Merck KGaA’s Journey to Deliver Impact Through Analytics and AI at Scale
How do you take People and AI analytics to the next level? A Merck expert explains how his people analytics team has to act like a business.
As part of our People Powered series, Merck KGaA Group’s Alexis Saussinan explains how the company made the move from people analytics pilots to a more mature set up including AI analytics which delivers real impact across the business.
Merck KGaA is a German multinational science and technology company headquartered in Darmstadt, Germany, with around 57,000 employees and a presence in 66 countries.
We have a traditional HR set-up, with business partners on one side, and the centre of expertise on the other., We also offer HR delivery services. Our people data unit—digital HR and data team—sits at the centre and collaborates across the business. My team operates very closely with the broader ecosystem of the business analytics team and other data offices. Our various innovation centres spread across the world are forward-thinking start-ups collaborating to work on new topics.
A people analytics function must act like a business
In two or three years we’ve learnt that if you want to move away from small pilots to delivering people and AI analytics and bring change and improvements across the organisation, you have to organise yourself and behave like a business.
We are sitting on robust assets, and have one single global people analytics (PA) platform, Visier, which gives us live insights on the entire group. Right now, we have 500 million data points and counting. This number grows every day. We’re constantly looking at making the most of this huge asset—and all of the data we have at our disposal. A critical part of our journey so far has been the buy-in from leadership. This buy-in means having a sponsor who has seen the impact of our work and recommends us to other senior colleagues.
Data governance and R&D
Our professional people analytics approach pays attention to data governance. We introduced a robust HR data governance pillar across the entire group—governance, quality, privacy and ethics—proactively managing our core asset and reviewing the infrastructure we are using.
In addition, we have a research and development (R&D)team which manages portfolios and pilots at different points along the cycle. This R&D team tells us which use cases will deliver the biggest impact. Not all pilots make it to the finish line. Those that do could be anything from skills matching to flight risk identification.
What we apply to marketing and sales we call digital HR UX and enablement. This ensures any use case and product that has been designed and developed hits the ground running with the most impact and highest levels of adoption. The team decides how to best segment the end users, chooses the best channels to reach them, and designs multichannel adoption strategies—the right mix of in-person or online, workshops, for example. This way, we can be confident that the target audience are the ones using the right products, whether that’s for training, employee engagement, or any other people-focused application. If we have great use cases, we want them to be used.
Show the value of people analytics
We are now in a position to deliver AI analytics across the employee life cycle through all touchpoints. Our digital team has grown over the years, from two to 23. We’re shooting for scale and moving away from pilots and small areas of applications, looking for the specific use cases that deliver impact for the entire company. But how did we get here?
Our approach from the start was to show the value. We immediately identified a few senior leaders who had an appetite for PA and use cases. We worked with them to show parts of the wider organisation how PA and AI analytics made a tangible impact on the business. For example, we showed that certain profiles in sales roles increase revenue, and that diversity increases innovation.
Showing how PA can help solve business problems led to word-of-mouth publicity for the work we’d been doing. Our work was getting traction.
We didn’t want to jump from one use case to another. We always wanted to shoot for scale, so when one use case was generating impact, we wanted to see if that value could be reproduced, and the value of the asset maximised. I’m not talking about absolute standardization, but how an asset can be repurposed to meet different needs at scale.
AI analytics predict and mitigate flight risk
One part of our business was experiencing alarming high levels of churn—we were losing people too early in the game. We worked with the business to look at factors that were contributing to people from certain segments leaving. We developed a flight risk algorithm and iterated over time as we looked at the data flowing back to us. Now, the algorithm is being used across the business in multiple functions where there is a focus on talent acquisition or retention.
It’s important to say that there is no one-size-fits-all algorithm that can solve a retention problem due to the varying number of regions, teams and departments. But what it can do is provide a solid foundation which can be tweaked and taken to market fast. This is a key part of its success.
Of course, not everyone trusts AI. It’s therefore important to layer it with the personal experience of leaders who know the people in question. Sometimes they were in line with the AI prediction, sometimes not. But it always sparked important conversations and incited new actions.
Using AI to predict the jobs of the future
Strategic workforce planning needs an understanding of how skills and jobs will evolve over time, which includes looking four to five years ahead.
We leverage information from the world, crawling for information, looking at external sources that show us how particular business skills and jobs are evolving, and also the likely levels of demand for those skills. Today, we try to reference these future trends in every business discussion. Strategic workforce planning is part of people and business planning’s remit. There is no business plan without a people plan. How this is evolving is a key part of the discussion.
A big ‘a-ha moment’ for us was when we realised that AI and analytics projections about skills and jobs were triggering strategic thinking in business planning about how we – the company as a whole – wanted to do business. That is, how the demand curve for new jobs and skills influenced discussions about our future business model. For example, the focus on customer centricity and digitisation is seen in the demand side of the employment market today and will feature in the business plans of companies tomorrow, if it doesn’t already.
Final thoughts: Learnings from the journey so far
Firstly, get your foundations right. If you want to answer business needs, you have to have a minimum infrastructure in place. It’s not going to get you far without that. If it takes you three weeks to gather the data, you’re very likely doomed.
Shooting for scale means making sure that wherever there is a need, you can get there and get there fast. This means mapping out the internal market and seeing the most pressing business demands. It also means deciding where we can practically deploy our assets, or redeploy others to help.
Without sponsorship from senior leaders to represent you, there won’t be any business impact. Business leaders and end-users are the ones making the most out of AI use cases every day, so it’s critical to spend time getting senior sponsorship and listening to feedback.
Prioritise! You can’t do everything. It’s all about being clear about the return on investment you’re generating. Sometimes it’s hard to do this in testing; but that’s fine. If you really want to maximise impact at scale, you need to be clear on return. So be prepared to make tough trade-off decisions.
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