This week’s podcast guest is René Gessenich, Head of Strategic Workforce Planning at Novartis, who shares the work that he’s been doing to build a skills operating system for workforce planning at Novartis.

In this episode, René and I discuss:

• Why having a robust skills ontology is so important

• How to incorporate internal and external skills data, in a single skills ontology

• Novartis's approach to skills-based workforce planning with some examples of their targeted approach

• Specific advice for organisations looking to adopt a skills-based approach to workforce planning

Support for this podcast comes from Medallia. You can learn more by visiting https://www.medallia.com/employee-experience/.

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 René Gessenich, Head of Strategic Workforce Planning at Novartis to The Digital HR Leaders Podcast. René, it is great to have you on the show. Can you introduce yourself and tell us a little bit about your role at Novartis?

René Gessenich: Hi David, thrilled to be here. Thanks so much for inviting me.

My name is René Gessenich. I have spent roughly the last 13 years in various different pharma companies and I joined Novartis at the beginning of last year. As you mentioned, I am heading up the global workforce planning practice at Novartis, and that is my main responsibility. 
I took on an additional role, which is also leading a mid-sized, cross-functional, team that is figuring out our approach to skills and what that entails so our skills strategy, or skills operating system as we like to call it now.

And for the ones that don't know Novartis, Novartis is a focused medicines company with 106,000 associates, so quite a large enterprise, and touching almost 1/10th of the world's population. Reaching 770 million patients worldwide, on an annual basis.

So that is my current role at Novartis. 
 That is brilliant René. Actually it is nice that in the two and a half years we have been doing the podcast, you are the third guest that we have had from Novartis. We had Simon Brown, Chief Learning Officer, who I know you probably interact a little bit with some of the work that you are doing and Stephen Baert, the former Chief People Officer as well. And I think, for those that have listened and those that haven't, I think a lot of the work that you are doing in people and organisation at Novartis, is really interesting and I know we are going to talk about that now. I will definitely come back to the cross-functional role because I think that is important as we talk about skills.

But skills is pretty much what we are going to talk about today. We are going to talk about some of the innovative work that you are doing in workforce planning at Novartis. But before we jump in, let's start by discussing the foundation, a single skills taxonomy.

Can you tell us a little bit about the skills operating system you are building and why it is so important to get this right? 
That is an interesting question and I think the taxonomy bit is one piece of it, but like you mentioned, we want to think about this more as really an operating system.
 So if you think about your phone, you have your Android, your iOS, and you have different applications that run on that phone. And what we really want to figure out is the backbone, the foundations, that then new applications can come in and others might go off that operating system. We started on that journey roughly one and a half years ago and it is an interesting piece, right?

So, everywhere you hear about skills, you might even say people have become skills obsessed. It is really hard to understand where you actually want to start and what problem you want to solve first, because as soon as you dig into it, you could say let's start with skill based workforce planning. Let’s make our learning approach more skill centric. Let's think about skill-based rewards, skill-based incentives, etc. So you go down all these different rabbit holes before you say, well this is really the problem we need to solve.

I think it took us some time to really frame this problem around this operating system, which of course consists of one common taxonomy and it is really about the things that need to be consistent across your HR solutions. So it is your taxonomy. It is your job architecture and how that relates to skills. It is how you assess for skills. It is the employee experience, you don't want your associates to go into four different systems for example, a marketplace, a learning experience platform, or your Workday backbone, to add their skills into a system. 
And if you think about these different elements that need to be consistent, it is already quite a big chunk, quite a big thing, to figure out and that is what we have been continuously working through and working out. To be able to say, okay, these are the different bits and pieces of the puzzle that is the operating system, that we need to make consistent in order to have the current applications. Which for us is Workday as a backbone, then Gloat as a talent marketplace, and Edcast as a learning experience platform to run on. But we also need to build something that kind of is sustainable for potentially a talent acquisition solution coming in the future, or a skill-based rewards, or any of these other elements that you might come in that we don't even know of, that might be coming into the talent marketplace in a couple of years time.

That is our main focus and scope of this work right now. 


David Green: It is fascinating, isn't it? We have had a few guests on, particularly in the last year, talking about how they are taking more of a skills based approach to workforce planning. We had Anshul Sheopuri from IBM, on just before Christmas and he talked about skills as that single thread that links together all the different parts of the employee life cycle. That is what we have seen and what we have commented on in a podcast that Ian and I, did at the start of 2020 is that it is almost like skills acting to break down some of those silos that we naturally have had within HR, learning, talent management, and talent acquisition, and skills seem to be the thing that actually acts as a thread through that. And obviously you have thought about that at Novartis, by the fact that you are running a cross-functional team around how you create this. 
What are some of the areas of HR and even the business that are involved in that cross functional team? 


René Gessenich: Yeah. So it's definitely, first of all, the big functions in HR and that want to understand that. You talked about Simon Brown, our Chief Learning Officer being on about our ambition about up-skilling and re-skilling, so learning is a really big component of that, talent management, talent acquisition, and rewards. First of all, it is the different HR functions you work with but also, quite early on, we gathered that there is this big appetite for a lot of this data that we either can gather from associates, the skills we have, but also the skills that actually are required, from a labour market perspective, in different functions.

So for competitive intelligence, it is interesting to look at skills data for example, from job postings you can gather through companies like Emsi Burning Glass or HR Forecast. So it has become a much larger kind of program where at the core it is the HR, but we have extended even into areas like procurement that want to source contingent labor and then want to use a similar kind of skill taxonomy as we are, for example, for our co-workers. So it is becoming that uniform language that everything that touches an employee that you work with, your workforce, is supposed to be able to describe.

But there are a lot of side benefits like competitive intelligence which I mentioned that you might not even have thought of at the beginning, but you come across once you start working on this.

I think the red thread idea is still the dominant one, that it starts in HR but then there are all these other layered benefits on top of it that you start to explore once you go into that work. 


David Green: And you talked a little bit about internal and external skills data there. How do you incorporate both those data sources, internal and external skills data, in to the skills operating system? And is there an optimum balance? 


René Gessenich: Yes. I think people often talk about this in the realm of talking about a taxonomy. So should I just simply take an off the shelf taxonomy, that I can get from Emsi Burning Glass or should I develop it entirely by myself? And I think we had these conversations, but then said, okay let's think about this practically. Let's think about some design principles. How we develop our skill taxonomy and then also let's think about the different data points and things that need to merge. 
I think, for us, it was very clear. First of all, we needed something that was independent of one system because we wanted to build an operating system that works for new systems that we want to plug in. We wanted something that is dynamic, that updates. And we wanted something that is very broad so it is really able to represent our entire workforce and is very deep. We actually had the luxury of having a skill taxonomy in place, linked to our job architecture that was built manually, and it was built in an annual process where we would ask a bunch of people and say, okay, what are the skills you need? And we found out that it was actually not representative of the work people did and it was not very specific to us as a pharma company. So we experimented a little bit with taxonomies like Emsi Burning Glass, and found it is really, really good. They are really good at representing because they leverage big data, like job postings, to understand the type of work people do and then infer skills from it. They actually were quite specific to us, as a pharma company, so that was interesting.

Probably the off the shelf, market ontology or taxonomy, will provide 90% of what we need, but what about this 10%? We said let's create a process. So we said we can crowdsource these skills. So we allowed every associate that wants to add a new skill, to propose that to us. We have established the role of a skills architect, who is working with the businesses and is also looking at these crowdsourced skills, and then actively curating that taxonomy. 
So that is creating that language that works externally, internally, but also beyond that. When you think about the data, we absolutely want to merge our external labour market data on trends to understand, for example, expected skills for a certain role and the actual skills our associates show and put in their profile, for example, in talent marketplace. 
But having that taxonomy that actually works with labour market data internally with our own associates and what they produce, is the starting point for that.

I think it is something that took us a while to figure out, but I think some of these players out there can really help you to really give a boost and really help you to enrich the quality of your taxonomy, perhaps with the language first but then also work with these different datasets in sync, on one language. 


David Green: And I guess what the external data sources can do, is they can give you a little bit of a window into what your competitors may be doing, both your traditional competitors and maybe newer competitors that Novartis may be coming up against, depending on where you are taking your business strategy. Because ultimately, workforce planning is about helping your business strategy be successful, isn’t it?

René Gessenich: Yeah, absolutely. There is a great source, I think Elon Musk, was even talking about how he could figure out that Google was building a self-driving car, by hiring a thousand engineers to do it. So it is this competitive insight you can get before somebody actually makes something public. 
And it is similar, we have been using that to understand very key trends. So for example, remote or virtual clinical trials, were something that we really wanted to try to figure out and understand. You could really selectively try to search for job postings in that field, across the pharma industry, to understand who is doing it and who is not. 
But also there are more basic things like even for a job architecture to link the top 10, top 15, skills to a job and what is required and expected is something you can do so much better just based on job postings and what pharma companies, or leading companies in the field, are expecting for a certain job versus just trying to create that manually through conversations. 
So I think there is many different applications for that data, from a competitive intelligence standpoint, but even for having your basic data architecture and your foundation work, where you can leverage this type of data. 


David Green: I sense that we can have a podcast episode on just that topic alone, so we will move on a little bit now, otherwise I think we will get lost in that. 
I wonder, what are some of the myths and challenges that you hear often, when it comes to leveraging skills as the new currency, that is a bit of a phrase that we hear a lot, and can you bust some of these for listeners? 


René Gessenich: Yeah, I think I buy into the skills as a red thread, otherwise I wouldn't be working in this cross-functional project team and trying to lead this work. 
But I think when you go a bit deeper and you are trying to really explore, sometimes the meaning of a skill comes more from the context, especially the ones that are more high level and I think project management is a good example. How you set up your taxonomy is a big part. It needs to be very specific and detailed and granular in order to be meaningful. Otherwise, if it is very high level, like project management, project management for rolling out Workday is very different from project management for managing your own wedding or a child's birthday or something else. So even though we say it is all project management, the type of work and the type of skills that are required, is often very different.

So context is often very important in order to describe a skill and to really have a common definition that provides that red thread.

I think, people often tend to oversimplify that and forget about some of the nuances that you actually need to be able to match work with people, which essentially is often what you are trying to accomplish by using skill data.

There are a couple of others. So going back to the labour market data, I think we often get very excited, as we see by the World Economic Forum or the ILO, but I think something I have learned now, more and more, from working with this data is you really need to dig deep to understand the source. You need to understand, for example, simple things in job postings. How does a vendor make sure he is not duplicating a job posting that is posted on 10 different job boards? So really the data quality piece is important in order to also draw robust insights from it.

I think the last one we are struggling a lot with, is the translation between different taxonomies. Because we have different systems, using different taxonomies, and we create that master taxonomy that plays in between them. 
It seems easy at first. But then as that context is so important, it is like translating between different languages. You might know a one-to-one comparison, but is it really describing the same thing? Maybe not.

So there are a couple of challenges on the data side that are not so easy to figure out and that people sometimes oversimplify when they jump into that topic. 


David Green: And in terms of that data challenge, what are some of the steps that you are taking to mitigate those? 


René Gessenich: So I think, from a data quality perspective when we think about labour market data, we did a big RFP and have now selected the most robust vendor in that space that provides the best kind of labour market data. 
The other piece for the translation, is something we are actually working on with these different vendors, that help working with them to create translations between their different taxonomies so that when we work with Gloat on the talent marketplace and Edcast as a learning experience platform, that they actually help us to translate between that taxonomy and do this well. So from a user perspective, you go into one system and add your skills and it is being translated to another taxonomy and another system.

The last piece, of course we invest in our data architecture. We try to get data from these different systems out and then we try to translate, from these different systems, into one joint master taxonomy. 
And that is what we are trying to solve for, with that operating system in mind. 


David Green: Because it is so important, isn't it? Not just for the workforce planning perspective so you can understand where you are with skills as an organisation, but it is so important from a learning or a mobility position, because the recommendations are only as good as the data that it has come from. 


René Gessenich: Yes, absolutely. And that is the red thread, right? So workforce planning and trying to understand what you need in the future. What you have. What the gaps are. How you close these gaps. And then of course, closing these gaps is exactly that connection to your learning systems, to then actually build certain skills that you have identified as critical gaps. 
And if that is not in one language and it doesn't work together, then of course you cannot really action upon some of your plans. That is exactly what we are trying to accomplish. 


David Green: Great. So, we have got the skills. Let's say we have got the skills data where we want it to be. And as you said, you can then use it in the different systems that you are using for different parts of the talent life cycle.

So let's turn now to look at skills based workforce planning at Novartis, in more detail. You are a big advocate for a nimble, almost pilot based approach, to workforce planning. Can you tell us a little bit more about this approach and why you think it works?

René Gessenich: Yeah, so I think from a fundamental framework for workforce planning, where you talk about business strategy, future demand, your supply gap, and then closing this gap, we are very close to that. So I think that is your standard framework for workforce planning, but the details below that, when you think about skill-based workforce planning, it is not just predicting one variable, which is headcount, it is really trying to predict many variables. Which is really the composition of skills within one job, the composition of how many jobs you need for a certain organisation in the future, how these jobs might work with each other. So it becomes a very intricate and complicated thing to solve for because there are many different variables and on top of that, you have a pharma company that is a very complicated net of a value chain. So it is really hard to actually do that at scale for many organisations at once. Also, it is very hard to do that in a way that is quite future focused.

Therefore we instead opt for an approach that tries to be very targeted. 
So we really try to zoom into areas of the business that either, will face tremendous change from market pressure or from our customers and how they are changing, or very important for us from our business strategy perspective, to really zoom into key pockets, which has maybe 500 or 1000 associates, and really try to make an impact and really try to action. 
So the part that people often miss about workforce planning is that the planning is only 10% of the energy, 90% is the execution. So we are really trying to adopt this mindset. It is almost a venture capitalist that is trying to look at key areas will be very important for us in five years, try to understand them with as much data as we can from the labour market, from internally, and come up with a robust plan to then action upon it, learn from it, and then scale it, potentially for the entire enterprise. 
That is what we are trying to implement and then have been some great results in these different pockets of the organisation, but we tried to pilot an approach in this way. 


David Green: You are right, Novartis is a big organisation, it is a complex organisation, it is a global organisation and as you said, trying to do workforce planning for the whole company straight away, from a skills based perspective, you have got to focus on the most important areas as you said, that is a key bit of advice for anyone listening.


René Gessenich: Yeah, absolutely. And I think that is how you can show impact. So I think it is also creating momentum behind workforce planning and by staying targeted and creating action upon your plans is very important.

But I think also it is a way to innovate. So it is a way to think how you change your company.

I think your company would always need to perform, so it is transforming versus performing, or it is the innovative dilemma. And what we are trying to play with is really moving beyond your obvious business model that you just play out for the next three to five years, into an area where we innovate this dilemma. What are new business models for a different business, or really radically different ways of approaching a certain problem, and really trying to figure that out at a small scale. Then learning from that so we are better prepared in 2, 3, 4, or 5 years when this future might arrive and we might need to do it at scale.

That is the thinking we are trying to foster with this approach.

David Green: Can you share one or two examples where you have applied this approach and maybe the related skills that you have targeted?


René Gessenich: Yeah, absolutely. So I think a good example is our drug development organisation. So roughly 13,000 associates and a very heavily regulated process on how you develop drugs. It is very hard to even change one wheel of the whole thing.

We wanted to look at a part of that organisation that is actually dealing with data. So we looked at a part of the organisation that is called data operations. These are statistical programmers, these are people that develop databases, these are people that acquire data. There is an interesting trend, also really accelerated by the pandemic, that when you want to develop drugs, historically patients would come to the facility and you would administer a drug, you could monitor them onsite, versus now having more and more remote clinical trials. So you pretty much shipped the drug there and you try to sensor, for example via a smartwatch or via an application, where a patient puts in certain data abut how he feels or there are sensors on their body showing how he is actually feeling, how he is doing. And it is a very different set of data, very unstructured, that we want to now incorporate into our clinical trials.

There was an example where we started to think, how do we cope with that? How do we do that? So we need different types of profiles that are able to, for example, acquire data that might be third-party data that has nothing to do with our clinical trials, that we can then incorporate and triangulate with our clinical trial data. And what that would mean in detail is something we try to figure out for that very small sub population, trying to figure it out, trying to say these are the new role profiles. These are the skill sets we need. And then let's start acting upon that. Learn at small scale, and then maybe that is the way to do drug development at large in the future. But we are then prepared because we have tried it in a small subset for some subset of our clinical trials. 


David Green: And then I think you have got another example related to building up a global ops centre as well?

René Gessenich: Yes. So that is another interesting one, where we are currently working on moving everything that doesn't need to be on the actual manufacturing sites on site, into global operation centres in Slovenia and Hyderabad. And of course what comes with that is you also move a lot of the planning that used to happen in the different sites into central hubs. 
So supply chain planning will be tremendously important in the future and in a very complex way. We have heard about disrupted supply chains also during the pandemic and we really wanted to figure out, for pharma, if we want to move all this activity into central hubs, what would that mean? And supply chain planning is really being lifted, being much more data-driven, using big data, really being able to understand the interdependencies of large systems and how if I move something here that changes some of the dependencies. And really redefining what a supply chain function does and what are the different roles needed to be successful in that new setup. 
And that was another interesting piece. Again, we looked at a very small niche kind of subpopulation we looked at versus everything you could look at in manufacturing. We have more than 20,000 people in manufacturing, these were 200, 300 people in supply chain in these actual roles, that we wanted to figure out how their work is changing, how their skill sets are changing in the future.

David Green: And going to this sort of granularity level and looking at specific businesses or functions. What is the business benefit of this approach? How do you ensure that the work leads to action and then value? Because as you said, 90% of the process is actually about execution, only 10% of it is the planning.

René Gessenich: First of all, I think it's trying to create clarity often, again, going back to the labour market data, what our competitors are doing, how the market is moving. What are novel approaches? You can read a lot from just reading a job posting of your competitors and just aggregating that up and showing that to business leaders. 
But then again, that targeted approach helps them to actually not be completely overwhelmed by the change, but saying, these are really the 2, 3, 4 or 5 things I can do now to be prepared for the future. To future ready myself, my organisation, or the talent in my organisation. And it is also something that is actually high effort on the HR side, to prepare the analysis and the data, to prepare the discussion, the conversation about it. From a business leader perspective, it is really some key interactions where you are just playback and show them what is happening, where you might also do some interviews and playback what the organisation thinks. 
And then it is really about taking some really quick, targeted actions to move in a certain direction and learn from it. So from an effort amount of mind space that you take up in a leader space, it is actually not so much, but it is still helping tremendously to get in that kind of future-proofing, future ready, mindset and also driving targeted action from it. 


David Green: Is it difficult to prioritise and then maintain the boundaries of this targeted approach? Because I am guessing you have got lots of requests, lots of business partners within P&O, as well as HR coming to you with requests for the businesses and the markets that they serve. How do you prioritise? 


René Gessenich: I think it is just human nature, wanting to know more. So if I now know how this piece works in the future, I want to know how this and this and all the other pieces work as well. 
So I think that is just human nature and there is of course, always more appetite to understand the bigger picture in different parts of the organisation. So I think that is just normal. I think it is a challenge really to maintain a targeted and focused approach and sometimes yes, you need large transformations. If I know my customer base is changing radically and I need to transform my entire commercial model, then yes you need to do that. Sometimes the targeted approach is not the right one. But I think we are really trying to go through a set of questions with business leaders, with business partners, to really say this targeted approach, where it is really about future-proofing that five-year time horizon versus the one, two year time horizon is the right thing for you. Are you ready to take these kinds of calculated risks with us because they might be wrong, right? If you take that venture capital mindset, it is high risk, but high return. So you take a bet on something that might never come, because often the signals are not as strong as you might have when you really know that the entire landscape has changed and so I have to change my business model, versus I sense there is a change in customer behaviour. I sense there is a change in how the regulatory agencies are dealing with certain issues and I want to be prepared. It is a lot weaker to think on, but if you are prepared, the benefits are of course a lot larger in the long run. But I think it is the set of questions that we would like to work with and guide leaders through. And business partners, like you mentioned, that is definitely a challenge every time you want to be focused and targeted not to get diluted and focus on different sets of challenges that might be also relevant in the moment and urgent, but might not yield that long-term outcome that you are actually striving for. 


David Green: Obviously you have provided some advice already, so you might summarise or there might be some additional. What advice would you give to other workforce planning teams looking to adopt this targeted approach? 


René Gessenich: So I think in my experience, working with leaders who are really in to this, leaders that buy into that vision and are able to pull this through work with their leadership teams, and really showing results. 
So I think that is the best. I think the worst thing is theorising and theorising versus actually starting doing. I have been fortunate to really find some exceptional leaders who said, we really want to understand this experiment and I think we will have a need for it. And it is creating outstanding use cases then that you can share across with different leaders to say, that is how I feel it should be done and that is the benefit you get out of it and you make it tangible. The first step is just doing it and experimenting and having leaders that are excited, but also forgiving if it's not all perfect, the first time you start working in that way with them. And I have been very fortunate to find a couple of those across Novartis. 


David Green: And I suppose, as you said, finding that leader or that sponsor within the business, who recognises they have got a problem or thinks they might have a problem or wants to find out, and it can actually take decisions and can do the execution, that you talked about earlier as well. That is absolutely key, isn't it?

René Gessenich: 
That is absolutely key. I don't know if there is an easy guide to doing that, but I think it is something where you just build your network and you find people who might be known for similar things. Maybe not just in the HR space, but in other areas too and you just work with them. But I think that is a critical first step.

We also try to say we want to be super analytical about the area we pilot, or we experiment, or introduce that. I think it is really better to look for leaders who are really willing to support this and push this through because you need their energy and effort, not just for the 10%, but for the 90% later on to execute on your plans. 


David Green: Yes, because the planning, although it is only 10%, it has actually got a lot of effort and a lot of time, particularly if you have got a relatively small team. Then if the execution doesn't follow, then you can argue that planning is a bit of a waste of time. 


René Gessenich: Pretty much, absolutely, and I think that people are sometimes missing that when they talk about workforce planning. The ultimate outcome and goal is to drive action out of your plan and not just to have a nice, shiny plan. 
And I think that is something also that you need to manage expectations with stakeholders early on.

David Green: The next question, I will cloak it by saying that I appreciate you have only been at Novartis for just over a year, so maybe this might be a conversation for a second podcast down the road. But how do you, or are you, going to approach scaling the targeted approach?

René Gessenich: 
So it is kind of a polarity, right? Scaling a targeted approach. But in essence, we strongly believe that the HR business partner is best positioned to apply and run these methodologies. So we are going to run a couple of bootcamps with HR business partners to actually enable them. 
But we also want to make sure that it is not something they need to do. So it is not something that we want to make an annual process, that everybody needs to run, but it is really based on a need and a pool from the business. So if there is a need in the larger transformation or an area that is very critical for our organisation, something that we might see in the data that is transforming radically on the labour market, there we really want to zoom in.

But I think it is this opt in approach for business partners and leaders versus saying we force top-down an approach onto everyone, that we will try to support. And I think that is very much also in the culture and DNA of Novartis, where we talk a lot about being unbossed. It is really an offering that we give and that offering we are going to give to the organisation and then have very much a pull approach in different directions, to scale it to an extent where it is needed. So that is our philosophy. 


David Green: I think you made a very key point there actually, the great thing is obviously you get the pull from the business, if you have got those stakeholder relationships with the business and they have got a need, they will come to you and you will better work on them for that. 
But the great thing about the external data that you are collecting, is that gives you the push so you can say, we are seeing this in the data, do you want to investigate this further to see how relevant it is for your business? I think that great balance that, whether it is a people analytics team or it is a workforce planning team, by having that data, the external data, you can actually help. 
Again, we have had a previous guest on the podcast who said, help the organisation see around corners. Which I think is a great analogy.

René Gessenich: Yes, absolutely. And I think our role also as workforce planner or people analytics practitioner, is sometimes to challenge and to provide a different perspective and also disagree sometimes. And like you said, you need to do that based on good data. I think, like you mentioned, the labour market data is a key piece of information you can take into these conversations to actually say, I beg to differ and there might be other ways of doing things. So it needs to be these two sides, like you highlighted, which is the pull but also being able to have your own opinion and strive in to a certain direction. 


David Green: Now on to the last question, we are going to go to a topic that you have mentioned briefly at the start, around employee experience. This is the question that we are asking everyone on this particular series of The Digital HR Leaders Podcast and I would be really interested to hear your view from the context and the work that you are doing at Novartis. What is the future of employee experience in 2022? 


René Gessenich: It is going to be in an interesting journey, when you think about hybrid work and all the big things we have on our minds. I think many companies will embrace really radical flexibility, in terms of location, in terms of time, in terms of ways of working. But I also feel more and more companies need to resolve for, when you think about the great resignation, what is the purpose of this organisation? How do we bring people together and align them behind one purpose?

What I have seen also now, within Novartis is more and more, what we are trying to accomplish is the polarity between the individual and the organisation to bring that together on team level. And that is something we have been doing more and more. When we think about our new performance management. When we think about the how conversations, how do we want to work together as a team?

And I am not sure if we are going to see more technology around that or different pieces around, how do you bring teams together to work effectively. To be able to be radically flexible as an individual and provide as much flexibility, but still be able to be a functioning, high performing, purpose driven team. I think that is something that we are going to see a lot more communication around versus often what you hear, what the entire organisation thinks about working from the office from Tuesday to Thursday, which is meaningless, towards, how does this team perform best? And that might be completely different setups, completely different ways of working, etc. 
I think that is a key trend that we are going to see accelerating more and more. 


David Green: And of course the skills data ultimately can have a role in helping to shape the employee experience as well.

René Gessenich: I Hope so. I hope that we were able to draw some meaningful insights into either a link, in terms of what skills are most relevant for certain individuals, what skills are the most employable within an organisation. But also when you think about employee experience, a big topic for us is really up-skilling and re-skilling. So I talked about this hundred hours at Novartis, when you are able to predict in decline skills for example, and you are able to predict that earlier for an associate to then act upon this intentionally rather than saying, you are not employable anymore. Rather than saying, well we know that this job or this skill will go away and it is going to be either automated, or it is going to be offshored, or whatever, and we are trying to act up on this intentionally. I think that will make a big difference to a lot of associates knowing that you actively think about this and care for them and think about their development. While we still of course, need to push for accountability on the associate level to actually drive their own development, but I think that is going to be a massive change. 


David Green: René, thank you so much for being a guest on The Digital HR Leaders Podcast. I really enjoyed our conversation. 
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?

René Gessenich: Well all these good things, that you just mentioned. So very happy to stay connected with many like-minded people that think about workforce planning, but also think about skills. Always happy if they want to reach out. Thank you so much for having me. I really enjoyed the conversation as well and very curious also to hear about a lot more guests on your podcast and hear what they have to say about skills and of course, workforce planning. 


David Green: Thank you René. It certainly was a big topic of conversation on the podcast throughout 2021 and I suspect it will be even more so as we go into this year, 2022 and beyond. So it has been great to have you on the show and I know listeners will benefit a lot from listening to the work that you have been doing at that Novartis.