The role of data in employee engagement, retention and productivity

Kelsey Kline is Manager, Talent Research at Intel Corporation. She also serves on the board of directors of the Organization Development Network of Oregon.

Jathan Janove: Describe your work at Intel.

Kelsey Kline: I lead a team of researchers with deep expertise in social science research methods. We are responsible for research and measurement of employee engagement and sentiment at Intel. Our purpose is to help leaders and managers understand how best to support their employees in a way that enables mutual benefit. This includes developing surveys, focus groups, and other methodologies for gathering employee voice data and evaluating how best to use that input to facilitate impact.

Jathan Janove: What makes you passionate about it?

Kelsey Kline: The exciting thing about research and analytics is that they enable us to uncover insights that would otherwise remain hidden. We can use engagement survey results, for example, to identify more than just how employees feel. When wielded by a researcher with methodological rigor, we can start to pinpoint why they feel that way, the consequences of that feeling, AND what to do to best support employees who feel that way. I love the potential scope of impact offered by analytics – because we have the opportunity to use advanced data methods to identify intersection points for optimal benefit to the organization and also the majority of employees.

On a more personal level, I love putting the research “puzzle” together to see the big picture.

Jathan Janove: I’ll confess I’m a little left-brain challenged. So when you talk about data and what to me is right-brain stuff like employee engagement, I get a little lost. Can you help me make the connection?

Kelsey Kline: It is all about using measurement to better understand what’s happening and research methods to avoid common biases that can crop into even the most well-meaning needs assessment. Data analysis isn’t the solution, it is a way to uncover the optimal solution. So, I think of what you call the “left-brain” stuff as the science and the “right-brain ” stuff as the art that complement each other to enable great solutions. We can apply objective methods along with more subjective contextual assessments to make recommendations.

Jathan Janove: Can you share a specific example?

Kelsey Kline: What we learn from an engagement survey is largely quantitative. We generate average scores to help us understand what employees are feeling – such as understanding to what extent employees feel undervalued by the organization or concerned about future opportunities. We can then get even greater value out of that insight by adding a predictive analysis component – such as evaluating what types of responses tend to predict an employee leaving the organization and calculating turnover risk for the overall organization with that info. That is very valuable information, and without analytics, we would only be able to make assumptions rather than predictions. We then need to add the art component to reach meaningful recommendations on what to do with the insights . We need both researchers and big picture thinkers to tell the full story and make it actionable within the context of a given organization or situation.

Jathan Janove: Intel is an enormous international organization. Is there a place for organization development data in small organizations?

Kelsey Kline: Absolutely! At a company like Intel, we have a high volume of data and fantastic researchers and analysts who can drive innovative analytics. A small company may use different methods but will still benefit from research. The complexity of analysis is not what makes analytics useful. What’s more important is that there is meaningful application of the work. Sometimes the most useful analysis is still simple counts or average scores. You don’t need a lot of employees or advanced statistics to start telling a story with data. Small organizations may not have as many stories to tell, but it is still important to understand employee experience and sentiment.

Also, I would argue that in smaller organizations, it is particularly important to have strong research methods understanding if your organization does any surveys, focus groups, etc. With those measurement approaches, it is very easy to interpret the data incorrectly if you don’t have training in human and data biases.

Jathan Janove: For readers interested in learning more about using data to improve employee engagement, retention and productivity, what do you recommend?

Kelsey Kline: There are a lot of resources out there, from academic programs and certifications (I actually teach as part of one such program at Portland State University) to free tutorials on YouTube, EdX, etc. I recommend taking a social science measurement course or program as a starting point. My background is in psychology and that has been hugely helpful in creating a standard for understanding measurement of human behavior and common biases.

I also recommend practicing your analytic mindset. Always ask, what data could help make a better decision? Do I have access to that data? What would it take to generate the needed information? How can I interpret that data?