Leaders in People Analytics: Microsoft’s collaborative approach to research

Leaders in People Analytics: Microsoft’s collaborative approach to research
What works in research doesn’t often transfer in practice. Learn what Dawn Klinghoffer, Microsoft’s HR Business Insights lead, has to say about navigating the research-practice divide and bringing analytics to the business.

Dawn Klinghoffer leads the HR Business Insights team at Microsoft. Her team tackles everything from measuring the effectiveness of Microsoft’s HR tools and programs to running in-house experiments for talent management and learning & development. Klinghoffer has worked in a variety of roles throughout Microsoft HR and finance for five years and in the people analytics field for fifteen years. She likes to stay on top of the latest social science news by reading People Analytics topics on LinkedIn posted by some of her favorite influencers such as Adam Grant, Josh Bersin and David Green.

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

KLINGHOFFER: At Microsoft, we’ve used people analytics to advance a variety of our employee career development programs — it’s hard for us to imagine how we could have made progress without it. Early on, we saw just how impactful our work could be to our business after seeing a group of employees point to our analyses at a company-wide hackathon and transform our policies on internal mobility as a result. Since then, our team has provided data to answer some of our business’s most pressing questions, such as: How can we support the career development of our employees at each level? What aspects of onboarding create an exceptional new hire experience? How can we measure the progress we are making and inform actions with our culture change journey?

One of the many reasons I love people analytics is that there is always more to learn. Many times the scope of your analyses is determined by how critical the problem at hand is and whether you have all the information you need to make your next decision. While we make progress on this topic each time we dig into the data, there are always new insights to glean as we figure out more innovative ways to collect and analyze employee data.

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

KLINGHOFFER: Maslow’s hierarchy of needs. It seems to be the one motivational theory — that people need to fulfill a number of needs before they can realize their full potential — that just about everyone in the corporate world can remember learning about, and it continues to resonate with many people. Regardless of how familiar you are with the theory, many organizations can agree that understanding your employees’ needs matters and makes a difference to your culture. While aspects of Maslow’s theory have been criticized — e.g., Does it really need to be a hierarchy? — the theory provides a relatively simple approach to an ambitious goal. For organizations that strive to create an environment where employees can reach their full potential — or a stage of self-actualization — Maslow’s hierarchy provides a familiar starting point and a straightforward framework to fuel discussions and help prioritize actions.

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

KLINGHOFFER: I recently read an article in the New York Times focused on how to improve reading comprehension. One of the author’s key points was that improving reading comprehension does not depend on vocabulary alone, but that your knowledge around the topic can make a difference too. The article describes a study where third graders with varying levels of reading ability were asked to read a passage about soccer. They found that students who played soccer were three times as likely to respond accurately to questions about the soccer passage than students with a higher reading ability who didn’t play soccer. This simple finding emphasizes the important role that pre-existing knowledge or context can play in someone’s understanding and interpretation of data.

As a centralized people analytics team working on a range of business challenges and with a variety of clients, we don’t always have the entire context available to understand the problem we are trying to solve. Therefore, it’s important for our team of analysts, data scientists, and researchers to lean heavily on strong partnerships with our clients to fully understand our business challenges and opportunities.

But it works both ways. It’s also just as much our responsibility to share our team’s knowledge, leverage our skills, and help our customers and partners grow their own analytical and decision-making capabilities. We recently launched two different training classes with these goals — Generating Data Insights and Applying Data Insights — that have received positive feedback.

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

KLINGHOFFER: One of the challenges we’ve faced is balancing the rigor of our research with our business timelines. Conducting experiments to determine causal validity (i.e., a clear “A leads to B” connection) takes time and resources (larger n-sizes, controlled settings, ability to randomly assign participants) that organizations do not always have in the same way that research facilities might. Given these limitations, businesses often choose to explore correlational research or conduct quasi-experiments to test their hypotheses.

When it comes to practicing research in an organizational setting, another good strategy is to emphasize to business partners the importance of taking an iterative approach when it comes to problem solving. It’s rare that we are ever “done” solving a problem — analyses can bring up new research questions, variables, and follow-ups that are important to understand. The work of people analytics is on-going, and requires us to build long-term relationships with our business partners. Just as they are constantly building and selling products, we should always be supporting and developing their people. Because nearly every business challenge contains a people component.

It’s important to be transparent with your partners about what your research process might look like (with any anticipated setbacks), and to build in any cushion time you might need. Our team works closely with our customers and partners to create expectations and timelines that don’t sacrifice the quality of our analytics.

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.