February breakfast panel discussion - Building the business case for data driven HR

March 12 2021

 

Building the business case for data driven HR 

How can HR build the business case for investment in tech? At this time of transformation, HR has the opportunity to demonstrate its digital capability. But how can you effectively make the case to the CFO when there are so many business priorities? Here’s what our February breakfast panellist had to say.   

 

OUR FEBRUARY PANEL  

Damien Shevlin, SVP Human Resources UK&I and Northern Europe at Atos 

Jamie Nevshehir, Director, HR Operations and People Analytics at NBCUniversal Media, LLC 

Mattijs Mol, Global Head of HR Impact & Insights, Wärtsilä 

Hugo Tucker, MD & Founder at Tucker Stone - HR & Change Search 

 

Investment in HR tech has been piecemeal  

Since 2019, there has been an exponential increase in the demand for HR analytics skills. Using our platform, Stratigens, to analyse this skill, we can see a 33% increase in these roles in the UK. Hotspots for these skills are major city centres and are currently grouped in tech, financial services and consultancy businesses. This surge in demand for analytics skills is an indicator of broader changes within HR 

Historically, HR received limited investment in systems and technology meaning data was often poor, and as a result, the business paid little attention to it. But things are starting to change, says Hugo 

The bomb shell is when the HR leader can go to an operations leader or CEO and tell them something about their people that they don’t know – to prevent something happening or to put in place an initiative with a commercial impact. The data question is always asked now when recruiting for a Chief People Officer.” 

At NBC Universal, the journey to being data-driven began two years ago. At the time, there was no function to proactively bring HR data to the business. Though a high impact example, Jamie won buy-in from the CHRO and this was the point at which HR operations began its growth into HR analytics.   

At Atos, Damien decided to bring data scientists into the team three years ago. Seeing data science as a key skill for the future, Damien ‘self-funded the addition of analytics skills through attrition; by bringing on people with analytics skills as team members left. Three years late, data analytics has provided new insights and has reduced the overall workload of the team which has furthered the business case. One of the issues Damien faced was that stakeholders did not trust HR data. The team has managed to flip that and feedback about the data is positive and that allows senior leaders to make decisions. 

Mattijs added that data is important to improve decision making capability. Data-driven decision making is key because we don’t want to cannibalise the knowledge of the HR team, but we can’t deny that data will support decision making and enable us to see whether we are making progress.  

 

Looking at an old problem with a fresh set of eyes 

Have you seen real examples of where data has brought a fresh set of eyes? Yes, says Damien, where at Atos, analytics have been used to bust the myths that inevitably exist in a large organisation. One instance was around mental health in which the team used analytics to drill down into what was causing mental health-related absence. 

Using analytics, the team analysed where poor mental health affected the business by area, geography, line manager, team, gender, seniority and ethnicity. This meant the team could localise the resulting approach to improving mental health. The outcomes from this project reduced mental health issues by 20% which therefore helped to reduce sickness absence.  

This project was incredibly successful and busted myths about why people take time off. Damien says this was an eye opener for the board and something that they have since used to have powerful conversations with customers.  

Mattijs added that he has also used data to bust myths around HR including looking into beliefs about whether people are poached or moved on from teams into others and whether some managers are allowed to make experienced hires while others are not. Using data, it’s easy to look into the evidence to prove opinions one way or the other, says Mattijs who has used process mining to apply data analytics in a more scalable way in relation to career paths:   

“How do people flow through the organisation by unit, geography, progression, what is the entry point, exit point? Which team is the talent hub? Which is the graveyard where you arrive there and never leave! We used to base career paths on textbook journeys but what happens in reality? Do you enter as graduate engineer and move up to engineer two then engineer three? Or do people have different shaped experiences?  

At NBC, Jamie and his team used data from talent reviews to surface trends. They looked at churn, where people are coming from and going to across the business. They then took stories from within each review that illustrated a lack of movement between functions. This raised the case for better internal mobility and was the test-case for winning buy-in to data-driven HR. 

 

What methods and tools are companies using? 

At NBC, Jamie says they looked at a number of platforms. The foundation was needing a way to surface data that was not clunky. They considered business intelligence tools like Tableau, Domo and PowerBi as well as packaged offerings like Visio 

NBC selected PowerBi which integrates with O365 as this was the direction of the IT strategy and therefore supported the business case. But, he says, in terms of demonstrating the business case and securing investment, this was won this through Excel and PowerPoint: “Get that small piece of work that gets you noticed.  

How can you persuade the business to become dependent on insights from data? It takes time says Mattijs and proximity is key; understanding what the business challenges are. By combining aspects of labour – strategy, business, roadmap, you are able to identify and help mitigate their risks. His advice is to reach out and do some quantitative and qualitative research; what do your stakeholders want? Remember that you don’t always need the most advanced tools to solve their problems 

 

How do we change the mindset of HR?  

The modern HR department is not data shy, says Hugo, but a stronger link between HR data and customers is needed. If people are your products, then you need the right skills at the right time and that affects the bottom line. Attitudes are changing and many highly visible elements of HR such as diversity and inclusion are dependent on data.  

The presentation of the data is important, says Damien. You can have loads of data, but internal customers have to understand it. At Atos, the team has moved from talking %s to talking £ to change mindsets. In one example the team modelled travel by salespeople in relation to travel costs and wellbeing – this combined approach is what gets noticed.  

Mattijs says changing mindset is about impact on bottom line: “Do we have the right skills and capability? For three, five and 10 years from now? Will we be able to meet our customer needs? What if we don’t need the skill x in five years in the way we need it today. Do we wait five years? Or can we start our change of capability and train people for the skills of the future now? 

Also, think about the capability of your HR population says Mattijs: “Train them but don’t scare them off. They don’t need to be data scientists, just to understand it.  

Jamie’s team is already looking at simple metrics, but the end goal is to move to scorecards. For example, in one exercise the team looked at retention and regrettable loss. This was modelled alongside compensation and showed figures in the millions. The team could then ask: “How can we reduce the loss of people leaving by 1% This has directed NBC’s talent development plans. These exercises are quick and easy. You don’t need to go deep into data science, just be able to quickly surface data says Jamie. 

 

Battlefield, tactical and strategic data  

At what level is data being used? At Wärtsilä, Mattijs says that the team is on one hand finalising the architecture and, in the meantime, creating showcase examples so people can see what the team’s ambition is. We need the business to immediately see value - and this is possible because at the moment the bar is not very high. For example, location planning used to be cost of real estate but there is also a significant people elementcompetition, access to talent, languages, skills. When the business sees we can focus on these elements they love it”, says Mattijs 

At NBC, the team is building a foundation, says Jamie. This is being achieved though building data lakes which Jamie believes is key to providing business intelligence more quickly. The team is also looking to provide data more strategically by, for example, combining exit data with employee listening data and HR data to become more predictive and look at what’s trending. 

At Atos, Damien says his team is using SAP and Python for more advanced analytics that allows them to be predictive - but that they are not there yet and are still dabbling in predictive analytics. For example, Damien’s team has modelled how long it will take for the business to close the gap – and busted a huge number of myths along the way! 

 

This was another fascinating discussion – thank you to our panellists, to those who submitted questions for our panellists and of course, to everyone who dialled in on the day and contributed to the online discussion. 

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