How people analytics can help you change process, culture, and strategy
It seems like every business is struggling with the concept of transformation. Large incumbents are trying to keep pace with digital upstarts, and even digital native companies born as disruptors know that they need to transform. Take Uber: at only eight years old, it’s already upended the business model of taxis. Now it’s trying to move from a software platform to a robotics lab to build self-driving cars.
And while the number of initiatives that fall under the umbrella of “transformation” is so broad that it can seem meaningless, this breadth is actually one of the defining characteristics that differentiates transformation from ordinary change. A transformation is a whole portfolio of change initiatives that together form an integrated program.
And so a transformation is a system of systems, all made up of the most complex system of all—people. For this reason, organizational transformation is uniquely suited to the analysis, prediction, and experimental research approach of the people analytics field.
People analytics—defined as the use of data about human behavior, relationships and traits to make business decisions—helps to replace decision-making based on anecdotal experience, hierarchy, and risk avoidance with higher-quality decisions based on data analysis, prediction, and experimental research. In working with several dozen Fortune 500 companies with Microsoft’s Workplace Analytics division, we’ve observed companies using people analytics in three main ways to help understand and drive their transformation efforts.
In core functional or process transformation initiatives—which are often driven by digitization—we’ve seen examples of people analytics being used to measure activities and find embedded expertise. In one example, a people analytics team at a global CPG company was enlisted to help optimize a financial process that took place monthly in every country subsidiary around the world. The diversity of local accounting rules precluded perfect standardization, and the geographic dispersion of the teams made it hard for the transformation group to gather information the way they normally would—in conversation.
So instead of starting with discovery conversations, the team utilized people analytics data to baseline the time spent on the process in every country, and to map the networks of the people involved. They discovered that one country was 16 percent more efficient than the average of the rest of the countries: they got the same results in 71 fewer person-hours per month and with 40 fewer people involved each month. The people analytics team was surprised, as was the finance team in that country, which had no reason to benchmark themselves against other countries and had no idea that they were such a bright spot. The transformation office approached the country finance leaders with their findings and made them partners in process improvement for the rest of the subsidiaries.
It’s unlikely the CPG company would have been able to recognize and replicate these bright spots if they had undertaken transformation with a top-down approach. And, perhaps more importantly, it involved and engaged the people on the ground who had unwittingly discovered a better way of doing things.
In bottoms-up cultural transformation initiatives, how things are done is equally as or more important than what is done. Feedback loops and other methods of data-driven storytelling are our favorite way that people analytics makes culture transformation happen. Often, facts can change the conversation from tired head-nodding to curiosity. One people analytics team in an engineering company was struggling to help develop the company’s managers, for example. Managers often perpetuated a “sink or swim” culture that didn’t fit the company’s aspirations to be an inclusive, humane workplace. The data analysis found that teams whose managers spent at least 16 minutes of one-on-one time with each direct per week had 30 percent more engaged direct reports than the average manager, who spent just 9 minutes per week with directs. When they brought that data-driven story to the front lines, suddenly a platitude was transformed into a useful benchmark that got the attention of managers. In this way, data storytelling is a lightweight tool to build trust among stakeholders and bring behavioral science to culture transformation.
Top-down strategic transformation is often made necessary by market and technology factors outside the company, but here people analytics is a critical factor for execution. A people analytics team can serve as an instrument panel of sorts to track resources, boundaries, capacity, time use, networks, skill sets, performance, and mindsets that can help pinpoint where change is possible and can measure what happens when you try it.
One people analytics team at a financial services company was trying to help the CEO manage growth while he worked to instill a new culture in which departments would be asked to run leaner and more competitive in the market—“scrappy” and “hungry” were terms that often came up. As the transformation accelerated, teams were asked to do more with less, generate more data, and make decisions faster. Amid this, department leaders began to hear anecdotes about burnout and change fatigue and questioned whether the pace was sustainable. To address this, the people analytics team provided their CEO with a dashboard showing the number of hours that knowledge workers were active for in different teams. When an entire team is over-utilized, he knows they can’t handle more change, while under- or unevenly utilized teams might be more receptive. He can also slice the dashboard by tenure, to learn whether recent hires have been effectively onboarded before approving new hire requests to absorb extra work.
As organizations increasingly look to data to help them in their transformation efforts, it’s important to remember that this doesn’t just mean having more data or better charts. It’s about mastering the organizational muscle of using data to make better decisions; to hypothesize, experiment, measure, and adapt. It’s not easy. But through careful collection and analysis of the right data, a major transformation can be a little less daunting—and hopefully a little more successful.
This article originally appeared in Harvard Business Review.