Here is an excerpt from an article written by Chantrelle Nielsen and Natalie McCullough for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.
* * *
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 characteristic 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, people analytics data was used 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 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.
* * *
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.
* * *
Here is a direct link to the complete article.