Using deep research and predictive analytics to drive culture decisions
Apr 26, 2022
Written by: Brad Shuck, Ph. D., Professor of Human Resources and Organizational Development, University of Louisville, Co-Founder, OrgVitals; and Amy Stern, Managing Director, Research and Strategy, BI WORLDWIDE
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Big data is important but to make it more meaningful, we need to add a human element that contextualizes it. Here are a few tips for getting started with deep research and predictive analytics.
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Culture is ready for its Moneyball moment.
In the same way baseball General Manager Billy Beane focused on nuanced and individualized stats to drive performance holistically, company leaders have an opportunity to use deeper analytics to shape the entire employee experience. This starts with asking:
• How are we using data in new ways?
• How are we leveraging data to help make decisions in the moment?
• What data can we use to help forecast what things may look like in the future?
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It’s also rooted in the idea that one single metric doesn’t tell the whole story. You can measure something like engagement but engagement doesn’t exist on its own – it coexists with other aspects of culture like stress, inclusion and alignment to a company’s broader mission. Measuring something like engagement will tell you how engaged employees are in their work but it won’t tell you if those same employees are experiencing burnout or a lack of support or disconnection from their team. By leveraging a variety of metrics at the individual employee level, we are better able to drive team performance.
A great example of this is wearable technology devices that track things like sleep patterns, physical activity, stress levels and more. By tracking data at the individual level and integrating it into traditional data models, it can help the employee (and their manager) make sense of what their day-to-day experience is like. This is also where forecasting and predictive analytics come into play, helping us understand the most important moments throughout the day, week, month or year and provide nudges that are deeply personal and meaningful to the day-to-day experience of the individual employee and their leader.
Oftentimes data like this has been centralized, stored in a dashboard or aggregate report as big data. The next evolution of this is to use data at the individual level, empowering employees and managers alike to take action and impact the everyday work experience. Thinking of this in terms of the workplace, there are three types of data we believe will be integrated to create a more holistic picture of culture:
1. Behavioral data: What am I doing?
i.e., Steps I’ve taken in a day, how many recognition messages I’ve sent, the learning I’ve completed, etc.
2. Attitudinal data: How am I feeling?
i.e., Burnout, commitment to staying with the organization, belonging, etc.
3. Qualitative data: What does that really mean?
i.e., If I feel like I belong, what contributed to me feeling that way?
We’ve talked about big data for a number of years but a lot of times all of that quantitative data is only telling the behavioral side of the story. To really get the most use out of it, we need to combine it with other types of data and turn the data into action by:
• Simplifying it.
It’s easy for complex data to become confusing. The key to using complex data in a meaningful way is to turn it into simple, actionable findings that everyone can understand.
• Making it easy to interpret.
Don’t leave people guessing about what actions to take. Structure findings in a way that directly connect to what needs to be done to address them.
• Creating individualized nudges.
How the data is interpreted and what needs to be done with it will differ for every employee and manager. It’s important to make sure data is shared in a way that’s personally relevant to each individual.
Big data is important but to make it more meaningful, we need to add a human element that contextualizes it. Without qualitative conversation around the data, we only know what’s important and where to focus at a high level – we don’t know what to do specifically. There is often a real bias towards the “what” that quantitative data tells us. But we need to be curious about finding the “why”, or the story that’s told in qualitative data. For example, if I work at an organization and I see belonging is important, I will understand what it is but I won’t know why or have other people’s experiences to help explain it.
Getting started with deep research and predictive analytics can feel overwhelming. But here are three things you can do today to take action:
1. Use data as a conversation starter.
2. Ask questions about trends you’re seeing in your organization.
3. If you aren’t collecting data yet, connect with someone who is an expert in that area to figure out what culture data you should begin collecting.
The best way to get started is to get in touch.
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