It is not enough to simply have data. Today HR leaders must uncover insights from the vast amounts of data and translate that into meaningful actions of what to do and what not to do.
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The yin and yang, an ancient symbol of harmony and balance, has been around for centuries. The symbol is used to represent good vs. evil, light vs. dark and negative vs. positive. More recently its used to characterize the interplay between art and science. In a world of big data—where the amount of available data grows by the minute—the pendulum is swinging to focus solely on the science. But data without context (or interpretation) is just data. To make it insightful, you need to wrap interpretation, judgement and narrative around it. You need to couple it with the “art.” That way, the data isn’t just presented ... it’s exposed, and it begins to provide clarity of action.
That’s the exact challenge we face every day in workforce analytics: how do we strike the balance between art and science? We often find that even with a lot of data at hand, it often fails to tell the full story. To truly uncover meaningful insights, we need to peel back the layers of the data and find out what is it NOT telling us. This is where the art comes in. The art of looking at the data through the lens of the business landscape brings the data to a higher order of analytics, insights from data that drives impactful actions. Which matters, because if we’re ever to truly live up to our promise to build more inclusive companies on this planet, we need every bit of data we can get.
Like chapters in a book, evidenced-based insights (the science) with business context (the art) tell a story, leading up to a crescendo of conclusions. If a board, let’s say, acts upon partial or wrong information—they’ll draw the wrong conclusions and even take the wrong steps. But this article isn’t about that—it’s about how to get things right.
Getting It Right
Over the last few years, there’s been an incredible build up happening around inclusion and diversity, with a focus on diversity data. In partnership with the board, we realized that instead of being truly insightful about what was happening with progress on diversity, we were instead providing basic information on percentage changes from year to year; it was more of a check-the-box exercise. We were simply reporting the data (what was happening) versus effectively analyzing the data (why it was happening and how we can fix it). We needed to explore the bigger picture and address the threshold questions that were top of mind. What insights should we derive from the data? What outcomes should those insights drive? What isn’t the data telling me that would make my actions impactful? We needed to get more granular and build robust analytical models that revealed more astute insight.
Today it’s no longer sufficient to show the leadership team and the board simple analytics, percentage increases/decreases, attrition rates and large grouped data; this only tells a fraction of the story. They both now expect and deserve more. We may know that we have a certain number of women globally or U.S. minorities, but this is a single point in time metric without any underlying narrative around its health to the organization. It’s only when we can compare that to the inflow of talent, the development and the exits that we begin to understand. Unless you analyze hiring and turnover rates together, for example, you may live under the false assumption that your percentage increase in women representation is good—because you will not have analyzed who’s walking out the door.
To drive diversity, the board needs to see patterns in all aspects of our talent strategy. Part of our job is to keep us all honest by asking the following questions:
To help, we created visual data maps to illustrate talent flowing in (hires), up (promotions) and out (exits) of Eaton. We like the Internal Labor Market (ILM) Map1 because it provides answers to who gets hired, performs well, moves upward and stays. If we focus in on gender and U.S. ethnic groups, it begins to tell us where we’re doing well and where to improve.
To make this point clearer, as you can see in Figure 1, we examined data in one of our large business segments and were surprised to find the drop in representation of women from the professional to manager level (from 44% women to just 14%). With some deeper investigation, we found that men were twice as likely to get promoted into manager positions compared to women (4% promotion rate for men compared to just 2% for women). In addition, there were significantly higher rates of voluntary exits among women at the managerial level (12% voluntary turnover for women compared to 4% for men). Essentially, we weren’t moving women up, and we weren’t keeping the ones we had. We’re now focusing on unconscious bias and whether it may be affecting promotions, nominations and mission-critical programs—because of these discoveries from our data.
A Tangible Journey
It’s not uncommon to see organizations set broad goals without understanding the actions needed to achieve those goals. At Eaton, we faced the same challenges. We found that instead of setting customary diversity goals, we needed to better understand what we wanted to achieve and what actions would help us get there. This is again where ILM maps can be instructive. It shows us our current state, but also provides a baseline to project the actions we need to make improvements.
Currently we’re aiming to increase representation of our professional workforce of global women to 40% and U.S. minorities to 34% by 2030 (See Eaton’s 2021 Global Inclusion and Diversity Transparency Report2). Using data from our ILM maps, we can craft a 5-year projection—outlining which levers (hiring, promotions, retention) need to be pulled to net the greatest impact (Figure 2). Now we have the data analytics needed to achieve our targets; we have real and defined actions that our senior leadership team fully supports.
The key is to strategically monitor and move the levers to drive sustainable impact. For example, in one of our critical business divisions, based on our projections, we know we have to adjust the hiring of minorities (to 35% of hires) and significantly reduce turnover (by over 10%) to achieve gains in representation. We can’t pull just one lever alone and that would historically be the solution if we had not done the richer analysis. Reaching these milestones is certainly a tall order but important for the organization to flourish.
The same data has helped glean insights across our functions, too (Figure 3). By highlighting the flows of talent in our functions by gender and ethnic diversity, we found where we could improve. We found that in one high growth region, we had some of the lowest representation of women leaders in functions that led to General Management (GM) roles. Furthermore, very few of our slates for senior level hires in those functions were well represented. To move the needle in our GM pipelines, we needed to make progress in the development of our employees and in the diversity of those slates. Through further analysis, we also found that when we have diverse candidates, and they make it to the offer stage, there is ~85% acceptance rate.
Driving Ownership and Accountability
We talked earlier about our journey of moving from reporting what was happening to analyzing why it was happening and how to fix it. This has been particularlyRecently our workforce analytics team worked in partnership with our Asia Pacific Regional Inclusion Council (RIC) to help move the needle. The RIC provides regional focus, leadership support, resources and accountability that help create a diverse and inclusive work environment. As part of a larger effort, the APAC RIC wanted to understand the leading predictors of turnover, particularly among women.
Using statistical modeling techniques, the project team identified the events and experiences that increased (or decreased) an employee’s desire to quit (Figure 4). After controlling for individual attributes, organizational practices and external labor market conditions, the analysis rendered a set of drivers ranked by their impact on retention. Some of the strongest drivers were around supervision and experience in the business. While all APAC employees were 90% more likely to leave when their supervisor left Eaton, women were also 52% more likely to leave when their supervisor simply
moved to another part of Eaton. But experienced supervisors helped in the retention of women; that is, women who had supervisors with more than 1 year of Eaton experience were 90% more likely stay.
The data gave insight that contrasted a “try everything” approach so often applied to retaining talent. While it could’ve been easy for the HR analytics team to simply outline actions and interventions to curb turnover, it was imperative that business leaders on the RIC drove the conversation. We leveraged virtual workshops using virtual whiteboarding and real-time questioning, polling and rating so that leaders across the globe realized the impacts from these predictive insights and could derive actions from them. Project teams were deployed to tackle onboarding, manager quality and transitions as well as talent recognition. Today, 90% of their suggestions are now being implemented. This type of predictive turnover modeling work is being completed in other business segments with the same energy and passion to improve retention.
Poised for Success
How did we get here? As a new CHRO at Eaton a few years ago, I understood the value of sophisticated analytics and one of my first actions was to hire anEaton’s Workforce Insights Team has deep data engineering and statistics expertise and is heavily leveraged across the entire company. We have methodically moved the organization—both HR and business leaders—up the learning curve in transitioning from pure reporting to insightful analytics, from just anecdotes to art and science. The demand for workforce analytics is continuing to gain momentum driven by the outcomes their insights highlight. When the business stops asking you for a report and instead starts inquiring for analytics and interpretation around key strategic initiatives, you know you’ve won.
As the team leverages more sophisticated analytic approaches, we’re building out an HR curriculum to support their efforts. This includes a library of videos and podcasts with topics ranging from “Storytelling with Data” to “Forecasting Headcount.” The team even released a dashboard, the Eaton Talent Footprint, allowing all HR professionals to easily explore a map visualizing Eaton’s talent and locations (Figure 5). The dashboard allows quick answers to various question such as: How many engineers do we have and where are they located? How many locations are there in a particular business segment? Where are our HR employees in a specific region? How many employees are in a particular union site?
Another marquee release was the Inclusion and Diversity Dashboard delivered in Power BI, which enables a dynamic, cross functional, multi-faceted view of how diverse talent moves across all functions of the company. With this visibility comes accountability for improving diversity and we expect this from our leaders.
Still, the real truth is that you don’t need sophisticated tools and statistics to get started, but you do need to start. Calculating and providing simple counts and rates of hiring, promotions and retention is the place to begin. That’s where we started, and we built the story—and capability—from there. What truly helps is the art complementing the science of the data, true partnerships with HR business partners who provide the business context behind the insights we see. And yes, be patient. It won’t happen overnight, but you’ll see momentum quickly when your HR practitioners engage in it, your business leaders request it, and your senior leaders and board make the right decisions because of it.
A More Informed Boardroom
It’s all about having a richer dialogue in the boardroom. We’ve moved from asking for more data to bringing in more insightful analysis that changes the discussion to the powerful outcomes that can be achieved. It’s amazing to watch the transformation because we are all learning in the moment, in ways that the traditional approaches could never reach. This is evidence-based analytics that, in each iteration, unmasks more valuable insight that propels us on this transformation.
New types of leaders are now engaging with the board. While it used to be mainly the CHRO, workforce analytics leaders are now spending time in the boardroom.
Our goal is to provide our board with valuable insights that make their decision making smarter, not in the intellectual sense but in a way that builds confidence because they know the analysis is world class. They also know that we will continue to build on those insights because the analysis should continue to stretch and challenge our thinking so that we continually make decisions that make the company better.
Where are you on this journey? How will you start your movement? My challenge for you is to challenge your organization to expose the data; once exposed, your yin and yang will become much clearer.
1 Nalbantian, Guzzo, Kieffer and Doherty. Play to Your Strengths. McGraw-Hill, 2004.
2 EATON Inclusion and Diversity Transparency Report, 2021.
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