the supply of a certain skill, and the data points as I said, are there to help infer where the demand is on the supply. If you put all these things together, that will give you a more nuanced picture of how the organisation should look moving forward. 

David Green: So from what you were saying there, that involves data that you have got within your organisation, but also quite a lot of data that is not in the organisation. So the labour market data that you are using and blending together to give that clear picture. 

Anshul Sheopuri: Absolutely. Absolutely. It is about the internal and external, and bringing those together for the business needs.

David Green: Okay, so before I give you the last question, going back to the data-driven decision-making. Where would you say, is the number one place to start when trying to embed that data driven decision making across the HR community?

Anshul Sheopuri: If it is about data-driven decision making for the community, I would still start with the same problem. If you are a client partner, who is being asked a question to go solve, validate that there is a problem to be solved. What is the business problem? 

Let me give an example. There was once one of my colleagues at another company came and asked me, somebody is asking me to solve an attrition problem because they are saying there is 15% attrition with certain jobs. The person felt strongly that it would lead to a lot of churn and so I said, one of the things that you might want to ask or look at is, what is the attrition in the market for those skills? And it turned out that it was actually a higher level of attrition than in the business.

So the problem that the individual in the business was facing it and feeling it, wasn't truly as big a problem as they thought it was to begin with. So there are ways in which we can validate that the problem is worth solving. 

David Green: Perfect. It is a great example, particularly at this time when we are hearing all this stuff about the great resignation at the moment. I think sometimes the headlines are worse than the actual reality.

Anshul, thank you very much for being a guest on The Digital HR Leaders Podcast. Can you let listeners know how they can stay in touch with you, follow you on social media, and find out more about your work?

Anshul Sheopuri: Firstly David, thank you for the opportunity I have thoroughly enjoyed this conversation. I am always available on LinkedIn, that is probably the best way to reach out to me. And of course, if you want to chat more, you can reach out to me by email as well.

David Green: It has been fantastic to have you on the show. I have loved hearing about the great work that you are doing and certainly those five steps that you outlined, that is definitely something I would recommend that everyone follows. So thank you for sharing that with listeners.