The digital revolution is here, and to keep up with a world that is driven by data and technology, we need to start helping our teams develop a ‘digital mindset’ to help future-proof our organisations. 

To talk to us about developing a digital mindset, in this week’s episode, David will be joined by Dr Paul Leonardi, Duca Family Professor of Technology Management at UC Santa Barbara, and co-author of the book The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI. 

Paul will be sharing what organisations and HR Leaders need to do to start helping their employees get to what he coins as the 30% rule of digital fluency – the minimum threshold of digital knowledge required to have digital conversations. 

In this episode, we discuss:

• The factors that impact developing a digital mindset

• The importance of using a relational approach to analytics to improve workforce efficiency

• The opportunity for organisations to invest in future-leaning technology

• How HR can add business value as we start to transition out of the pandemic

Support from this podcast comes from eQ8. You can learn more by visiting:
https://www.eq8.ai/

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: In today's age, where data and technology are revolutionising the way we work and interact with others, there is no doubt that adopting a digital mindset is key to remaining competitive in both business and in our careers.  So in today's episode, I'm delighted to be talking to Paul Leonardi, Duca Family Professor of Technology Management at UC Santa Barbara, and co-author of a terrific new book, The Digital Mindset.  We will be discussing how, as HR professionals and leaders, we can help ourselves and our teams become more digitally fluent.

Paul Leonardi: It's becoming more and more clear to leadership that they can't know everything that's going on on their teams.  There's so much invisible work that's taking place that they need help to understand what are the trends and what are the dynamics, and I think that's creating a really unique moment in time for HR to be able to step in and say, "We can help provide you with some of this kind of analysis that's going to give you the capabilities to make your own decisions about how to lead your teams in the best way".

So, the pandemic is breaking down this macho myth that the leader needs to know everything about their team without any outside help.

David Green: Our conversation will also cover the importance of using a relational approach to analytics to help improve workforce efficiency; and of course, what organisations and HR leaders need to do to start helping their employees and their HR teams attain Paul's infamous 30% rule of digital fluency.  I started our conversation by asking Paul to tell me a little bit about his background and areas of expertise and interest.

Paul Leonardi: I am a consultant, professor, researcher, wear many of those hats in the area of the future of work and digital transformation.  I've spent the last two decades really working with, boy, hundreds of companies that are trying to implement new digital technologies to improve the work lives of employees, to increase productivity, to enhance organisational culture, you name it.  And I am trained, and my background is in engineering, I have a PhD from Stanford School of Engineering; but I really focus on the human aspects of using new technologies and the data they produce to help make the workplace better.

So, I interface a lot with HR leaders, who I know is a major audience for your podcast, and senior leaders of companies that are really trying to get a handle on, how do we get our employees using the best kinds of tools, making the best sense of data and creating a better workplace?

David Green: And, Paul, you've been banging the drum for data analytics for over 20 years now, which is really impressive; how have attitudes to it have changed over time?  Looking back, are there some milestone moments or times when excitement ebbed or flowed?

Paul Leonardi: Yeah, I think if we take the long view and we think about what are some major changes that have happened in HR related to data analytics and technology, there are a couple of key movements or moments that I would highlight; the first was in the mid-2000s, early 2000s.  What I really started to see at the time was that HR was transitioning from being more of a service arm, and I don't mean any offence, right, to say that HR had sort of been a service organisation, but moving to being much more of a strategic partner with the business units. 

HR had a lot of value that it could provide, beyond simply helping to onboard employees and deal with personnel issues and the like, that would help leaders be better leaders and help employees to have more profound experiences within the workplace.  That was a transition that I think really began at the dot.com boom in the early 2000s when so many employees were joining the organisation and HR needed to start to rethink itself a little bit about what kinds of capabilities it could provide.  What we've seen, since that period, is that I think HR has been moving in evermore concerted ways to becoming a trusted partner.  I know we'll talk in our questions today about data and analytics and new digital tools fit into that, but that was certainly one change that I saw. 

I think another key one was probably just at the beginning of the last decade, maybe 2012, 2013 timeframe, was the move towards what I'd call the analytics revolution; the seeds of that really began in HR.  They had been prevalent in other parts of the organisation, operations and manufacturing for sure, but it really was, I think, the rise of SaaS-based tools that began to proliferate around this timeframe that gave HR access to many, many more data sources. 

Without the capabilities across the rest of the organisation to make sense of and analyse or harness those data sources, HR became a place that could provide strategic value to the business partners and making sense out of what things are affecting our employees in what kinds of ways, and are there are ways of understanding broader trends in workforce that really could help us to be better leaders and managers.  So, that is a change that we saw, I think, start to happen around that timeframe. 

Certainly authors like Tom Davenport, that wrote about the analytics revolution, his book came out in the mid-teens, and a number of different kinds of articles and conferences that began to focus on people analytics; Wharton launched their people analytics conference right around this time as well, so that movement began to happen.

I still don't think today that that movement has reached its peak though.  We haven't seen the promise of data analytics and thinking through technology implementation in HR really reach its apex yet.  Companies are not taking advantage of it still in the rights kinds of ways and perhaps that has something to do with how HR has or has not positioned their role in this process, so I know we can talk about those kinds of things.  But those are some of the major shifts that I've seen.

The last thing I'll highlight here is that the pandemic has been a really interesting time, of course, for HR, and you have had a number of guests recently that have talked about this and the challenges that have been associated, and opportunities, that arise with the pandemic.  But in the area of data and analytics, the major change that I think we're seeing now is that, come winter of 2020, there was this massive and wholesale transition to remote work; that meant that everyone across the organisation was doing their interaction, their communication, their work on digital platforms. 

As we know, those digital platforms, if they do nothing else wonderfully, they record and create metadata out of every interaction that occurs.  What that has given HR practitioners, what it has given the data scientists that live in the HR function, this unparalleled and really heretofore unimaginable dataset that we may be able to use to be able to dive in and understand and uncover insights about employee behaviour and trends and suggestions for improving our workforce, because we have data at such a granular level that we never had before. 

There are pros and cons associated with that, there are changes that need to be made to HR for that really to reach its fruition, but I think we're the midst of a third major revolution that I've seen, at least in my career span, about what HR can do and what it will do in the future.

David Green: We've seen ourselves, with the work we do at Insight222, that the pandemic has kind of elevated people analytics teams and HR functions within companies, and those companies that are already invested in people analytics are reaping the dividends of those investments at the moment.  So, the data actually gives you that nuance on what those companies want to use the office for potentially, who should be coming in, when they should be coming in, how are you going to structure work as well.  Then we can really start to understand perhaps, and I know you've written about this in the past, how we can actually design our offices as well, so understanding how people are using offices.  So I think, as you said, I totally agree, we're still only scratching the surface in a lot of respects.

Paul Leonardi: True.  Do you mind if we dwell on this for a just a second?

David Green: No, please do.

Paul Leonardi: The thing I wanted to bring up that you mention here is about the shift from HR using data to support HR functions versus helping the businesses and having HR occupy a strategic role in using data and analytics to support the businesses, and I would talk to leaders across the businesses.  They were not extremely receptive to this idea; this is pre-pandemic, right. 

A big part of the tension, I think, has been that there is this, I'll call it a myth, but that a really good leader in a business should know what's happening all the way up and down the hierarchy in their business.  They should have deep and penetrating insights into what's happening on their teams and that if an outside partner, like HR, were needed to come in and help to give them insights, they were in some ways failing as a leader.  I think this is a real dominant myth that we sow the seeds of in business schools as well, and I've taught in a business school for many years; we tell managers, "You need to have your finger on the pulse of your teams, and you need to know what's happening".  So, the thought of bringing in an outside counsel, outside expertise to help you, especially an outside that's within your company, is not seen as the hallmark of a great manager.

However, one of the things that the pandemic has been really good for in this capacity is that, as teams have been become distributed and as senior leaders increasingly don't just manage several teams but teams of teams, and we're seeing that trend proliferate right across so many organisations, it's becoming more and more clear to leadership that they can't know everything that's going on on their teams.  There's so much invisible work that's taking place that they need help to understand what are the trends and what are the dynamics.

I think that's creating a really unique moment in time for HR to be able to step in and say, "We can help provide you with some of this kind of analysis that's going to give you the capabilities to make your own decisions about how to lead your teams in the best way".  So, the pandemic is kind of breaking down this macho myth that the leader needs to know everything about their team without any outside help, and now I think is a great moment for HR to be able to seize this opportunity, because people are much more receptive to that than they have been in the past.

David Green: Where you do you see that we are today for HR?  Where is HR today, and what does that mean for the future for HR?

Paul Leonardi: The large, more sophisticated organisations that I work with have a more expanded and generous view these days of what HR is and what HR can do; and it's the mid-size, small- to mid-size companies that seem to be stuck in a 20th century mindset of what HR is, right.  I go to HR when there's a personnel problem, I go to HR for help when we are onboarding new employees, I go to HR for these kinds of issues; the feel, the discipline of HR needs to be providing those kinds of service-related activities, but that's a part of what they do.  What HR can really be doing is becoming more of a trusted advisor to the businesses.

I think today, we're in this dance of some businesses are kind of understanding that and getting onboard with that, others still being reluctant, and HR really trying to still figure out what is our value proposition for the businesses?  It can't be the same thing that it was in the past.  We also have to be careful, we have to step delicately because we know that leaders want to make their own decisions and own their decisions, but they increasingly need better insights in order to do that.  Where do some of these analytic capabilities, where can they be, or where should they be located in the organisation?  Should it be that we have our businesses running their own analyses and trying to come up with these kinds of insights, or does it make sense for an organisation like HR to be doing that?

There's tension across so many different organisations about this decentralised versus more centralised approach to how we make sense out of employee and managerial activities and a sense of employee wellbeing.  But I think that the thing that's happening that's exciting for me right now in terms of where HR is going is that many, many organisations are recognising that, with the move to distributed work and remote and hybrid work arrangements, the increasing emphasis on employee wellbeing, that there's only so much I can do as a leader to move the needle in those areas, while also trying to make sure that I'm keeping a strong culture within my team, that I'm also moving forward on our operational deliverables, and I need partners to help me do this.  How HR can negotiate that partnership and frame that partnership is, again, still open to some discussion, but that's what I'm seeing is happening right now, and that's really exciting.

David Green: Typically, it's the larger companies that have invested in people analytics teams and the technologies that support that as well.  Now, obviously there are small and medium companies that have invested in people analytics as well, but it's not as widespread, certainly in my experience; some of those companies that have invested in people analytics are only really using it for reporting and kind of rear-view mirror stuff, which is helpful, but you want more than that, marketing certainly has a lot of more.

So, it's interesting, yeah.  I'd love to do a study of it and to see, is HR perceived as being a strategic partner to the business in organisations where it's got that muscle of people analytics versus where they don't have that muscle that they can utilise?  It's not just about people analytics, of course, it's about the people leading HR, it's everything else, the investment in it, it's about the mindset and everything else and the skills, of course, but interesting discussion.

You wrote a fantastic article, Paul, with Noshir Contractor back in 2018, it's in Harvard Business Review, about relational analytics, also known as organisational network analytics, also known as relationship analytics.  I've love to hear a little bit from you, if you can talk a little bit about what a relational approach to people analytics actually means.

Paul Leonardi: So, I think the place to start is in a distinction that Noshir and I have been talking about for a long time, and that we opened that article in Harvard Business Review with, and it's thinking about a distinction between individual attributes and relational attributes.  When we think of people analytics generally, whenever I talk to a company about people analytics, they think, "Okay, how long has an employee been working here?  How many teams have they worked on?  How long is their commute to work?" if they have some extra data, "How many performance reviews have they been through?" 

All of these are attributes of individuals, they fall into this category of human capital; it's the things that I own, that I carry with me regardless of what job I'm in or what organisation I work for.  Those are essential elements to running a good people analytics operation and to making sense out of employee behaviour and trends, but while they're necessary, they're not sufficient, and that's because so much of how our work really gets accomplished is through the relationships we have with other people.

So, while individual attributes are one side of the people analytics equation, the second side is really what we call relational attributes.  And, relational attributes are really no more than thinking about what's the social capital that any individual has within the organisation, what's the nature of the relationships that they have with other people. 

In that article, what Noshir and I did was we outlined what we called structural signatures, which were essentially the models that you can use to identify key variables, like who's likely to be an influencer?  Who is someone that we would expect to be able to come up with innovative ideas at some regular basis?  Could we identify vulnerabilities if someone left this position; would we cut off access to a supplier organisation, for example?  But what we didn't talk about in that article really was, what are the use cases for those?  That's what I've been spending, basically, the years since we wrote that piece working with companies on.

Some of the key use cases really have been around team design, so you're trying to put together a taskforce or a team that's going to tackle a really key challenge within the organisation.  We need that team to get up to speed really quickly, they need to interact flawlessly with one another, they also need to pull in lots of unique information from different parts of the organisation to make their decision.  We can use relational analytics to identify that optimal team structure. 

We can use it for DEI efforts, and this is what I'm really excited about, is companies are really good at using demographic data to identify what a diverse team could or should look like.  So, we can look and say, "Okay, well, we do have enough women on this team, so we're going to add some more women to it" or, "We don't have enough African-Americans or enough Latinos on this team", or whatever the case might be, and we can put people on the team; that gets to a diversity question or issue that we might have.  But just having people from diverse demographic backgrounds or cognitive backgrounds or functional backgrounds, whatever the case may be, isn't enough.

We need to make sure those people are included, that they are being listened to, that they're being asked questions, that they're giving and sharing their knowledge and information.  That's the reason to have diversity in our organisations, not to check a box.  What relational analytics can help us to do is get a sense of that I in DEI.  Are the diverse members of our team actually included in the conversation; are they central to the discussion and to the key decisions that are being made?  That's an area that's really exciting that I think relational analytics can help us unlock.

David Green: I completely agree.  Relational analytics allows us to go beyond counting, the need to measure this not just through surveys, although surveys definitely still have their place, as I'm sure you've told me that, but through really mining this passive data and not looking at the individual level, of course, but trying to understand some of the patterns and aggregating that.  Are you seeing more and more companies implementing this now?

Paul Leonardi: They're starting to.  There are a couple of challenges and, if you're interested, I'll outline I think what a few of those challenges are.  Many of the organisations that we work with, first and foremost, are concerned with privacy and employee privacy, and this is a really important issue that can't be just swept under the rug. 

I talk to lots of leaders, particularly in HR, that say, "Wow, this is a fascinating idea, that we could be using this digital exhaust to collect relational data that we would not other have access to", and the problem is that are we being too invasive with our employees?  Do they know that we are looking at this data; and, what are they worried about exactly?  A big part of the work I do with companies is to try to develop what are humane, transparent and reasonable solutions for dealing with this privacy concern.

One of the key ways that we've come across to deal with this in many organisations is to be very clear to employees that we see tremendous opportunity to unlock and uncover patterns of interaction and employee behaviour, by paying attention to the kinds of communication that people have with each other across the company that can improve our organisation, and help perhaps give you insights into improving your own performance in ways that will benefit that entire the organisation.  And, in order to try to achieve those goals, there are certain kinds of data we're going to be collecting about your communication. 

Where most organisations tend to put a hard line at this point, and I think it's the right place to put it, is that we will collect data about your interactions but not on the content of your interactions.  So, I'll know if Paul and David are sharing an email or DMing each other on Slack, but we will never look into what you're saying to David, that there's a bright line there that we will not cross.  So, that's one assurance that many companies give.

Then, what we often do to deal with this privacy concern is to say, "We are going to show you any data in the dashboard, let's say, that pertains to you", so if there are analyses that we're running to try to compute, for example, who are the key influencers in this organisation or in this department.  That does a couple of things; first, I think it really helps to show employees that there's not much nefarious that's going on here.  So, a lot of this just ends up being like if we share it with employees and we're very transparent about what we're doing, these just become more datapoints for people to improve their own behaviour and to be reflective about the things that they're good at.

A second one though is about the technical capabilities of being able to move from raw digital exhaust data to data models that turn that digital exhaust into actual organisational networks, and that is not a straightforward approach.  That's something that I've been working on with my students, in collaboration with Noshir in the joint research projects that we have, trying to understand what are the best ways to create data models that actually represent robust employee interaction?  Part of what we have to do is make this transition between the raw data and the data model, and then once we have an acceptable data model, then we need to be able to run these structural signature analyses to identify what are the things that are happening, and then relate those to use cases.

So, there are some technical components that many organisations struggle with in, "How do I extract that data; how do I create the data models; and how do I run the structural signatures?"  Then, there are more managerial issues that the organisations struggle with which is then, "How do I take those kinds of insights that I'm getting from these analyses; and what are the use cases to which I put those?"

What we're finding is that there are a number of companies, and I'm working with some of them as well, that are on the software provider side that are trying to develop these kinds of tools that will create those data models, run the structural signatures, correlate them with certain KPIs for you so that you really have to only deal mostly with the managerial issues, not with the data issues.

So I think, if you think about privacy, you think about the data side and you think about the data translation application side, these are the three elements that are still up for a lot of negotiation, and are slowing the movement towards using digital exhausts for these kinds of predictive analytics.

David Green: Which leads quite nicely, I think, to the next question.  So, we're moving to talk a bit about the book I think, because that third piece, that data translation, and particularly as we think about HR professionals and also managers in the business frankly as well, your book is very practical and it's obviously aimed to empower people to feel that they can adopt a digital mindset.  What are the main reasons for people to feel resistant in this area?  Does it come down to individual personality type, size of organisation they're in, attitude of their own leaders?  Love to hear some of the research, and I'm sure our listeners would love to hear some of the research that you found were the main reasons for resistance.

Paul Leonardi: Well, the reason that we wrote this book, The Digital Mindset, is that Tsedal, my co‑author, who's a professor at the Harvard Business School, and I have done lots of the kinds of projects with companies that I described to you here.  My focus has more on the HR side, analytics and technology, and Tsedal's has been a little bit more on the business side, which is helping remote teams be successful in the wild.

The thing that we both really have noticed so often is that there's a reluctancy amongst many people across the organisation to fully embrace a lot the technology and data changes that are characteristic of the digital economy, because they say, "I'm just not sure what it means to be digital, and what skills do I have?  I'm not a computer programmer, I don't know how to code in Python or Ruby, and I don't know how to run advance multinomial models, so am I left I the dust; what's the story?"

So, we decided to write this book that could try to say, "Here are some fundamental skills that you need to know, and if you can develop these skills, you're going to be able to shift the way that you think and you'll be able to ask new questions and you'll be able to interact with your colleagues in new ways, because you have a new vocabulary".  So, we call that change "a mindset shift", and so we say, "You need to develop this digital mindset". 

What we do in the book is we say, mindset is basically approaches, there are approaches for how we orient towards work in our life.  We talk about three fundamental approaches that you need to develop a digital mindset.  You need an approach to collaboration that recognises that we are increasingly collaborating with advanced AI-powered robots and bots as teammates; you need an approach to collaboration that recognises that the people that we work with and interact with are going to be remote and distributed, and we're not going to see them in person very often, and there's a whole host of interpersonal and social dynamics that need to change when that is a reality. 

We talk about developing an approach to computation, that you need to understand about how data are collected, how data are categorised, how data are stored, how data are analysed, what data are included, and what data are not and what the implications for that are.  We spend a whole chapter in the book basically talking about data, and it's not a complex mathy chapter, but it's really about understanding that data is a social product. 

Data aren't objective things that exist out there in the world waiting to be uncovered, but that the way that we instrument our environment and the way that we monitor people on their machines and the way that we classify those data and the algorithms that we push those data through all massage it and change it and turn it into particular kinds of objects that we use to make predictions with. 

You need to understand how that happens regardless of what role you're in in the company, and you need to have some rudimentary knowledge of statistics, because as we move to an increasingly data-driven world where insights are being demanded to be derived by data and analysis, if somebody tells you, "This is an significant result", you need to be able to say, "Is it really?  Show me the confidence interval on that".  You need to understand, did they run it through the right kind of statistical model?  You don't need to be able to run those models yourself in most cases, but you need to speak the language.

Then the final approach is what we call an approach to change, and that just is simply to recognise that the world is no longer a bunch of long periods of dormancy or statis punctuated by rapid events of change and then you go back to a period of everything is calm and quiet for a while.  No, we're in a constant state of transition, a constant process of transition at this point, moving from one data to new data, to one technology to new technology, to one consumer need to different consumer needs. 

So, reorienting to the world in a way that recognises that, has implications for how we think about experimenting in organisations, how we think about security, how we think about culture, and how we think about training and upskilling.  So, all this is to say that we say you need these three approaches to collaboration, computation and change to develop a digital mindset, and you need some skills to be able to get there.  The good news is that you don't need to be an expert in any one of these areas, we find.

David Green: You outlined something in the book which I think will be the music to the ears of many people listening here; it's the 30% rule.

Paul Leonardi: Yeah, I think that's the place to go next, is that our argument, and this is grounded in lots of data, is that you need about 30% fluency in these different topics to be conversant enough to ask the right questions and then make smart decisions in the digital age; we use the analogy here to learning a foreign language.

So, most researchers of foreign language agree that, if you have about 12,000 words, in English at least, then you have roughly native level of proficiency, native level fluency, but you can be a really productive contributor with about 3,500 to 4,000 words.  You don't need to be fluent to get along well and to interact with people smoothly, and that's a really nice analogy to what we mean by the 30% rule.

If you were in a data science function, you need to know a lot of data science, but if you're interacting with a data scientist, you need to know enough to be able to ask the right kind of questions, you need to be able to make sense and interpret what data and analyses they're giving you and get to that 30%, and you can do that.  So, in the book, we try to basically help people get to the 30% in these areas.

David Green: I've seen stuff out there talking about HR professionals who have to become data scientists; they don’t.  But that 30% is so important, because analytics is really about getting down to the right questions that you can contest the analytics, so that's part one of that.  So, you think of yourself as an HR business partner interacting with the business, then can you ask the right questions to drill down on what are the real problems that we could potentially measure with analytics? 

Then as you said, the second part of that is how can we take the insights that the data scientists have found, and how can we translate that into a language that's going to resonate with the business leaders that we're working with, if you're HR business partners; how can we manage the change, and how can we measure the impact; that's really key, isn't it?

Paul Leonardi: From the HR perspective, I think there are two ways to really think about this: one is how do HR leaders and professionals get to that 30% themselves?  They need to for all the reasons you just described, David, that it's really important, but it's really important that HR and L&D is helping the rest of the organisation to also develop their 30%.

We outline in the book, we have an appendix, we talk about this in the last section, but we also have an appendix of some companies that we think have done a great job of upskilling and reskilling, and we do an overview of the kinds of programmes that they've put together to help employees get to that 30% in these different areas.  The one thing that we've seen that's so, so essential for any kind of digital transformation initiative to be successful, is that it's not enough to hire technical people to do the technical stuff.  Everyone across the organisation needs to have that digital mindset because you need everyone rowing in the same direction.

David Green: How do they get to the 30% stage?  So, let's say one way is obviously you hope your company's going to support you, but as an individual, how else could you potentially reach this level?

Paul Leonardi: Yeah, well I think a start is to read the book, and I don't mean that in a glib way really, but we worked really hard to distil what do we think are the key essential ingredients that you need to know in each of these areas.  So will, at least, put you on the field.  Next is you want to be able to move across the field in some way and, if your organisation is not supporting you, the good news is there are so many different resources out there available to us electronically today for doing these things. 

I was trained as a social scientist and I worked at a PR company and then decided to go back and get my PhD from a college of engineering and, boy, that was hard.  I had to develop a whole set of skills in programming and operations management and areas that I didn't know anything about.  That was really, for me, the start of developing a digital mindset, was building those skills, but that was 20-plus years ago now.  Since then, I have taken executive education courses from the different universities, I have been in some open enrolments; Stanford had this great CS undergraduate open enrolment programme to learn the basics of some new programming languages.  I've taken several different courses on what used to be lynda.com, which is now LinkedIn Learning, to constantly refresh my skills.

So, part of it is the recognition that, even if you do have some technical jobs, they're going to be obsolete if you don't continue to refresh them.  But if you don't, I think what you need to do is figure out, "What are the areas in which I'm going to be interfacing most frequently and where the consequences are the highest and the most severe if I can't speak enough of the language to participate?"

If that is in working with the operations team in your organisation, and they're using Markov models to make predictions about supply chain and you don't understand that well enough, then that's an area where you need to seek out some learning to develop that 30% to be able to converse.  So a lot of it, I think, needs to be self-directed, and like I said, with LinkedIn Learning, with university extension and executive education courses, there are lots of great, low cost, easy, asynchronous kinds of places that we can do that.

I think the imperative for companies though, moving forward, is going to be providing some of that essential training inhouse where we know that our employees need it en masse, but also making sure that we have budgeted in time in our employees' schedules for them to be doing the kind of learning that maybe happens outside the organisation, and supporting that.  Whether that's supporting that with some kinds of tuition reimbursement or PTO to be able to attend these sessions, today's good organisations need to recognise that making an investment in employee learning is absolutely essential to not only getting them to that 30% but helping them to stay there. 

David Green: It's interesting actually, we know through the myHRfuture Academy that we deliver both to individuals and to organisations, that the appetite from HR professionals to learn is definitely there.

Paul Leonardi: Yes, very high.

David Green: So, it's just that organisations need to support that.

Well, you talked about there's a gap sometimes on the individual side, but what about a gap on the organisations that really want to lead in this area and those that get stuck behind; is that gap expanding or contracting?  Obviously you've worked with different organisations, you might not be able to name names of those that aren't doing this very well, but you might be able to give some examples of companies, along with Atos that you already mentioned, that are doing this well, that are helping people close this gap and get to that 30%.

Paul Leonardi: Yeah.  Well, I like to think about this in terms of, again, all those little undergraduate things that you remember, but remember Maslow's hierarchy of needs, and you start at the basic level, you need to have your safety needs met and your physiological needs met, and then you can move up that pyramid to get to self-actualisation needs; I think that that's a useful way to think about where companies can fall on this. 

If we're haemorrhaging employees and we're haemorrhaging money and we can't close our sales and we can't do these kinds of things, probably investing in helping employees develop the right skills to have a digital mindset is not going to be top of mind for most organisations, and it's a difficult putt to try to get them to shift energy and resources in that direction.  But I think that acquisition of those skills and moving into this digital mindset isn't something that you wait until the top that pyramid to do.  It needs to happen a pretty low level, because it's the thing that helps catalyse all kinds of productive behaviours in companies in the digital age. 

So, yes, I've worked with a number of companies that I would say are not huge, they're not extremely technically-sophisticated, but have embraced this idea of developing a digital mindset, and I think one of the big ways that they do that is that they have created policies for employees to be able to go out and get the kind of learning that they need.

I worked with a company recently, and I wasn't involved in this initiative but I was really impressed by it, that part of the performance, or their annual review process, was for leaders to work with the employees to develop a roadmap of the skills that they needed to develop to start to move into different kinds of roles within the organisation.  Some of those were formal roles, like if you wanted to move from manager one to manager two, like what are the skills you need to do that, but some are much more informal roles like, "This employee has an interest in personnel and they want to be able to interface more closely with the HR organisation to learn some of these kinds of things, and that's an informal role that we could create for this person that would really help us in our team.  So, what is the kind of knowledge that somebody needs to be able to be an effective interlocuter in those kinds of conversations?"

So, what they do, and not just develop a performance plan, but they would develop an education plan for the employees about the skills you need to learn to achieve those formal and informal roles.  The company backed up that education plan with a certain allowance for employees to be able to go and take trainings that they needed, a certain number of extra days of PTO that were applied for educational purposes.  So, that demonstrated a real commitment to employees to improve their skills in these kinds of areas.

Where I did get involved, and this was incidental to a different consulting project I was working on with them, is we had a group that was in one of the divisions that was really gung-ho about this, and was really aggressive about making sure that they were encouraging employees to take their PTO to do this, and there was another group that didn't.  We just looked at the data over about a three-year period in those formal roles, because those were the only ones we had data about, but did people move to the formal roles more quickly if they had done more of these kinds of trainings? 

It turned out that they did, there was a significant difference across these two groups in the speed at which they moved.  They didn't move quite twice as fast, but it on the order of maybe like, if it was going to take normally on average a year and a half to move to this new role, someone did it in about 13 months, or 14 months.  So, the trainings and the commitment to allowing employees to develop an educational pathway really made a difference in their upward mobility within the organisation, which is great for the company in many ways; it increases retention and employee engagement, all of these kinds of activities.

So I think that, in order to take this on as a company, you have to have this mindset of continuous learning.  It's that our employees are going to be most effective as individuals, as humans, as employees, as workers, if we can give them opportunities to constantly develop their skills, but they can't do that without our help.

David Green: How might this, and it's a leading question, how might this impact the approach that companies take towards workforce planning, for example, if we think about the whole digitisation agenda that's happening as well?

Paul Leonardi: Yeah, well, especially in a tight labour market, we want to make sure that we are creating the right employee skills internally to allocate our employee effort into the places that we need it.  So, in labour markets that are not so tight, the default strategy is, "Well, we just go hire from the outside with the skills that we need", but there's lots of evidence to suggest that there are major benefits for having more internal mobility on a number of dimensions.  One is that, as organisations tend to get flatter over time, there are fewer opportunities for vertical mobility and so we need to create more pathways for horizontal mobility in organisations, and we need to reward that, incentivise that and create the right kind of status around that.

When we think of workforce planning, if we can create more horizontal avenues for employees to move that increase their domain knowledge, increase their technical responsibility and give them a sense of career progression that just is horizonal rather than vertical, we do a great job.  There's a lot of evidence to suggest those employees stay longer, so that reduces our turnover and replacement costs, they tend to be happier, more motivated, committed employees, and it blunts against this problem of not being able to find enough outside talent.  So, we can't do that without arming our employees with the right kinds of knowledge and skills, and we can't arm with those if we don't take a more comprehensive view to our employee learning programmes.

David Green: So, this is the last question, this is the one we're asking everyone on this series, what do you believe to be the two to three things that the HR will need to do to really add business value as we come out of the other side, hopefully, of the pandemic?

Paul Leonardi: Sure.  Well I think the first thing that they can do, that they'll need to do, is to figure out how to be trusted advisers, and I don’t just even mean strategic partners, but HR I think has the capability and potential to be trusted advisors to our businesses.  That means that when, as a leader, I'm running into an issue with hiring, with staffing my team, with employee motivation or retention, that there is someone I can go to to get insights and advice, not to do paperwork, but to get real actionable insights that will allow me to make the best decision, and that the decision is not being foisted upon me by somebody else.  That's the key movement, I think, that HR needs to do to really, really add value, and I think HR, for all the reasons that we've talked about throughout this great conversation, is uniquely positioned to do.

The first obvious place for HR to step in and help about that is about being that trusted advisor around managing a distributed, remote workforce, because there are so many changes that we're starting to see and that we are going to continue to see as they accelerate as we try this great experiment of the hybrid/remote/flex office environment. 

The best way to figure out what is the right kind of model of constructing a team, or do we have employees that are fully remote, or they flex their schedules throughout the day, or do we bring people on site in the office, is through trying different scenarios out and collecting data about whether those scenarios work, how well they work to meet key objectives that we care about, and using those data to then create policies for our various teams, and I don't mean formal but informal, about what does this hybrid work environment look like.

Every leader I talk to in a business is worried about this, they don't know what to do, and this is where HR can again become that trusted advisor.  The pandemic I think is providing, and will provide, this unparalleled opportunity to accelerate this transition from HR to this trusted advisor role if the HR organisation can take advantage of it in the right way by partnering.

David Green: Perfect, and a key part of that is the HR organisation getting that 30% rule that we've spoken about as well.

Paul Leonardi: Exactly, and helping all the teams to develop that 30%.

David Green: Well, Paul, that's a great point to leave this.  Thank you so much for being a guest on the Digital HR Leaders podcast.  Please can you let listeners know how they can stay in touch with you, follow you on social media and find out more about the book?

Paul Leonardi: Yeah, great, well lots of opportunities.  You can see my personal website, it's at paulleonardi.com, it's very phonetic, just sound it out; you can follow me, @pleonardi1 at Twitter; I'm also on LinkedIn and always happy to connect and enjoy having conversations on LinkedIn.  You can find the book at your local bookstore or Amazon or Bookshop, wherever you liked to purchase The Digital Mindset: What it Really Takes to Thrive in the Age of Data, Algorithms and AI.

David Green: Well, thanks very much, Paul.  I've thoroughly enjoyed our conversation.

Paul Leonardi: Me too.

David Green: I've learnt a lot in the last hour or so. 

Paul Leonardi: Great.  Thanks so much, David.