Companies have known for years that people data is one of their most powerful tools when it comes to increasing productivity and closing more business. HR and Talent leaders are often encouraged to put data to work to make sure they’re hiring, retaining, and enabling the best and brightest talent. Sales leaders attempt to crack the people data code to improve performance and close more deals. But what sounds like a straightforward assignment quickly becomes a quagmire when teams try to collect, analyze, and draw conclusions from disparate, siloed and incomplete data sources. For many companies, what starts as an effort to optimize operations quickly becomes a challenge to use any data at all.
If this dead-end data wrangling scenario sounds familiar, you’re in good company. While 83 percent of HR leaders agree that all people decisions should be based on data and analytics, just 37 percent use data to solve people management problems, according to a report from Sage People. With so many organizations struggling to put their data to use, it’s no wonder that a majority of organizations report feeling unprepared to use people analytics to improve processes and drive growth.
After years of working with teams within small companies and global organizations, I’ve seen dozens of people data projects start and fail due to mismatched expectations, ineffective data silos, and unrealistic project scoping. The few projects that succeeded each kept these 3 objectives in mind when putting people data to work:
Start with a simple question, and work to capture data that provides insight.
“Anytime we analyze data, it helps to start with the basics.” writes Peter Capelli, Professor of Management at the Wharton School at the University of Pennsylvania. Starting with the basics can be hard when we have complex questions, like “How do we solve turnover issues?” or “How can we improve our training programs?”. Instead, focusing on a smaller part of a larger issue can shrink the amount of data you’ll need and shorten the analysis time it will take to draw conclusions. Questions like “What do our top performers have in common?” or “What are the strengths of our longest-tenured employees?” allow you to look at a few data points within a subset of your audience. From there, you can use insights to inform strategies that solve long-term organizational challenges.
Prioritize a limited number of processes to improve, and expand the list over time.
Data projects falter when teams have overly-complicated data sets to collect before they get started. Instead of trying to solve all of your organization’s problems with one all-encompassing project, try to pick a few processes that could benefit from a closer look in the short term. “Putting data to work includes the whole sequence, from data to insight to profit,” writes Thomas Redman, President of Data Quality Solutions. Find areas that could use immediate improvement, for instance, shortening employee onboarding processes, and determine what insights are needed, and how successfully implementing changes will help you meet revenue targets. To get started, your team could implement a baseline survey with a small subset of new employees. From there, managers could send regular pulse surveys as employees complete their first week, first month, and first 90 days to learn how onboarding process could be improved, and who could benefit the most from changes to the process. After drawing insights from your sample audience, you could test changes within regional or global onboarding processes, and track the long-term results with performance data and employee feedback loops.
Look for sign posts, not roadmaps.
Too often, companies believe that data will be a mystical oracle that will transform their organization, if they could just capture it all in a simple dashboard. “You get tempted to know everything and that’s not a strategy, it’s an act of desperation that is doomed to end in failure,” writes big data and analytics expert Bernard Marr for Forbes. Instead, think of your data as a means of getting insightful recommendations, not black and white answers. For instance, data might reveal that your most successful engineers are musicians who enjoy gaming, or that your most productive sales managers have less than 6 direct reports. These types of real-time people insights can serve as a recommendation engine to help you tune your people strategy, allowing you make more informed decisions about hiring, incentivizing, and creating teams as your organization grows.
Data is notoriously hard to capture, and even when you have access to it, it’s not a silver bullet that will drive results for your organization overnight. At Structural, we’re working on new ways to use people data to surface insights and provide recommendations that make it easier to build stronger teams and close more business. If your organization is still figuring out how to put your data to work, we’d love to hear about it. Drop us a line, or subscribe to Structural updates to stay in touch as you work toward a more insightful approach to driving growth.