Read an Excerpt
CHAPTER 1
Define the Business Challenge
Chapter Snapshot
Questions we will answer:
• What are HR analytics?
• Why are they important?
• Why should I read this book and what can I expect?
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Meet Jen. She's the HR director of a 1,200-person company. What started as rumblings of an issue has become a full-blown problem. A few vocal managers have been complaining that her company is struggling with turnover. She doesn't know how serious the issue is. She does know that the HR department can't be perceived as unresponsive.
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HR has a tough job. It has to serve employees; protect the business; keep up with ever-changing laws and regulations at local, state, and federal levels; and figure out how to do more with less, faster. These demands leave HR professionals with big questions: How can we make better decisions about where to invest our limited resources? How can we deliver solutions that make employees happy? How can we convince the C-suite to invest in people initiatives? How can we make sure HR helps the organization achieve business outcomes?
HR analytics can help with all of these things and more. It's not a silver bullet, but it is a powerful tool for elevating the credibility of the HR function.
I know — I've described only the tip of the iceberg of what makes your job hard. Now I'm saying you need to learn something that might be new. It may also be intimidating. You might have even gone into HR to avoid having to take extra math classes. But hang in there. I'm going to make this as painless as possible.
In this book, I'll use an example of a common, real-life business challenge to teach you basic concepts. This example will help you understand how to
• identify where analytics can help,
• differentiate types of metrics and analytics,
• use different types of metrics and analytics, and
• maximize their impact.
This book provides an approachable introduction to HR analytics. I won't get into the technical details of building databases or cleaning and analyzing data. Instead, I'll give you enough information to know what to request from an analyst and what to do with those results. But don't worry — if you want to dig deeper, I'll direct you to other resources to get you started.
Throughout this book, you'll notice some icons that keep popping up. These indicate opportunities to get into something extra. Anytime you see "Take a Deeper Dive," I have curated outside resources, giving you the option to go more in-depth. As an HR professional, you know that "telling ain't training." To that end, I've built in reflection questions and activities throughout to make sure you're absorbing everything. Wherever you see "Reflection," it'll be your turn to assess the situation and share your thoughts. There's also a whole workbook in the appendices to help you apply the content back at the office. Lastly, each chapter ends with insights from the SHRM Research Lab. Consider this optional information if you'd really like to dive into a certain topic.
There you'll see research findings from the behavioral sciences to explain why things happen. Now that you know how to use this book, let's dig in!
Reflection
Let's return to Jen's situation. Her plate is full. How might she go about deciding how big of a priority the managers' complaints are?
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Jen knows these managers are usually pretty quick to complain. However, she wants to be responsive. She decides to take a look at some metrics. She asks an analyst to pull the last year's turnover numbers. They indicate turnover increased about a percentage point over the last four quarters. Jen doesn't see a problem, so she writes a polite email to the managers and moves onto the next item on her list.
The next day Jen is called into an emergency meeting. She walks into the room and sees her boss sitting there along with Mark, the director of the managers who complained to her yesterday. No one looks happy. She senses that she's in the hot seat.
Mark asks Jen to explain what happened. Suddenly she's getting peppered with questions. How is it possible that turnover isn't a problem? All of Mark's direct reports claim to be losing their best people. What is the company's turnover rate? Is that high or low? What if only the best performers are leaving?
Ultimately, Mark says he doesn't care what the numbers say. They can't meet their objectives without the necessary talent and that is her problem to fix.
After the meeting, Jen's boss sticks around to talk to her. She starts by asking Jen to reflect.
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Reflection
What went wrong in Jen's situation? How might this all have been avoided?
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In the past, HR professionals typically relied on their understanding of HR processes and the organization to make decisions and serve their stakeholders. Hard data were limited. Any data that were captured were done so manually or were housed in different systems that didn't talk to each other.
Luckily, the world is changing. Thanks to advancing technology, data and information are everywhere, which means they're easily and quickly accessible. Most organizations have human resource information systems (HRISs) to record basic transactions, such as hiring date, compensation, promotions, and performance ratings. Increasingly, organizations are also collecting information about the learning and development of their employees. This includes proficiency levels of various skills and aptitudes, as well as training and development engagements and outcomes, such as end-of-training scores.
Despite having access to more data and analytic power than ever before, many HR organizations still aren't relying on those to make significant decisions. Finance, customer service, marketing, and sales functions all use data extensively. Your stakeholders are using data and analytics more and more. Their expectations and recognition of the importance of talent-related data are increasing too. Not only are your managers and directors concerned about something they see as a big business problem, they may also be expecting you to approach it the same way they approach issues in their department. For HR leaders to have broader organizational impact, they too need to shift from a focus on HR processes to the impact of talent on the broader business strategy and outcomes.
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Jen took the first logical step in looking into her company's turnover rate, but what did she miss by stopping there?
To start, organizations act more like complex ecosystems than as single organisms. Looking at only the top level or company-wide turnover rate, Jen may miss what's happening in a specific skill area or part of the company. She also may not have enough information to understand who is leaving and what impact those departures have on the business. If the bulk of the work is being accomplished by a handful of star employees, the impact may be a lot higher than the numbers alone reveal. The managers may be sensing something that hasn't yet started showing up in the metrics HR routinely analyzes. Jen may need to collect more information to fully understand the situation.
Maybe Jen has just fallen into old habits. She had a transactional interaction with the managers. They complained. Jen did research. She sent them the outcome. Today's HR professionals have to move from transacting to consulting. The managers are the ones closest to the people and business issues. Jen can partner with them; she'll bring her knowledge and skills together with theirs to understand the situation and co-create a solution.
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There's a lot of talk about big data, algorithms, and automation. However, the value of data is limited by your ability to extract information and insights from them. Organizations are using technology to collect more and more data. They need people capable of interpreting the data and extracting valuable information from them. Equally important, they must help turn that information into insights that can be used to make better decisions and provide a competitive advantage.
Analytics provides a way to demonstrate the linkage between people and business outcomes. HR analytics (also called people analytics or talent analytics) use measurement and analysis techniques to understand, improve, and optimize the people side of business. Data are the raw numbers you track. When an employee who reports to one of the managers in Jen's scenario leaves, it creates data. Metrics focus on counting, tracking, and presenting past data. Analytics uses statistics to help you see patterns in the data. Figure 1.1 shows how these three pieces lead to HR solutions.
Jen was looking at the complaint through a metrics lens. How many people have left, and has that number increased over time? Shifting her lens to analytics — focusing on who is leaving, what is their impact on the business, and why are they leaving — can give Jen a lot more information.
Take a Deeper Dive
For more on differentiating between data and metrics, see "Know the Difference between Your Data and Your Metrics" by Jeff Bladt and Bob Filbin.
Survey evidence shows that HR's credibility increases as it starts using data to inform its decisions. This means that not only can analytics help you diagnose and solve problems, it also can make you look good, make the HR function look good, and positively impact the bottom line.
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Jen and her boss talk through what went wrong. This officially becomes Jen's top priority until she comes to a resolution that the managers are on board with.
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In the next chapter, I'll help you understand what you'll need to use analytics. I'll also help you figure out what you already have.
How Might This Look Different in Small Organizations?
The example we're working through uses a large organization as the backdrop. However, things might look different in small organizations. For starters, you have fewer employees. Even one person leaving could significantly impact your turnover rate. This means you may need to rely even more heavily on qualitative information (things like interviews with stakeholders or employee exit interviews) and external information (market conditions such as unemployment rate, external benchmarks, etc.). Later, we'll revisit how Jen's situation might unfold in a small organization.
From the SHRM Research Lab
People aren't as good at making decisions as they think. We like to think of ourselves as rational actors, but our informational-processing limitations, emotions, and biases get in our way. The world is complex and humans have developed ways to help simplify it. So-called cognitive biases are ways our brains help us take shortcuts to deal with four primary problems: information overload, lack of meaning, the need to act fast, and knowing what needs to be remembered for later.
These shortcuts come at a cost. There are also four primary problems that these solutions create: we don't see everything, we create illusions, our quick decisions can be seriously flawed, and our memories can reinforce those errors. Despite all the evidence that our judgments are faulty, leaders typically rely on their guts.
Take a Deeper Dive
For more detail on cognitive biases, see Figure 1.2.
We can do better. There are ways to mitigate the limitations in our decision-making, including relying more heavily on the systematic collection, analysis, and combination of data. Professional judgment that incorporates hard data or is based on statistical models is more accurate than judgment based on individual experience. Information based on scientific research is more accurate than the opinions of experts. This means that if you start using analytics, not only can you improve your decisions, you can also help your leaders make better decisions. This is a win-win situation.
If we can make better decisions, why do so many leaders still rely on gut instincts? Unfortunately, people tend to trust their own judgment more than data and algorithms, even when they know it is less accurate. We call this "algorithm aversion." Although it can make prediction worse, people are more willing to trust algorithms if they can insert their own judgment by tweaking the outcome.
Key Takeaways
• HR analytics apply measurement and statistical techniques to identify and understand patterns in data. This information helps optimize the people side of the business.
• HR analytics can improve the credibility of the HR function by showing the linkage between people and business outcomes.
• Technological advancements have made data — about people, processes, business outcomes, customer engagement, and more — available. Other fields are using these data to build a competitive advantage, and they are expecting you to do it too.
Pratt & Whitney Creates a Workforce-Planning Vision
The People Analytics (PA) team at Pratt & Whitney was early into the development of an analytics function when they realized that sophisticated data models weren't going to win over their stakeholders. When tasked with creating a workforce-planning portfolio that applied across the organization, the PA team readied themselves to take on a long-tailed and ever-evolving data project. At the onset of the project, they found that each stakeholder group described the workforce-planning process differently. This made proving the value of the new function an uphill battle.
The PA team began with the critical step of understanding the anticipated project outcomes by holding informal meetings to collect input from each stakeholder group. Using clear and concise language, they defined an overarching value proposition that fit the needs of all stakeholders: workforce planning provides Pratt & Whitney with the ability to place the right people with the right skills in the right roles at the right time through agile planning tools and methods. Pratt & Whitney took the additional step of literally visualizing the more popular inputs and outputs of its models to show all stakeholders that their variables would be considered (see Figure 1.3). This visual allowed all customers, whether mathematical gurus or relative newcomers to data, to clearly understand and reference the workforce-planning vision.
Even with the stakeholders on board and the anticipated outcomes defined, the PA team encountered challenges. They concluded that no single model could answer every stakeholder's workforce-planning questions. They quickly discovered that it was the ability to deliver quick wins from many imperfect analytic models that allowed for healthy dialogue and helped guide the workforce-planning scope in a way that always pointed back to the value proposition. The solution was to adopt an agile approach to workforce planning: the components of each model would vary based on the stakeholder group and the specific workforce-planning questions that the group was trying to answer (Table 1.1).
Despite the ever-changing nature of stakeholder needs and countless additions to the workforce-planning portfolio, it was not long before the PA team offered insights to the business that began to drive meaningful decisions that in turn became points of maturation in the workforce-planning portfolio.
Through this iterative process, Pratt & Whitney continuously improves upon its interconnected workforce-planning toolkit through connections between HR strategy and the larger business plan. By ensuring a collective understanding of the initiative's output at the onset of the process, the PA team ensured that analytic projects gained buy-in and became an integrated part in a proactive HR decision-making process.
CHAPTER 2
Understand the Analytics Domain
Chapter Snapshot
Questions we will answer:
• How do I respond to any pressure I'm feeling to resolve the people problem my business leaders are complaining about?
• How can I build a stronger partnership with business leaders?
• What skills do I need to have to develop my expertise in HR analytics?
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After some creative calendar clearing, Jen sits down to wrestle with her new top priority. The managers are acting like there is a talent emergency. She doesn't see evidence of a turnover problem — at least based on company-wide metrics. Regardless, it's clear that this issue isn't going away, so it's time to dig in.
Jen decides to schedule a meeting with the managers who first raised the turnover concerns. But first she wants to make sure she's done her homework. She needs to turn this relationship around fast. She already has the organization-wide turnover numbers and percentages over the last year broken down by quarter (see Table 2.1). Turnover did go up, but by just over a percentage point. It's no big deal, right?
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So, is a 1.24 percent increase in turnover a big deal? Maybe, but maybe not: it's all relative. With an organization-wide turnover rate of 6 percent, an increase of close to a percentage point may be nothing to sneeze at. An additional fifteen people walked out the door as a result of that approximately 1 percent increase. To get a better sense of what might be going on, Jen may need more information to better assess what she's seeing.
(Continues…)
Excerpted from "The Practical Guide to HR Analytics"
by .
Copyright © 2018 SHRM.
Excerpted by permission of Society For Human Resource Management.
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