People Analytics For Dummies

Mike West

• Publisher: For Dummies
• Year: 2019
• ISBN:
  9781119434795
• Edition: 1
• Topic: Business
• Subtopic: Human Resource Management
• Index: Business

Maximize performance with better data

Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them.

People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce.

• Start a people analytics project
• Work with qualitative data
• Collect data via communications
• Find the right tools and approach for analyzing data

If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier.

Part 1

Getting Started with People Analytics

IN THIS PART …

Discover exactly what people analytics is

Make the business case for a people analytics project and figure out where to begin (all at the same time!)

Understand the differences between an insight-oriented analytics project and an efficiency-oriented analytics project

Get acquainted with a matrix of current options for managing people analytics moving forward

Chapter 1

Introducing People Analytics

IN THIS CHAPTER

People analytics, defined

Examining how some businesses already analyze people data

Starting your first people analytics project

A business consists of people who work on behalf of the company (employees) doing things for other people who don’t work for the company (customers). Business decisions about people working for the company — who to hire, where to find them, what to pay them, what benefits to provide, whom to promote, and countless other decisions — have a substantial unseen impact on the company’s capability to meet customer needs, bottom-line performance, and reputation.

Traditionally, the way the leaders of companies have made human resources-related decisions has been based on gut instinct, copying what other companies are doing, tradition, or compliance with government mandates.

Today, many business decisions are now being made with data. What customer segments to focus on, what product feature improvements to make, what projects to invest in, and where to put a new store are just a few of countless examples of important business decisions that are increasingly made with data. If you go into a board meeting or participate in an investor phone call, you will see that the most important parts of the discussion are all about a series of important numbers recorded in the balance sheet, what the company is seeing in other numbers that suggest actions that may impact the balance sheet, and whether or not previous actions that promised to impact the numbers in the balance sheet have actually done so. The conversation may drift from abstract to tangible and back to abstract again, but numbers serve the purpose of keeping the conversation anchored to what is real and to drive accountability for real results.

Fortunately, now you can use data for human resources-related decisions, too. Thanks to the prevalence of human resource information systems, plus the wide-scale accessibility of modern data collection, analysis, and presentation tools, human resources-related decisions can be made with data just like countless other business decisions.

In this chapter, I define the term people analytics and talk about some of the ways that companies I’ve worked with have used a human resource approach informed by data to solve real-life business problems. Then I describe how you also can add people analytics to your arsenal — and increase your people data savvy, too.

Defining People Analytics

At a high level, people analytics consists simply of applying evidence to management decisions about people.

More specifically people analytics lives at the intersection of statistics, behavioral science, technology systems, and the people strategy.

People strategy means making deliberate choices among differing options for how to manage a group of people.

Figure 1-1 illustrates how people analytics joins together these four broad concepts (statistics, science, systems, and strategy) to create something new that didn’t exist before.

FIGURE 1-1: People analytics is what happens when human resources professionals realize the power that a good dataset gives them.

Many forward-thinking companies are already realizing the benefits of evidence-based decision making in human resources. To identify what other people think people analytics is, I rounded up 100 job descriptions related to people analytics from job boards. To summarize, I created a word cloud from the words in those job descriptions; it appears in Figure 1-2.

FIGURE 1-2: Creating a word cloud is a kind of data analysis to identify and visualize trends in vocabulary.

If you’re not already familiar with word clouds, this is how they work: The more frequently a word appears in the text that you’re analyzing, the bigger and darker that word looks in the word cloud. You can tell from the figure that data, analytics, human resources (HR), and business must be central concepts to people analytics.

These 100 job descriptions are from Human Resources department that are ahead of the pack in using hard data and analysis as decision-making tools. The insights data is providing these companies gives them an advantage over companies that do not yet know how to do these things. A vast majority of companies do not yet have people analytics and most people do not even know what people analytics is. That being the case, you, by learning about people analytics, will be in a great position to differentiate yourself among your peers (and your company among its competitors).

Solving business problems by asking questions

Like all business analysis disciplines, people analytics offers businesses ways to answer questions that:

• Produce new insight
• Solve problems
• Evaluate the effectiveness of solutions and improve going forward

Produce new insight

Donald Rumsfeld once said, “There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don't know we don't know.” Donald Rumsfeld can get his words a little twisted up, but to finish his point for him: the most perilous things in this world for you are the things you should know but don’t know you should know. One of the great contributions people analytics can make to you is to reveal some of the perilous things you don’t know and don’t even know you should know but in fact should know.

This unknown unknowns’ problem can be epitomized by an experience I had with a large pharmaceutical company. This company was very successful. It had an over hundred-year history of scientific achievement and business success. This company was a leader and financial powerhouse in its industry, if not all industries. They were a great company and they knew it.

With a smart, scientifically-oriented management team, the company tried to measure nearly everything. As a result, it was among some of the first companies to apply rigor to human resources with data. This is how I got stared in the field of people analytics before we even called it people analytics. After working at this company, I went on to do this work at other companies, but work in the people analytics field was few and far between back in the early days.

One of the earliest data-oriented human resource activities at this great pharmaceutical company was to participate in a common employee survey conducted across many companies, facilitated by a consulting firm that would provide confidentiality to everyone involved. This survey allowed the company to compare itself as an employer against a selection of the highest-performing companies across all industries across roughly 50 aspects of the employee experience using roughly 100 survey items. A few examples of the categories of employee experience the survey measured were: employee opinion about the company’s prospects for future success, leadership, managers, pay, benefits, opportunities for learning and development as well as attitudes such as overall satisfaction, motivation, and commitment to the company.

In reviewing the results, it was no surprise to all that this well-run company performed above other high-performing companies in nearly all categories of the survey. Employees at this company were on average more committed, more motivated, and happier than employees at other companies and all of this could be validated statistically.

What was surprising to everyone was that the company performed slightly below other high-performing companies in a set of questions the survey referred to categorically as Speaking Up. The Speaking Up category represented agreement or disagreement with statements that indicated employees felt the company provided a safe environment for them to express their concerns or disagreements with their superiors. This finding seemed odd, because everyone talked about how the company had a history of making decisions by consensus. When young, intelligent scientists joined the company, they were told to be aware of the importance of consensus in the company’s culture and should therefore expect to work together with others more than they might have had to do in previous environments.

Given the seeming oddness of the Speaking Up finding, and that the company had performed well on all other questions on the survey, no substantive new actions were decided. There was some concern expressed by the head of human resources about the Speaking Up items, but at the time there was an ongoing debate among the executive leadership team about whether or not the company should intentionally break its culture of consensus decision-making in order to keep up with new competitors. At the time, the assessment of the leadership team was that, overall, the survey results were good and the Speaking Up issue must have just been echoes of their effort to change the culture for the better.

No one at the time foresaw the connection between the survey findings and the disaster that would ensue next. Around that time, a previously successful but bullheaded research director had disregarded the concerns of some scientists about a possible safety issue with a drug. The safety issue was not crystal clear at that time, but the issue should have received more attention. The executive had a reputation for having a big ego, but he also delivered results for the company, so the company let him win this argument. Time and attention costs money. The scientists’ concerns about the drug were squelched in favor of progress. The result of rushing ahead was a drug that later had to be recalled — a foolish mistake that risked lives, cost the company billions of dollars, and nearly took down the company for good. At the direction of the bullheaded director, the company pushed through a pharmaceutical product that should have been scrutinized further. Specifically, no scientists should have been made to feel unsafe to express their opinion and all credible concerns should have been researched more thoroughly before taking the drug to market.

What this example shows is that even simple early efforts in people analytics — a seemingly trivial employee survey — can deliver new insight that is not obvious or trivial. The result in this case may not be the best example of successful people analytics, but it illustrates the potential in ways that success wouldn’t have. Unfortunately, at the time nobody knew that the weakness identified in the survey was so important. The survey produced an insight that blew in the opposite direction of what the executives believed and so the weakness ...

Table of contents

• Cover
• Table of Contents
• Introduction
• Part 1: Getting Started with People Analytics
• Part 2: Elevating Your Perspective
• Part 3: Quantifying the Employee Journey
• Part 4: Improving Your Game Plan with Science and Statistics
• Part 5: The Part of Tens
• Index
• About the Author
• Advertisement Page
• Connect with Dummies
• End User License Agreement