Leaders in People Analytics: Merck & Co. on the research-practice divide

Leaders in People Analytics: Merck & Co. on the research-practice divide
Sometimes it’s less about what data you have and more about how you communicate it to the rest of your business. Learn how Geetanjali Gamel, Director of Workforce Analytics and Planning at Merck & Co., Inc. and her team use research to influence decision making.

Geetanjali Gamel leads Workforce Analytics and Planning at Merck & Co., Inc. Gamel’s team enables better business decisions through data-informed insights, with a portfolio of analytics products and services ranging from rich descriptive to complex predictive. She’s been with Merck for 1.5 years and in the analytics field for 11 years across diverse sectors including energy and financial services. She likes to stay on top of the latest social science news by reading blogs and articles on forums like Harvard Business Review, The Economist, MIT Sloan Management Review, Singularity Hub, and re:Work.

RE:WORK EDITORS: What is an example of a business challenge you think is worth solving by using people analytics?

GAMEL: Employee engagement is a critical area for the sustained health and success of an organization. Yet, many struggle to derive meaningful, actionable insights for leaders from just employee engagement surveys and scores. At Merck, we faced similar challenges and wanted to better understand the following questions: how were our employees adopting our strategic cultural pillars? how energized were various employee groups feeling? how could we measure the level of innovation and collaboration of our employees? how could leaders drive better business decisions in the company?

We wanted to use our workforce analytics to make our engagement survey results more meaningful for our leaders. So we shifted the focus on engagement from measurement-based to learning-and-impact based by looking into employee clusters of highly innovative and collaborative behaviors and the positive business outcomes tied to them. To take a deeper look at the opportunities and challenges highlighted by the initial analyses of the survey results, we supplemented our survey with focus group sessions. We then used different content and sentiment analysis techniques to extract important insights and create effective, consumable, and actionable insights for our leaders.

What's an insight from social science that you've found to be especially influential in the workplace?

GAMEL: Although we recognize the importance of data in our work, we don’t always trust it. In organizations, there is sometimes a tendency to rely heavily on human judgment and experience when making people decisions — especially when under a time crunch. Even as a pharmaceutical company with a large workforce of scientists who follow evidence, we’re not completely exempt from the under-reliance of data at times.

Literature on algorithm aversion has found that people tend to ignore the probabilistic nature of the world and often reject imperfect, albeit superior, algorithms in favor of less accurate human judgement. Even when we have the data, there are times where we go with our gut — with what we think is right, or with what we want to see.

Researchers found that one way to prevent algorithm aversion and increase participants’ likelihood to trust and use data is by providing individuals with control to influence, even slightly, the process. Analytics can seem like a “black box” to other parts of the business because often people are excluded from engaging in these types of conversations until the very end. At Merck, we recognize how much of a difference context setting can have on how engaged and connected our partners and clients feel with our work. That’s why we seek out our stakeholders’ feedback early on and incorporate it into our research model. We want to build the analytical involvement and confidence of our partners and clients so that they feel a part of the decision-making process every step of the way — from the initial brainstorming or modeling to the final delivery of the project.

For example, in one project, we were asked to help an internal client understand the key drivers of employee turnover in their business unit. Throughout the model development process we proactively obtained their views and explained our approach at each major milestone. The cultural context we received from them around the particular region, its labor flows and work-life expectations, not only helped supplement the modeled outcomes, but also bolstered the confidence of our clients in the study.

What's a particularly useful finding you read about recently? How has that informed your work?

GAMEL: Context setting is not only relevant to stakeholders working with you, but it is especially important to your employees, your end users who are ultimately impacted by your findings. In an organization, it doesn’t always make sense for everyone to know the details of all parts of the business. It is important, however, that people have insight into the decision-making processes that impact them. What you choose to share and not share with your employees can ultimately influence the way they respond to your programs. In a study, researchers found that people were more likely to let someone cut them in line when someone provided a rationale (no matter how unsatisfying the excuse) compared to when they did not. This finding suggests that a bit of context can really go a long way, even when it seems like there is minimal choice given in a decision.

People’s desire to understand the logic behind an action can be leveraged in a very positive manner in the workplace, particularly when soliciting employee feedback. When we collect feedback from our employees, we believe it’s important that we first be transparent with them about why we’re collecting the feedback and what we plan to do with it. We want employees to have the full context so that they can truly feel motivated to engage and share their perspectives with us.

If we truly want to make analytics seem less like a “black box”, we need to make sure our workforce feels that their feedback is part of the business’s decision-making process from the start. Their feedback itself is an incredibly important data set that can start and solve many research queries.

What is the hardest part about straddling the research-practice divide? What strategies have you and your team used to overcome it?

GAMEL: I think we have to continually strike a balance in the workplace between what research says and what your business needs. It’s important to understand what gaps exist, from the philosophical to the tactical.

In research, factors like time and resources are more easily controlled (though perhaps scarcer) compared to a business whose priorities are constantly evolving. In academia, the pool of participants to select from can be much wider compared to that of a workforce population. While small n-sizes can prevent the execution of certain academic research projects, organizational researchers must be resourceful with how they leverage sound analytics for projects that are of high business priority, regardless of these types of limitations. We also see other examples where what research recommends might not be a good fit in practice — e.g. research encourages you to reward failure to promote innovation, but this behavior might be difficult to incentivize in certain corporate environments.

And while there’s no denying this gap between research and practice exists, there are opportunities where we can leverage both. At Merck Workforce Analytics, one way we’ve approached this challenge has been by creating a role on our team with specifically dedicated time to building academic partnerships and exploring external research that can inform and improve our work. Additionally, we’ve launched an external speaker series where our team gets to hear directly from professionals in academia about important areas of organizational research.

Merck & Co., Inc. (NYSE: MRK) is known as MSD outside the United States and Canada.

In this series, we interview leaders in People Analytics from across different industries to learn more about how organizations have applied research and data to their work and people processes.