Introducing People Analytics

In this:

• People analytics, defined
• Examining how some businesses already analyze people data
• Starting your first people analytics project

A business consists of people - 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– whom 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 decided about human resource-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 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 critical business decisions that are increasingly made with data. If you go into a board meeting or investor phone call you will see that most important parts of the discussion are about a series of essential 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 change the numbers in the balance sheet have actually done so. The conversation may drift from abstract to actual and back to abstract again. Still, numbers serve the purpose of keeping the conversion anchored to what is real. And numbers drive accountability for actual results.

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

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.

<TIP> In this context, people strategy means 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 already realize 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 departments that are ahead of the pack in using hard data and analysis as decision-making tools. The insights data is providing these companies provide an advantage over companies that do not yet know how to do these things. A vast majority of companies do not, however, have people analytics, and most people do not even know what people analytics is. That being the case, you, by learning about people analytics, you will be in a high 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:

• 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 little twisted up, but to finish his point for him: the most dangerous things in this world for you are the things you should know but don’t know you should know. One of the significant contributions people analytics can make to you is to reveal some of the dangerous things you don’t know and don’t even know you should know but should know.

Some early career experience I had with a large pharmaceutical company epitomizes this unknown unknowns problem. This company was very successful. It had an over a hundred-year history of scientific achievement and business success. This company was a leader and financial powerhouse among its industry, if not all, industries. They were a great company, and they knew it.

With a math and science-oriented management team, the company was before most other companies in interest in applying scrutiny to what they to find improvements or advantages with data. As a result, it was among some of the first companies to apply rigor to human resources with data. This is how I got started 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 people analytics was few and far in-between back in the early days.

One of the earliest data-oriented human resource activities was to participate in an employee survey conducted across many companies, facilitated by a consulting firm that would provide confidentiality to everyone involved. This survey allowed the pharmaceutical company to compare itself as an employer against a selection of the highest-performing companies across all industries, comparing across roughly 50 aspects of employee experience using around 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, the opportunity for learning & development as well as attitudes such as overall satisfaction, motivation, and commitment to the company.

In reviewing the results, it was no surprise 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, motivated, and happy 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 would tell them to be aware of the importance of consensus in the company’s culture and, therefore, to try 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. Still, 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 to keep up with new competitors. At the time, the leadership team assessed 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 favor in the situation went to the director. Time and attention cost 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 deliberated longer. 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 unimportant employee survey – can deliver new insight that is not obvious or trivial. The result, in this case, is not to 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 significant. The survey produced an insight that blew in the opposite direction of what the executives believed and so the weakness identified was disregarded. Mysteriously the employee survey had actually predicted the reason for the company’s near demise – it was providing access to an unknown unknown. In short, the survey was warning us about something the company otherwise could not have known that was important. Had the company taken the Speaking Up issue more seriously, executives could have put in place a way for the concerned scientists to express themselves to slow down the bullheaded director, preventing the giant mistake. Now I know to take even a basic employee survey effort very seriously because you don’t know what you don’t know, but when you ask a lot of questions, you have a flying chance.

Solve problems

Data can also help you devise solutions to known problems.

A children’s hospital knew that the attrition rate for nurses in their first year was 25 percent. This attrition rate means that 1 in 4 nurses hired would leave the company in the first year they were employed by the company. In contrast, the average employee attrition rate was only 10 percent, meaning 1 in 10 people overall would leave the company in a given year. Therefore, early tenure nurse attrition was 2.5 times worse than average attrition. Even worse, early tenure attrition is a self-reinforcing problem because if you change nothing, there was a fairly high chance the replacement may also leave as quickly and around and around you go.

For good reasons, the hospital wanted to bring that early tenure nurse attrition rate down. Each nurse exit in the first year had to be replaced by another nurse that had to be identified, hired, on-boarded and trained. Of course, hiring and training new nurses costs money, but more importantly, new nurses are less familiar with how to deal with complex situations and more likely to make mistakes than experienced nurses.

Some analysis of applicant and employee history data showed that the hospital could hire nurses more likely to stay by simply hiring more experienced nurses, rather than nurses straight out of nursing school. It seems obvious to say now, but they didn’t know how much of a difference it would make for them operationally until they looked at the data. While more experienced hires have to be paid more, the data showed they were also more likely to be successful, and they were more likely to stay with the children’s hospital beyond their first year. Focusing on hiring the candidates with the characteristics that predicted longevity, the data showed the hospital could reduce the overall first-year attrition rate from 25 percent to 15 percent. The reduction in attrition on the cost of recruiting and training would more than offset the cost of spending more money to hire experienced nurses from the outset. As time went on, the attrition rate of nurses decreased, costs went down, and patient safety measures went up.

Evaluate solutions and plan to improve

You also can use data to evaluate the effectiveness of solutions to increase the certainty that the solutions will work before implementing them more broadly. Experiments can provide a dataset that allows testing ideas in ways that prevent costly mistakes and provide a dataset to facilitate improvement before rolling out ideas more broadly.

A pet store chain had a history of collecting standard retail measures, like same-store sales and customer satisfaction, as well as people related measures, like employee enthusiasm and knowledge of pet-related topics. Measuring both kinds of data together helped the pet store chain uncover correlations between how it hired, trained, and rewarded employees in the stores and the goals it was trying to achieve: increase customer loyalty and store sales.

By looking at the employee and customer data together, the company knew many things that other companies could only dream of knowing. For example, the pet retailer knew that the more employees in the stores knew about a pet topic, the more the store would sell in that pet topic area. For example, if people in the store knew a lot about frogs, they would sell more frogs. If they knew less about birds, they sold fewer birds and bird-related supplies. And so on and so forth across every category of pet. If what the store employees had the knowledge to capture the imagination of the customers, as well as help the customer solve their pet problems, the customer spends more money with the store over time. As a result of this information, the pet retailer hired, trained, and rewarded to increase employee knowledge about pet topics. The pet retailer also used test results to identify stores that need training, measure the results of the training, and assess the impact on the bottom line.

Even with all of this, the pet store chain was seeing increased competition from big-box retailers, grocery stores, and online retailers, which made it difficult to grow revenue profitably. Big-Box retailers, grocery stores, and online retailers were starting to stock many of the same items as the pet retailer, and they could offer these items at a lower cost. If the pet retailer decided to compete on cost, it would put pressure on the profit margins of the pet retailer because the pet retailer didn’t have other items they could mark up to make for the losses in the items they marked down to compete. To make matters worse, this was in a period of economic downturn and increasing gas costs. Customers were condensing their shopping trips to as few store locations as possible, and they were selecting the lowest-priced locations with the largest range of products. The bottom line is that fewer customers coming into the pet stores meant fewer sales.

The pet store needed to get a handle on its situation, and so it embarked on some new store-level experimentation and analysis. One of the experiments of the pet retailer embarked on was to use some of the square footage available in some of the stores to offer pet services in addition to pets and pet supplies. Examples of pet services include dog grooming, doggie daycare, dog training, and pet health clinics. The theory was that services would provide a reason to draw more people into the stores, and just like increasing pet knowledge increased sales; the pet retailer hoped that offering services would do the same. But no one knew if this was true.

In the beginning, the services were not offered at all pet store locations. The expertise required to offer these services required new company learning, and the employees hired to perform these roles needed to be more skilled, which meant they also needed to be paid more. The company had to learn how to source, hire, train, and pay entirely new types of people for entirely new types of jobs then they were accustomed to in the past. Rolling this idea out to all stores - without a period of observation and learning - could bankrupt the company. By choosing a small number of stores to start, the company could measure the impact of the changes, assess their performance, and assess what to do next. If the experiment with the new services was working, the services could be expanded to more stores – if not, then the service program could be modified or abandoned. If the company had implemented the services in all stores, then it would not be able to assess if they were working or not, and it would be perilous.

The way to analyze data from an experiment of this nature is straightforward. The pet retailer chose a small set of stores to implement the new pet services in and chose another set of stores without the services to compare the stores with services to. With relatively simple math, the pet retailer was able to identify the impact of the services on customer store visits, sales, and loyalty using the same data and metrics they had already been using just by comparing the location to each other. The experiment validated that adding services, in fact, increased store visits, overall customer spending, and customer loyalty in the stores that offered services versus those stores that did not. It may seem obvious that when people went to the pet store to get Fido’s trained that they also would be more likely to purchase other items. Less visible learning was that those customers that got Fido trained also spent more on Fido over the entire lifetime of Fido, not just at those visits when Fido and the pet parent were in the store together for the training. By offering services, the pet retailer was both attracting and creating better lifetime customers. The inevitable result is increased sales.

Through its analysis, the pet retailer was able to validate its investment in people to provide services that were working. The stores where services were available produced more of the business outcomes the company was looking for, the stores that did not offer services did not. The solution for the company then became more certain - expand the program. Additional research questions included the mix of services to offer in the stores and how to scale the services to more stores with equal quality, but the company new enough to proceed and could evaluate these more complex questions as they worked services into more stores in differing packages.

Further analysis of the store level employee data over time indicating that the satisfaction and retention (reduction of attrition) of service employees in the store had more impact on customer loyalty and sales than that of other types of store employees, cashiers, or stockers for example. Across all jobs, the more distinct pet-related knowledge for the job required, the more impact attrition in that job had on the pet retailer’s success. With this information, the company prioritized how it allocated its people budget to reduce attrition in key jobs, as opposed to spreading their resources out thinly across all jobs, which might produce inferior results - or no findings - while spending the same amount of money. The pet retailer learned that employee attrition matters more in specific key jobs, and since profit margins were thin, they had to prioritize where to spend it to get results.

Many people do not like to talk about differences in pay, but the reality is that there are always differences in pay based on many factors. Job responsibility is a valid criterion for differentiating pay. It is natural, in everyone’s best interest, and generally agreed to be fair, for each company to focus its resources on the unique jobs and people that make it successful. Furthermore, any entry-level store worker that wants to learn a more lucrative service job had the opportunity to apply, and frequently they did. Having a ladder of positions of increasing skill and pay made working for the pet retailer more of a long-term career opportunity to potential employees than just a fun short-term job fix. By adding higher-paying services roles, the pet retailer was able to make itself more attractive to both the customers and employees it wanted to attract for the long term.

Using people data in business analysis

People are the face, heart, and hands of your company. All companies depend on people in every aspect of their business because people are what:

• Empathize with customers wants, pains and problems
• Create and improve products and services
• Design, manage and execute the strategies, systems, and processes that help everyone work together toward a productive enterprise

Considering how important people are to the performance of each company, it’s amazing that more companies don’t study employee data for insight into their businesses. Your company probably hires experts with advanced skills to analyze your finances, equipment, and workflows, so why isn’t anyone studying the people who use these things?

Part of the reason is that, until recently, the pool of available employee data was pretty shallow. When companies had only physical file folders full of employee data stored in the file cabinet, the opportunities for in-depth, meaningful analysis were few. Over the past couple of decades, though, the amount of electronic data that companies keep (intentionally and unintentionally) about their employees has quietly been building.

Today, your company probably has a flood of electronic employee data, whether you realize it or not. You’ll find some of this data in obvious places, but you might not have thought about the data available from some of the sources I list here:

                    * Employee resource planning systems (ERP)

                    * Human resources information systems (HRIS)

                    * Payroll systems

                    * Applicant tracking systems (ATS)

                    * Learning management systems (LMS)

                    * Performance management systems

                    * Market pay benchmark surveys

                    * Employee surveys

                    * Email and calendar system data

                    * Corporate intranet (internal websites) traffic data

                    * Job boards

                    * Social network comments

                    * Government Census and Department of Labor data

The good news is that businesses do seem to know that their employees are their greatest asset. What businesses don’t seem to know is how to analyze data about their employees to improve their business outcomes. In the chapters in this book, I demonstrate how you can do just that.

Applying statistics to people management

All managers think they’re above average at making decisions, but at least half of them are wrong! I’ve just demonstrated the wide variety of data that human resources managers have available to them. Still, they need the right tools and methodologies to interpret and make decisions based on that data. If you misinterpret your data, the option that seems right can turn into a disaster for your company.

That’s where statistics come in. You might think of statistics in terms of the procedures a statistician uses, like t-tests and regressions, but analyzing data with statistics isn’t just a mechanical operation. My favorite book on statistics, The Nature of Statistics, by Allen Wallis and Harry Roberts, defines statistics as “a body of methods for making wise decisions in the face of uncertainty.” The field of statistics offers the tools, but you need to apply those tools to produce useful insight from data wisely.

Combining people strategy, science, statistics, and systems

As a relatively new field, people analytics feels a lot like what I imagine the Wild West of American folklore must have felt like: There aren’t many rules, and everyone’s making their way to some unclear new opportunity.

If you asked a group of people analysts to describe their work, the answers you’d hear would likely be quite different from each other and depend a lot on the background of the person. Here are some examples of how categorically different types of people you will find working in the field of people analytics will emphatically think differently about what they do.

                    * Human resources: Someone with this background might describe people analytics as “the decision science of HR” or “the datafication of HR.” Put a different way, as customer analytics is to sales, people analytics is to human resources. The focus of a person with human resources or management strategy background is likely on the implications of data on how the company manages people or human resources conducts its work, with less emphasis on the nuances of how the data was produced.

                    * Behavioral science: A person who comes from a scientific background is likely to describe people analytics as simply a new term referring to the nearly century-old practice among university professors and graduate student research to study people in the workplace in fields as wide-ranging as psychology, sociology, anthropology, and economics. This crowd carries the all-important distinction of three letters behind their name, (Ph.D.) or two letters before their name (Dr.). The focus of the doctors is on the application of science to human behavior to produce new learning, less so on the day-to-day processes to efficiently collect, store, and use the data. Scientists are the best types for identifying new data that should be collected and developing a reliable and valid means to collect that data, but probably not how to do that efficiently.

                    * Statistics or data science: These folks might describe people analytics as using statistical methods and machine learning algorithms to infer insights about the people aspects of businesses from data. Their focus is on mathematics and technical processes to produce insight from an existing dataset, with less emphasis on the determination of what data should be collected or how to apply the findings from data to produce change.

                    * Information technology: Someone in this camp probably would focus on the systems that make reporting and analysis more efficient to produce. Their focus is typically more on the overall data architecture and systems than the analysis itself. From an information technology standpoint, people analytics is nothing more than the application of reporting (sometimes referred to as business intelligence) to the specific domain of human resources, as opposed to something new and different.

The answer, of course, is that people analytics is all of these things and a dozen things in between. You can apply the tools of people analytics for many purposes. Just like in the Wild West, rugged individualism is a common characteristic among people analysts, but we still stand to gain a lot by listening to and learning from each other.

Blazing a New Trail for Executive Influence and Business Impact

The human resources organization that endeavors to incorporate people analytics into its processes stands to benefit in many ways. Not only does a mathematical analysis of data add weight and seriousness to the proposals to deliver to your executive team, but the results you get from programs you based on data are also better for the employees and the company.

Taking on new analytical responsibilities isn’t something you can do lightly, in any case. For human resources professionals who are accustomed to the ways of “old HR,” people analytics methods might seem very strange at first. However, learning these new tricks is definitely worth your while.

Moving from old HR to new HR

For human resources practitioners, learning new problem-solving approaches based on data and analytics doesn’t mean abandoning the soft skills they’ve developed over their careers. People analytics adds more tools to your human resources tool belt.

I tell you in the following list about some of the differences I’ve seen between organizations that use only the “old HR” approach, and those that have also incorporated the “new HR” tools and methodologies — the benefits of expanding your toolset will speak for themselves:

                    * Old HR focuses on creating policies based on how we have done it before or based on a concept known as best practices. Best practices are the idea that your company can achieve greatness by merely copying the practices of other successful companies. The idea of best practices assumes that the selected best practices are what resulted in those other companies’ success, without any scrutiny of whether or not that is true, or if the presumed benefits of the new practices can be replicated from one situation to another. Instead, New HR uses data to evaluate what was working or not working for you already in the past, scrutinize assumptions underlying proposed changes, predict what will happen if changes are implemented, and evaluate if the predictions made were correct or not.

                    * Old HR produced overworked HR professionals. The old way was to implement all possible ideas that might matter and keep adding to this each year without scrutinizing what worked or didn’t in the past and therefore resulted in adding a lot of new activities without taking any old activities away. The old way resulted in too many commitments and not enough time or resources to achieve consistent results in any one area. New HR uses data to direct time and resources on what matters most and reducing time and resources on those things that do not matter at all or as much.

                    * Old HR direct HR professionals to deliver programs, practices, processes, and policies in functional focus silos such as Talent Acquisition, Compensation & Benefits, Employee Relations, Diversity.  New HR uses data to identify system-level results coming from cross-functional inputs and collaboration on big-picture problems.

                    * Old HR assumes that success consists of staying busy with activity. New HR doesn’t confuse motion with progress.

                    * Old HR often focused on the questions of how to increase the consistency of HR activity or how to reduce the operating costs of HR activity. New HR focuses on the subject of how to increase the value of HR activity through evaluation of business impact.

                    * Old HR is a service provider to the rest of the company. New HR is a trusted business partner.

Using data for continuous improvement

Continuous improvement is an old business topic that is more important today than it was even in the past. People analytics is a great tool for iteratively evaluating your policies and processes for continuous improvement. Looking at people's data lets, you get a high-level view of the organization and then dive down to scrutinize tiny details.

Using analytics, you can narrow the sea of opportunities into a refined focus on what are the most critical problems for you to work on right now. Because you’re prioritizing based on correlations to business outcomes data, you know that the issues you’re working on are important ones in your organization - ones that make a difference to the people you work with as well as your customers. You can tell whether your solutions are working - it’s all right there in the data.

You’ve probably encountered (or even initiated yourself!) programs, policies, and practices that no longer serve the company, or maybe that you’re not sure ever really worked in the first place. With the data behind you, you can confidently make the call to let go of those that don’t work.

People analytics provides support to your objective of differentiating your company in the market in decisions ranging from the type and quality of talent you want to hire to the way work gets done in your organization, to the employee culture you want to create, to how you pay, to what unique benefits you provide, to how you express messages to employees and potential employees and countless other decisions. Human resources professionals who can use data analysis to direct, evaluate, and modify all these human resource-related decisions to the advantage of the company in the market become more valued members of the company’s leadership team.

Accounting for people in business results

In the June 2005 article for Harvard Business Review titled “The Surprising Economics of a People Business,” Felix Barber and Rainer Strack encapsulate the growing awareness of the effect that people have on business outcomes:

To identify where and how value is being created - or squandered - people-intensive businesses need people measures that are as rigorous as financial measures, but that helps to understand the productivity of people rather than of capital. The distinct but generally unappreciated economics of people-intensive businesses call not only for different metrics but also for different management practices. For instance, because even slight changes in employee productivity have a significant impact on shareholder returns, “human resources management” is no longer a support function but a core process for line managers.

The companies I worked for early in my career were large companies that spent billions of dollars on people: large pharmaceuticals companies, large retail companies, large technology companies, and large hospitals. These large successful companies had already achieved advantages by paying careful attention to what people they hired and how they managed them, and it just made sense for them to be on the cutting edge of people analytics in their search to multiply their sizable people's advantages.

As time has gone on, I have worked with smaller companies in a more diverse range of industries that want to reduce the advantage large companies in their industry have over them or find a new edge by applying people analytics too. Today, people analytics work is not limited to a specific industry or company size; it offers opportunities that span across and within companies of nearly all types and sizes.

Competing in the New Management Frontier

Data analysis in the field of finance was once the frontier of business management, and the first companies who used it gained a distinct advantage over their competition. In turn, data analysis in the field of marketing was once a competition wrecking differentiator. Over time, though, these techniques became so widely used that their benefits no longer offer a leg up on the competition. Instead, using these kinds of analysis is just the price of entry into the game of big-time business.

Today I believe that the business world is seeing the rise of data analysis in the field of human resources as the latest example of this trend. Eventually, everyone will need to use people analytics to keep up, but for now, forward-thinking companies have a chance to use it to realize a real advantage.

Now that you’re convinced, your first task is to figure out where to get started. To do that, you need to choose a project. As you will see through the methods and examples I share in this book; people analytics can help you use data to find:

                    * Where your company’s people strengths and people weaknesses lie

                    * How to drive change with data

                    * How to prioritize and allocate scarce resources and time

Specifically, you will have to determine where to look to find the best return on investment for your effort in people analytics.

                    * Is it in applying data to the recruiting process to find ways to increase recruiter output, reduce hiring time, reduce hiring cost, or improve hiring quality?

                    * Or is the best use of your time in listening more carefully to employees to understand what obstacles are preventing them from doing their best work or using data to identify hidden undercurrents that may be a threat to the company’s future success? Is a lousy manager, missing tools and resources, arguments among employees, a lack of competitive pay and benefits, or other issues preventing the optimum productivity and resulting in a decline in employee motivation and commitment? 

                    * Or is the best use of your time to identify meaning from patterns in data about who stays and who goes? Is it in challenging the common assumptions about what makes an employee stay or leave the company, identifying what employee characteristics or conditions makes employee exit more predictable, determining what actions can management take to reduce the likelihood of its best employees leaving, or evaluating if the actions management is taking is working?

These are just a sample of questions from three broad categories of exploration where you can apply your time and resources in people analytics, among thousands of possibilities. The analysis that is most beneficial to one company may not be beneficial at all to another company. Without knowing more about your company, I cannot tell you where you should begin for you to have the biggest impact. To avoid an overcomplicated list of options (and thousand-page book), I have reduced the range of possible focus into three core people problems that all companies must solve for: employee attraction, employee activation, and employee attrition. There will be much more about the concepts, measures, and methods of analysis for these topics in the book to come. A careful review of each of the three high-level areas of focus (attraction, activation, and attrition) provided here in this book will help you determine where your attention will have the most impact for your company.

There is no shortage of opportunities for what you can learn about these topics or places your initial work may take you, so let’s get you started!

This is an excerpt from the book People Analytics for Dummies, published by Wiley, written by me.

Don't judge a book by its cover. More on People Analytics For Dummies here

I have moved the growing list of pre-publication writing samples here: Index of People Analytics for Dummies sample chapters on PeopleAnalyst.com

You will find many differences between these samples and the physical copy in the book - notably my posts lack the excellent editing, finish, and binding applied by the print publisher. If you find these samples interesting, you think the book sounds useful; please buy a copy, or two, or twenty-four.

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