Episode 5: How to Build a People Analytics Team in a Global Organisation (Interview with Eden Britt, Group Head of People Analytics at HSBC)
Welcome to the fifth and final episode of the first series of the Digital HR Leaders podcast. This time, we look at how to build a people analytics team in a global organisation. Being the Global Head of People Analytics in a large multinational is an exciting but at the same time challenging role. Building and structuring the team, developing your operating model, identifying the right projects and business sponsors as well as turning insights into outcomes, are just some of the key responsibilities involved. Factor in a new HR System that will go live simultaneously in over 80 countries to 250,000 employees and you really have a challenge on your hands.
That’s the topic of this week’s podcast. You can listen below or by visiting the podcast website here.
Our guest today faced this mountain and climbed successfully to the summit. Eden Britt combines being Group Head of People Analytics with the role of Chief Data Officer for HR at HSBC. He is one of the leading and most respected leaders in the People Analytics space and I always enjoy speaking to him.
In our podcast Eden and I discuss:
Highlights and key learnings from the recent implementation of SAP SuccessFactors at HSBC and how this is helping drive people analytics
What is involved in the Chief Data Officer for HR role, a role that is growing in a number of organisations around the world
Eden’s insights on how he has built and structured the people analytics team at HSBC, including the mix between Global and local delivery
Key challenges Eden has encountered and details of a couple of interesting projects the team has delivered
What excites Eden most about people analytics along with his biggest concern
How the role of HR will evolve by 2025
This episode is a must listen for anyone working in or interested in the people analytics space as well as anyone about to embark on or currently involved in an HR transformation.
Support for this podcast is brought to you by Culture Amp - find out more at cultureamp.com.
INTERVIEW TRANSCRIPT
David Green: Eden, welcome to the Digital HR Leaders show, it's great to have you here.
Eden Britt: Thank you.
David Green: Would you like to give yourself an introduction to yourself? What your role is and also your vision around people analytics as well?
Eden Britt: So, Eden Britt, obviously. I am at HSBC. I've been there for the last three years and I have a relatively broad mandate.
So my role is essentially split into two different areas. I look after the people analytics function. And I look after the Chief Data Office. So I'm Chief Data Officer for HR and my background is really 20 years in HR. I've done a number of different roles. I started in recruitment. I've worked in different parts of human resources, and I've spent 15 years of my career outside of the UK. So I worked in the Middle East for nine years out in Dubai. I was working with Cisco Systems and then I moved to Singapore and I joined Standard Chartered Bank. And when I was in Singapore was really where I moved into the analytics space.
So from a early age, I've been interested in computers. I actually did a degree that was combined Information Technology with Classical Music which which is quite interesting is quite a strange double major but on reflection now, the things that we learned as coders in 91 to 94. Before the internet pre-Netscape, and what we know is the internet today.
We learned SQL coding, we learned object-oriented coding and we learned database programming on VAX VMS Mainframe type platforms and it's interesting that that skill set is really what's come back now and to help with large data projects, certainly managing bigger datasets, the understanding of how to code. Particularly Python and R and other things has really helped and I think from a music perspective, there is a lot of evidence that suggests that musician's brains work in slightly different ways. And I think pattern recognition makes a great part in the investigative, the detective part of analytics.
So I think that that balance of IT, the pattern recognition music/musicality part and then the experience in the context of HR has led me to be in a position that means that I really enjoy the role that I'm doing.
David Green: It's great. What I must do is I must send you an article that Thomas Rasmussen wrote about the link between analytics and music. I'll send that to you later on.
You've been at HSBC for over three years now, and obviously you've had a small matter of a big SuccessFactors implementation, which I think you completed last October?
Eden Britt: Yeah. We went live in August actually and then we came out of hypercare in October.
So in a period of making sure that everything was working in a live environment, but then we declared success in around about October.
David Green: And you did it big bang?
Eden Britt: We did, so we had a choice like most people do you can either roll out in phases or you can think about what the ultimate goal is for moving to a new platform.
So for us that was really around the experience of the employee making sure that we had a platform that enabled us to take advantage of a lot of the changes in HR Tech as we go forward and I think when you're in an on-premise platform like we were before on Oracle/PeopleSoft. You tend to have customised the platform quite a lot in different markets to cater for different laws and different ways of working and the beauty of work by moving to a cloud environment helps you to get on to much more standard processes, and then you tend to configure the environment rather than customising it.
And then the benefit of that is that the employee gets a similar experience and you can leverage things like mobile and other functionality and so for us we took the decision that what we would do is we would re-engineer all of our processes and we really go end-to-end through country localisation, all of the Global Services that we offer within HR and then ensure that we configured the environment correctly, but we'd also thought about what is the simplification that we need to do in HR to make sure that we are making sure that everything worked but also everything joins up. So the way I think about the HR life cycle is the employee life cycle. Somebody enters as an applicant or a candidate. They become an employee. They go through some mandatory initial learning and then we do the normal Performance Management cycle, Succession Planning, career Etc.
And so by thinking about processes around the employee life cycle, it really helps us to get that right.
So to do that properly we decided Big Bang would be the way to go. The balance of that is that you have a lot of work to do in a lot of different countries. And so, we're present in over 60 countries and we have different entities within those countries that makes it even more complicated. So to make sure that we were prepared, this was a two-and-a-half to three year program of work and I think at the point we went live we were the largest SAP implementation that went live in a big bang approach. So that for us, I think obviously was a lot of work but ended up being in a great place for us.
David Green: Well, congratulations, you've come out the other side. And from a people analytics perspective, what benefit is that implementation now offering you?
Eden Britt: So the good thing about it is that we managed to put reporting in an analytics work stream as part of the go live. So it's not technically part of the SAP product suite. We didn't take the workforce analytics solution from SAP. We decided we're going to build our own. But we put it as a workstream as part of that implementation. And so what we were able to do was get the data structures correct from the beginning and when you're moving from one environment to another environment, one of the difficulties is taking data that was in one structure and putting it into a different structure.
So the amount of data mapping, the amount of configuration that we needed to do, the amount of business validation on data that we needed to do meant that at go live, our data was probably the best that it had been in at least the last ten years. Now, of course that will degrade over time, If we don't get the governance and the quality assurance processes right, but at the point of go live we had great data in a new platform that was on a new environment and new infrastructure and we had built from scratch the reporting instance right from Foundation, new schema, new import from the new platform. And then built a BI tool on top of it.
So from the data that we can now use both from a reporting perspective and for other analytics work it puts us in a really really good space.
David Green: I think it's a great example, because I've seen so many organisations go through these big implementations whether it's SAP or one of the other big players in that space and they've not really considered the data and the analytics part until after they've implemented. You did it actually throughout the three years.
Eden Britt: And it wasn't easy and the data side is quite difficult. Right? So you've got to get around the HR function. You've got to have people understand why data is important. You've got to get them to think about what reports they need in the future and for what purpose and a lot of the time the energy is wasted not on the build part, the development part, but the energy is wasted in trying to get the ratification of the request. So the quicker and more structured that you can have the conversation with the end user to help them understand that the solution is not to rebuild the Excel spreadsheet that they hold in their hand. But the investment in time at that point will help you way, way better in the development stage.
So I think it's really important if you can get it in, really early on.
David Green: So talking about data Eden, in your introduction you explain that one of your two roles is Chief Data Officer for HR. What's actually involved in that role because there's not many people who've got those roles currently in the function?
Eden Britt: Yeah, and I think this is part of the evolution of HR when it starts to take data a bit more seriously. So if I split the role into three practice areas, the first area is data governance and that includes things like the ownership data within HR and predominantly, that will be a process. So a subject matter expert like recruitment would also own the data that's in their recruitment platform and also the data elements that get created through that process and then if you think about the way that data flows through the systems and the organisation, the recruitment process is the originator of most data in HR which creates from a candidate record an employee record and it's really important that we understand what data gets created by whom.
And then if we have problems with that data later, we need to go back to to look at the process or the integrations that might have caused the problems. So that first area of data governance is really important and it's the role that runs the data governance forum in HR. It's the role that interacts with the wider Bank governance teams.
It's the role that defines policy and makes sure that we implement those policies in the function. The second practice area is data quality and assurance. So this is how we measure, pro-actively, data quality issues. The governance people run a data dictionary, they engage with the data owner. They agree the threshold of acceptability of data. They agree the logic that's required for measuring quality. So it could be completeness. It might be validity, maybe accuracy, possibly consistency between different applications. And the data quality team will take that data from the data dictionary and now apply that logic across our dataset and and show back to the HR function the quality of data in which we operate.
The good thing about that is that we can then look at the processes that we run in the function and then we can get a sense of how much operational resource is required to run that service. So take payroll. for example, we can measure the accuracy rate of payroll and that might be when we move money into someone's bank account that you pay them accurately.
Well for most organisations we do that really well. So we've got 250,000 people in the bank. We pay a quarter of a million people every month or every two weeks in the US and we do it really well, but if you were to go back a few steps in the payroll process and we extract the payroll run early in the month that we pay 10 days later what work happens between the initial extract and the payroll run and that's where the operational effort comes.
So if the payroll team received data that's of a poor quality, they have to do a huge amount of work to go and fill in the gaps or to make sure that we don't do anything wrong. So the payroll accuracy works at the end. That's the job of the data quality team is to go and proactively look at that data. Give the heads up to the payroll function. For example to let them know that we've got some issues in certain areas. That team also does assurance and issue management. So when we do find problems then part of their role is to go and engage with countries or businesses or write to employees or to manage the IT function or the SAP configuration team to understand why there are issues being created and then make sure that we've got a program of activity around fixing it to improve the data.
And then the third practice is data architecture, so the architecture team are responsible for understanding the lineage of data, the flow, where we send it to, which other systems downstream in the bank outside of HR we send data to and then externally to the organisation where we might send data to. And then over the top of that sits privacy, which really sits in that first practice of governance, but privacy and sensitivity of data really flow through everything that we do.
So when we understand where we send data to and we understand obviously in Europe, GDPR, but in many other countries, we've got similar regulations around privacy and data protection. Then we need to understand who's got the data. What do they do with it? Where do they store it? How do they change it? How long do they keep it? And if we do have to comply to any regulations around the removal of data or purging of data, we know where the data is and how to do it.
So it's actually quite complicated in that CDO role. But the great thing about it is if we get the data in the right place, a great return on investment back to HR to help the function to run its processes more efficiently, but for my people analytics function, we get much better data to be able to use to do analytics activity. So it works out really well.
David Green: Which leads us onto your your other role as Group Head of People Analytics at the bank and obviously as you said you've been in that role now for three years. What are the key services that your team provides to the business?
Eden Britt: Well, luckily I have three practices in that area as well.
So, to simplify what we deliver we think about it as Global Services. So in the same way that we run the CDO function, the People Analytics function is split into three areas. And the reason I did that is that if we're not really clear on what service we offer and who gets to use that service.
It's very difficult offer any form of consistency. So I split into three different practice areas. Broadly, the majority of HR and managers and Business Leaders will use a reporting and BI service and so for HSBC I also own reporting and Business Intelligence. I have a leader who runs that, we have a Oracle OBIEE platform that we leverage for Enterprise reporting.
And that's built on that schema that we built from the SAP implementation where we rebuilt the data sets. But we leverage that for large scale near real-time information. And when I think of reporting I think about run, run the bank through data, operational data that helps you to make better decisions.
And so we've got 46,000 people who have access to that tool, every line manager in the bank, 38,000 of them, will get access to it and they get to then make decisions about their employees from the data that we show through that which is a really great way of putting data into the hands of the line managers, making them better managers and we've obviously got a lot of them. So it really helps us to make sure that the employees are having a great relationship with their manager, the managers understand who their employees are and that platform doesn't only count the number of people we've got it also tells them about notable events like birthdays coming up and new joiners so that the manager can get prepared for new joiners coming in.
So that platform services the HR Community, line manager community, CFOs, COOs with near real-time information and it refreshes daily from the SAP platform.
The second practice area is more of a workforce management advisory practice. So it's business facing analytics to the business lines. It's a HR global service analytics team. So who faces off to Recruitment and Succession Planning, Benefits, Reward Etc. It is the the team that looks after strategic workforce planning and operational workforce planning and they also look after organisational design and organszational effectiveness and those areas together really for me fit under workforce advisory, workforce management. It helps us to have a better conversation with the business than them asking for reports, and if you don't offer a service that helps the businesses understand how they're structured, where there are opportunities for more efficiency, then what we found in the past was we were asked lots of questions that were just reporting questions facing a different way.
And then the third practice area is data science. So I've just hired a new Head of Data Science who joined us in January, already some really great work that's coming out of that team and this is where I'm thinking about optimisation. I'm thinking about prediction, how we embed automation across HR, how we think about using people data in different ways, to help us to make the organisation more profitable, more productive, a more engaged workforce, so that team, whilst in its infancy now is an extension from a lot of advanced analytics work we've been doing over the last three years. So we're not new to this space. But what we've done is formalised that with a separate practice area, which now has to stand itself up in communication out to the organisation the HR ExCo and brand itself as a data science team.
David Green: Great, and I know you've got a reasonably large team across those three areas. How do you structure that team between centralised delivery and localised?
Eden Britt: That's a great question and I can't say I've necessarily got it right, but I tell you what we do today and probably the direction that we're heading. So we have a split of onshore and offshore resources. We've got Regional and Country teams that manage requirements for the regions and countries which are usually regulatory in nature and being a bank we have a huge amount of regulations, particularly in markets like Hong Kong, Singapore, UK, the US. So we have teams that are based in those locations. We have a group team that's based in the UK, which is my management team and also their delivery teams around global programs and global work that we do and then we have a shared service centre which sits in Bangalore which is part of our global analytics centre for the bank. So every business line has an analytics team that's in a shared service centre and we can leverage resource and thinking and best practice out of that. So that's how we are structured today. The question that I'm asking myself is how big does a team need to be to service an organisation of our size?
And that's where I think we have challenges, because I don't think there's any formula to define what that is. The question comes down to how much bespoke work will you deliver and how much automation will you deliver. And because we're on that journey, we're probably not at the end state yet. But I think in the future we'll have a combination of probably a shared service coverage that follows the sun a little bit more rather than relying solely on India, and that's because we have to work, outside of hours sometimes with the team in Bangalore and that doesn't necessarily mean we're covered in markets like Mexico and Latin America when we're outside of the India time zone.
It also means that we can probably leverage different skills in different locations when we think about either language skills or technology skills, where would we look at different service areas, but as we build out our BI platform and as we expand that to become mobile enabled and we expand it to be a tool that's more for the executive user versus for the volume user, as we build that practice out I think we probably need less people doing reporting activity, which means the volume of people reduced, but we probably need much more capable people to do better analytics and answer the why question and so that shift in capability and capacity I think will mean that we will shift the current coverage model.
David Green: I think you're right. I mean there's no magic formula for how many people you should have in your team depending on how big your organisation is, because it depends on so many different variables and I think there's a lot of comparing like for like and it's not it's like comparing apples with pears. Because some teams don't have responsibility for reporting, other teams aren't in the regulatory industry such as you, so I think you can be inspired by what others are doing but then you have to apply it within your own organisation of course.
Eden Britt: Yeah, and I think that's one of the things that I like about the Insight222 thing that we're obviously members of is that whilst other organisations won't have the exact answer. It's not just me sitting at my desk thinking of the solution. I can share it with people who have got similar problems and they can help me to think through the answer.
I've still got to make the ultimate decision. I still carry the ultimate risk. I still carry the budget requirements for that. But it does help me to bounce ideas off other organisations and there's a real synergy between organisations that are regulated like Pharmaceutical, like Telco, like Banking so it doesn't have to be another bank that I lean on I can lean on anyone who's working with analytics.
David Green: I think you're right. I think the great thing about our community and the People Analytics community is everyone's actually quite open to sharing and collaborating together. And long may it continue.
Eden Britt: Exactly.
David Green: I love the way you've structured the team and obviously built a lot of capability over the last three years. Can you give us an idea of some interesting projects that the team have delivered during that time?
Eden Britt: And we've done a lot of stuff that is both, what I would say is good ROI for the organisation. They would be around things like organisational effectiveness. So I think for anyone who's trying to get into more progressive analytics, it's not quite data science yet, but it's more than just a static report of number of people, counting transactions.
I think the org effectiveness piece is really interesting, so you can quite easily structure your organisation in layers or in different hierarchies to take a look at how the organisation is built and it's quite easy to see complexities within that. So some of the things that we look at are senior grades that are low down in the layer structure within the bank. We look at spans and control obviously to see where we may have layers that are not necessarily working the most efficiently. We've also done things from a network analysis or graphing of network analysis. So we take the organisation and it's quite difficult with an organisation our size to do this, but we put the organisation into a node structure where we using either the position hierarchy or the functional management hierarchy to set that node structure.
Once you've got it in a graph database, what it helps you do is to do all kinds of calculations and questions that you couldn't necessarily do in a relationship database. So when we layer onto a graph structure, if you imagine a node structure of nodes that split out with the most senior CEO at the top and the breakout of those nodes down through the organisation, then as we go down each layer and we layer on top through the use of colour or shape, we can start to see where the regional roles are, where certain activity is done. And so that really helps us to understand the structure of the organisation that helps the business understand particularly tough questions around, why are you built like that? Is it by design? Is it by osmosis? You know, it's just happened over time, that this thing has just grown that way and when you talk to the business about those questions, it's much easier to go with something that you found than a blank sheet of paper and ask an open question of what's your biggest challenge and let me help solve for it.
So I think those are areas that are quite interesting and I think we're getting really good feedback from the organisation on that.
Other areas that we've done recently. We did a great project on looking at Glassdoor information. So there's an API available for Glassdoor, which is essentially a URL where you append certain text to it.
And you send that out to the Glassdoor API and it will send you back some information on the external results for a five-point scale of how your CEO is doing, how your senior leadership's doing, the view on benefits within the organisation. So if you take that snapshot of your company, every three or six months, more or whatever you can start to see if there's an external change and then you can look to, whilst it's not in the most granular format, you can look at similar patterns to your internal surveys and then get a sense of the internal lens or the internal voice against the external voice. And so we did that but then we wrote a python script that helped us to use that API to go out and look at a hundred other organisations within our sector and outside of our sector so that we could pull this data back. You get it back, it's a REST API with a JSON structure that comes back, for anyone who's technical who understands that, but what it means is it's a structured format. We can then read into a data set that then we can look at and we can start to do some analysis, on so that was quite cool.
The other thing that I'm really excited about that we've been doing in the past few months is a lot of natural language processing on unstructured data sets where we've asked specific questions. They could be the engagement questions, but they could also be information around the pay and reward program that we've just been through for end of year or other pulse surveys that we run. So we do it anonymously, but what we look to do is cluster the certain phrases into two, three, and four word groups and then we look for nouns and then we look to try and cluster them into some form of view of what the pulse of what the people are saying and that's actually working out really well. So again a python project, leveraging open source, so it doesn't cost anything, leveraging packages that you can download as part of python that take advantage of some of the things that other people have done, particularly where you want to use packages that already recognise nouns and other words and joiner words and things that it might want to ignore. So that you're really focusing on the value of those, so I think those are some really exciting projects.
What I would say though is the challenge we're doing that work is that there is not a lot of people who can either do that work or if they can they don't necessarily want to work for a bank. Or if they do they want to be a Quant they don't want to work in HR so so I'm third down the list of trying to find people who want to work in a bank and want to work in the HR function. So I think we are, I wouldn't say we are necessarily struggling to find resources, but there is a lack of resource that comes with contextual understanding of HR, i'm not too concerned about that because you can teach it, but have the ability to technically code the solution. But also to look at the data and then package it up to be able to put into a research paper or to have a conversation with a stakeholder. And I think when you look at the skills that you need in this space to do that interesting work, a lot of people talk about storytelling. I don't think it's storytelling. I think it's about packaging. It's about understanding that the data scientist will produce the peer-review journal article, which is quite technical.
What you want to deliver that into the organisation is the New York Times journalist who turns it into the story. The packaging of that story with the nice infographic on the front that probably doesn't mean anything as a graphic to use as a replacement for a bar chart, but it's interested enough to make people want to read the research and then the most important part is the recommendation and then the implementation, or at least the: let's try that in this part of the world and not this part and we'll do some basic A/B testing.
So I think there's some challenges and trying to do this interesting stuff, but the NLP, the glassdoor stuff, I would say if you've got people who have basic python experience and can leverage APIs or can leverage some pre-built packages. Then it's a really good place to start.
David Green: And also one of the excuses that I get given from HR leaders why they can't do analytics is that their internal data isn't good enough.
Well with the Glassdoor example, you're using external data to actually do it.
Eden Britt: So that's a bit of a misconception I think. If you say your data is not good enough. You're probably still thinking about operational reporting. Yeah, I think I right and so data is always good enough to do something while perhaps not always but but most of the time you can you can look at the data you can do a very quick discovery.
You can eliminate the known outliers. You can eliminate known data quality issues. You can segment the data down to a dataset that is of good enough quality and then you can use it and for most of the projects that we do we substitute data anyway, so whilst it might be real data, if we want to do an anonymous data activity, if we're doing something around machine learning, what we might do is substitute or mask all the data anyway.
So, you know for me I don't think that's necessarily a big problem. If you're thinking about this as a data person not as an Excel person.
David Green: And I think a lot of those challenges come back from people who aren't actually doing the work.
Eden Britt: Yeah.
David Green: Because of a lack of undertanding.
Eden Britt: I would just say to anybody be brave, try and do something. You never know what you'll find. You can approach this activity in one of two ways, you can either have a hypothesis and try to prove or disprove it or you can have some data and go find something interesting. And tell people what you found, the aha moment that you found in the data. Either at the beginning of the journey is good enough.
David Green: More curiosity in HR.
Eden Britt: More curiosity, absolutely.
David Green: So what's next, what's on the road map for the next 12 to 18 months?
Eden Britt: So for us we are, the services that I talked about we're obviously going to get deeper into those services. So from a CDO perspective, the regulatory landscape is increasing we need to obviously continue with things like GDPR and when I think about GDPR whilst we've got a European regulation that means that we need to be better at data protection. For an organisation our size, well, why wouldn't we offer the same data protection to all employees? No matter what country you're in? So I think we've got a service that we need to offer as an organisation. And so we'll be continuing to look at the better ways to evolve that, get the data better.
The analytics space, the data science space or practice is really really interesting. We'll definitely do more in that. We are targeting to release a number of research articles this year, internal research articles. We're working with a couple of external folks. So we're doing some work with the IBM data science elite team. We are looking to do some work with the Alan Turing Institute out of the British Library, which is the think tank for artificial intelligence and machine learning for the government. So we're trying to do stuff in that space. We're trying to leverage other thought leadership to help us to think this through.
As we go on from there, I think that the obvious way that people analytics can help the organisation is to move beyond HR analytics. So I truly believe that people analytics is bigger than HR analytics. Getting external data, getting non-HR data to help us answer some of the bigger business problems and some the people constraints within those problems is something that we should look at.
But then how do we automate things for the function? So a great thing that I like to talk about is if you think about Netflix. So you think about the homepage of Netflix right? It's pretty simple. It's got some indication of things that you've watched before and things that you might want to watch and then Netflix have got north of a hundred and thirty million users and they've got north of a hundred and thirty million home pages, right because every home page that you land on is built specifically for the use of the fans.
So why wouldn't we as an HR function have an HR landing page that is specific to every employee in the organisation, what they like what people who look like them, they like. Where you are in your career? What do other people look at? Where people have progressed through the stage that you're at? What did they look at and what helped them to progress?
So I think we should be thinking about how we leverage the data to be able to make that employee journey a lot better. That's much more than the processes and services that we run in HR. So I think that's probably a bit further away than 18 months. But that's definitely the direction that we're going as we think these things through.
David Green: So actually if we look a little bit outside the bank now look at people analytics in general terms. Now what really excites you about people analytics and what it could potentially deliver in the future?
Eden Britt: Yeah. I think I think there's an obvious opportunity for us to think about skills for the future and the future of work.
Lots of organisations will need different types of people. Not necessarily less people but different types of people in the future and the question that we should be asking ourselves is what skills do we have today? What skills do we need for the future? Do any of our learning programs today help us to define those skills, to build those skills now? Do we even know what skills people have because a lot of the challenge with people, employees within an organisation is that unless you've got a robust internal talent profile then your view of the employee is the job that they do and your assumed skill set or competency set around that job. And one of the things that we found is that when we go out and ask for people with project management skills, we find people have got project management certifications in roles that don't even need project management. And so we should be able to offer that opportunity.
But for future skills particularly where we may automate what I'm seeing externally elsewhere is that organisations that are doing this well are really focusing on that gap. And understanding how they leverage their current platforms, their future budgeting cycles, their Strategic Workforce Plans to be able to ensure that they are heading down the path now to identify the skills that they need for the future.
David Green: Yeah, and actually it's funny because speaking to a lot of your colleagues in other big organisations. It seems to be a big focus area now ,this whole skills for the future is a big challenge for many big organisations around the world and there's no real HR technology that they tell me they've seen, nor I've seen, that answers that question and it's this whole thing around, do we build, do we buy it, do we borrow or do we bot? This whole question around that how we help our workforces acquire those new skills either through learning, by finding out things like their adeptness, I guess to learning new skills or their agility rather to learning new skills. So yeah, it's an interesting area.
Eden Britt: And there's two points to add then to that, if people are looking for skills and you don't have a competency framework or what I would want to work or skill and activity taxonomy, then O-NET which is an open source org psych database which has a huge amount of jobs in there.
If you spend the time to match your jobs to the O-NET database it comes with a whole bunch of job description skills. And so if you are looking to quickly get a sense of 80/20 view of what skills you need and what activities performed in roles then the O-NET database I think is a great way and it's open source and it's free. So we love free stuff.
The second point I think is on Automation and Bots and other things which is your fourth "B" in your example. When I think about chatbots and HR, I try and think about three different options. So we've got the chatbot, which is where most organisations are going now. It's an automation of that Tier 0/Tier 1 support level where you ask a question, the computer recognises the type of question and then it provides a pre canned response. That's normally from a knowledge database to the employee. And you've got the next stage of that which is more about this virtual advisor. So you might ask a question, how many days holiday have I got? And the computer will recognise it, it will go off and it will look at your amount of holiday and itI'll come back and then you might ask a follow-up question. Would you like to book some holiday? Yes, and then it will give you the URL to go to the holiday booking system.
So there's this continuity in the in the conversation. And then the third piece is around virtual assistants. So if you think about Siri and Alexa and these other things. That's really about having a conversation and that the evolution of that will be the follow-on.
So how much holiday do I have? You've got 28 days. Would you like to book some? Yes, would you like me to give you the weather forecast in the direct place you're going or how do I book travel? This is the link for the travel. When are you thinking about traveling? Would you also like to do XYZ and I think that's where the people analytics team can absolutely help the HR function whether it's with external vendors, whether it's with proof of concepts around this stuff to think about how we again might get back to Lake Point employee experience and help us to build things that help the employee have a better time within the organisation.
David Green: So we covered what excites you about people analytics and I agree. I think those are really exciting areas. Any concerns about the field and where it's headed.
Eden Britt: No such concerns other than ethics and sensitivity, right? I think the more that we leverage machine learning and I'm in this interesting situation with the chief data officer role. Where I really don't want to give access to anything of course and I'm managing sensitivity and governance and in the people analytics role, where the data science function, potentially would want to use everything. So I have this this role where I am trying to balance out both of those. So I think the questions that we need to ask ourselves are to do with ethics or where are we comfortable leveraging things like machine learning to help us make decisions. And a good example of that is if if you built a machine learning algorithm that helps you to choose a better hire most HR functions would be very open to using that. But if the same algorithm in a restructuring program suggested people that you should restructure that shouldn't be here anymore. Then most people wouldn't be comfortable with using that. Now essentially the question's the same, we're using data to make some decisions. And arguably we probably have more data on internal people than we do on the external people coming in. So we'll take a CV at face value. And we'll build an algorithm that will help us to make better hiring decisions.
But we potentially wouldn't want to do that ethically for other decision-making. Now, there is no computer says everything's right. There is always a human/computer aspect to this but both aspects have bias, inbuilt bias in machine learning and if you're not careful with inbuilt bias the model will continue to do more of the things that you probably don't want it to do. So, add in things that don't fit the model, hire people that it says don't hire if you really think you should and the model will change and adjust and then I think the bias in the human as we know is not only gender and other things that people think about but it's taking random data and making something of it.
There's a great thing I read the other day which plots the Dow Jones over time and the line graph against the the social media activity of Jennifer Lawrence and it has a 0.86 correlation. Now that's an interesting example, but many people do see a lot of meaning in very random data sets when we look at this stuff as we start to get access to more data and we do make decisions that potentially we should take a step back in, so that's the only thing that I think we need to be careful of.
David Green: Yeah, I think going back to one of your earlier points, it's all about the translation interpretation of some of these results I think and you do need that safety valve in there. I think we've both seen Cassie Kosokrov recently from Google talking and she said, it's not the machine, it's the decision maker giving the information to the machine. So that's what we need to be careful around.
So i've got a couple more questions. Firstly, our space is developing really really fast. How do you learn, other than conferences?
Eden Britt: I read a huge amount. I think I'm naturally I'm a learner.
So do podcasts, as in listen to podcasts. I read a huge amount of books and I attend conferences. I try to get around round tables and peer groups. So I'm constantly trying to get information in to help me think through some of the challenges that we've got and I do the same with my leadership team.
We have sessions where we discuss your blog for example, so we take a look at the most interesting things that people said in the last month and then we choose the ones that resonated with the leadership team and that we have some debate around what we think of it. So there's a lot of opportunity to get information in, one of the challenges is that a lot of the information tends to be quite similar.
So I think trying to find out where it is and that's where the curation that you do and others do really help because there is so much of that out there. But trying to balance a combination of things like people analytics articles with books. So a great book that I just read if people are interested was the Fifth Risk by Michael Lewis who wrote Moneyball and wrote the, I forget the book's name, the one about the crash with the subprime mortgage that was made into a move recently. But that book about the Fifth Risk is really interesting. So it starts out at the transition of Trump into power which is interesting but beyond that it talks about the department of Energy and the Department of Agriculture in the US and how they use satellite imagery to look at farming and how they use analytics to look at weather forecasting.
And so if anyone is inclined in analytics is to read books that make you think and investigate. Super Freakonomics, Freakonomics the Steve Dubner, Steve Levitt books are really good I think to help you get your brain going.
David Green: Yeah, I think that outside in thing is really important because if we just bury ourselves in HR stuff, we're not really going to develop as a function certainly not as people analysts.
Eden Britt: And just just one thing to add on that. So if you think about this. If you gave the people analytics function to The Economist team in my bank then, what would they do differently to what we do when we approach it from an HR perspective and I think we should always ask ourselves that if you do that outside in view, what would they try to look at?
I guarantee if you're looking at behavioural economics, it will be a very different way of looking at it than if you've come through the old reporting and analytics journey and you and done reporting and this has played a big part in the way you think. So, that outside view I think is really really important.
David Green: Which leads us on to the last question, which is a question we ask everyone on the Digital HR Leaders show. Where do you think HR will be in 2025?
Eden Britt: I think that HR as an organisation, and this is my opinion, is moving beyond the old target operating structure that we saw historically through lots of different models, but there are a few models that we do we know about that are a few years old now, I think what we've seen with HR is the shift into a new way of thinking part of that is about the journey of the candidate and the experience that's driving that, part of that is around the separation of the business partner from the consultant to the advisor and how do we structure that business partner activity? A lot of it will be around the adoption of cloud based technology, the adoption of mobile, the adoption of robotic process automation and not all HR functions will do this quickly, but I think the larger enterprises will. And there's a balance between efficiency, cost effectiveness and experience and I think as long as we lead with experience, then you'll get the efficiency and you'll get the cost savings over time.
There is an investment upfront obviously, but the engagement factor, the ability of a process to run better long-term I think is really where we're going. 2025 will we be at the Siri for HR or the Google goggles for HR? Probably not, but I do think we will see much more improvement in the recruitment process, in the assessment process, in the combination of the learning from that data into helping the career journey of the employee through the organisation.
David Green: Eden, thank you very much for being on the show.
Eden Britt: You're very welcome.
David Green: How can people follow you on social media?
Eden Britt: So they can follow me on LinkedIn. Sure. Eden Britt on LinkedIn. I have a Twitter account. They can follow me on that. And if need be just reach out so reach out on LinkedIn. I'm an open networker, ask me a message and I'll always try and respond.
David Green: Eden, thank you very much.
Eden Britt: You're welcome, thanks.