CHAPTER 9
Using Employee Data to Guide Business Decisions
Talent Pipelines and Oil Refineries
An oil company was struggling to achieve production objectives due to work stoppages in its refineries caused by a series of safety incidents. No one had been injured, but each incident resulted in a delay in production. The initial reaction was to increase safety training and reinforce the importance of following safety protocol. But analysis of employee experience data indicated the problems were due to operator error resulting from fatigue. A staff shortage combined with high production targets had resulted in operators working large amounts of overtime. Plant operators wanted to hire more employees, but there was a lack of qualified job candidates. Analysis of labor market data showed a shortage of qualified candidates in the local community. Further analysis of candidate experience data revealed the pay offered by the company was not high enough to draw qualified candidates from other regions of the country. When the company used data to calculate the cost of paying higher wages to attract qualified candidates against the loss in revenue caused by work stoppages in refineries, it became apparent the best way to keep the oil flowing was to invest in building a stronger talent pipeline of qualified candidates.
Many business leaders have a limited appreciation toward the value of employee data. These leaders pay attention to salary costs, attendance, and productivity metrics that directly affect profit and loss, but they glaze over when shown data such as skills inventories, engagement levels, and talent pools. They may ask for help when turnover increases or productivity declines but show little interest in the metrics that predict why employees quit or lose their motivation. This needs to change if companies are to unlock the full value of people to drive business results. Taking advantage of employee data leads to better use of employee capabilities. It also prevents spending money on solutions that do not address actual problems. Imagine if the oil refinery in the previous story had acted on its initial intuition to invest in safety training. Not only would it have been ineffective, but the failure to address the real problem might have made employees more frustrated. They didn't need training; they needed rest and reduced overtime. It could have even led them to quit, further compounding the actual problem, which was a shortage of qualified candidates.
Employees are the most expensive, most important, and most variable factor in business operations. Employees can be extremely productive, but they can also be extremely counter productive. The difference depends on the capabilities employees bring to the job and the experiences they have at work. As people become more important to business performance, business leaders need to change their attitude toward the importance and value of employee data. This change might be compared to how business leaders embraced the importance of quality management data in the 1980s and customer service data in 1990s. There was a time when many business leaders paid little attention to quality and customer satisfaction metrics. Now, board meetings regularly include discussion of process improvement and customer satisfaction measures. It is time for business leaders to undergo a similar mindset change toward employee data.
The expanding use of work technology is generating masses of valuable employee data, but relatively little gets used to guide business decisions that have a major impact on the employee experience. This chapter explores why employee data are not used more extensively and provides guidance on how to change this by examining six challenges that often limit the use of employee data to guide business decisions.
- Understanding the value of employee data. People will not use data to make decisions if they do not think they need it. One step to increasing the use of employee data is to make leaders aware of what they do not know about their workforce and their employee experience.
- Having useful employee data. Companies often lack tools to collect detailed data about the people they employ. Advances in technology are making it easier to collect employee data, but companies must effectively leverage this technology to realize its benefits.
- Being aware of employee data. Historically employee data were often locked in files and databases not easily available to business leaders. Fortunately, advances in cloud-based mobile technology are making employee data easier to access.
- Interpreting employee data. The value of employee data comes from using it to understand, predict, and influence business-relevant outcomes. Part of making employee data meaningful is helping leaders interpret it in the right context.
- Managing employee data risks. Employee data do not always put companies or leaders in the best light. Leaders must be confident that analyzing data will not put the organization at risk.
- Linking employee data to business results. Companies employ people to drive business outcomes related to sales, service, and productivity. But data about these outcomes are not contained in the same technology systems used to manage employee data. To truly understand the impact of workforce management decisions and methods, companies need to analyze relationships between employee data and business operations data.
Employee data can fuel more effective business decisions, actions, and conversations about people, but companies need to use the data for this to happen and need to interpret the information in the right way.
Understanding the Value of Employee Data
Expenses related to employee salary, development, and benefits frequently account for 50% or more of a company's operating costs. Given how much people cost, one might expect business leaders to be obsessive about ensuring people-related decisions are as accurate as possible. But they aren't. One can find many examples of smart business leaders making seemingly stupid decisions when it comes to people.1 Why is this and how can we change it?
There are two basic ways people make decisions: based on data or based on intuition. The advantage of intuition is that it is quick, free, and easy to understand. The disadvantage of intuition is it is frequently biased and inaccurate.2 Data-based decisions tend to be more accurate and less biased than intuition, but they typically take longer, require some level of cost, and may require using mathematical concepts that are harder to understand. If we want business leaders to invest the time and resources needed to make data-based employee decisions, we must make them aware of what they are losing by over-relying on intuition and underusing employee data, in other words, making leaders aware of how bad their intuitions can be when making decisions about people. Here are just a few examples that show how business leaders' decisions and assumptions about people are often flawed:
- Staffing decisions. The decision to hire someone is the most important decision companies make about employees. Every other decision or action is a consequence of this initial decision. Yet intuitive hiring and promotion decisions are influenced by characteristics that have nothing to do with job qualifications, including qualities as trivial as an employee's height.3
- Job performance. People often think high-performing employees are like average-performing employees but just more effective. From a mathematical perspective, this can be described as viewing performance as a linear variable. But in many jobs, performance is distributed in a nonlinear, exponential fashion where the highest performing employees may contribute 10 times or more than their peers.4 Problems arise when leaders assume high-performing employees are just a better version of solid-performing employees. At best, leaders fail to recognize and use the capabilities these individuals bring to the organization. At worst, high performers become frustrated at being constrained by management methods that fail to appreciate their value and they quit.
- Employee experience. Leaders' view of what it is like to work in the organization can be very different from those of other employees. Leaders' roles are relatively unique compared to other employees. They receive resources other employees do not get and are treated differently by coworkers and customers because of their status and position. Leaders also tend to be motivated by different facets than many employees. As a result, leaders are often wrong when they try to guess what employees are thinking or feeling based on their personal perceptions and intuition.5
- Organizational decisions. Many leaders incorrectly assume organizational decisions that make sense from a financial perspective will work out from a people perspective. For example, most acquisitions and restructurings fail to deliver expected financial results, and one of the main reasons they fail is employees affected by the change did not act the way leaders intuitively assumed they would.6 This is less likely to happen if leaders consider employee data as well as financial data when making organizational decisions.7
The more leaders are aware of the dangers of relying solely on intuition to make people decisions, the more open they will be to supporting use of employee data. The role of employee data is not to replace leadership intuition but to augment it. Leaders did not become leaders by making bad decisions. Leaders' intuition about people, although often flawed, can also be highly accurate and insightful. The goal is to find the ideal balance between intuition and data.
Having Useful Employee Data
There is an old joke told in statistics classes about a man standing under a streetlight at night looking for his car keys. A passerby asks, “Where did you drop them?” and he responds, “Over there in the alley.” The passerby then asks, “Then why are you looking for them here?” to which the man responds, “Because it is too dark to see in the alley so I'm looking where the light is.” The point of this joke is that our ability to solve problems using statistics is limited by the data we have available. This can be a major challenge when it comes to analyzing employee data. Companies often fail to collect meaningful data about employees, and when information is collected it is often stored in inconsistent and inaccessible databases.
Table 9.1 summarizes different types of employee data, why it can be difficult to collect, and technology solutions that can help provide this data. Two particular advances in work in technology are making it much easier to collect employee data. Electronic profile solutions are enabling companies to collect and consolidate a range of personal data about employees in one, easy-to-access system that can be managed to ensure data confidentiality and privacy. Interoperable data management solutions are enabling companies to integrate and analyze data from across a range of technology systems including business operations solutions. As will be discussed later in this chapter, having this ability to link employee data with business data can transform how organizations design and manage their workforce. Another area enhancing the availability of employee data is the growing use of experience measurement and relationship analysis solutions. This is generating data about how candidate and employee attitudes and relationships impact hiring, performance, engagement, retention, and development. Looking forward, we can also expect to see increasing use of wearable and biometric data to understand interactions among employee capabilities, work schedules, job locations, attitudes, well-being, and performance.
TABLE 9.1 Using Technology to Collect Employee Data
| Data Category | Common Data Collection Challenges | Technology Solutions That Can Help Provide This Data |
|---|---|---|
| Workforce Characteristics: data describing workforce characteristics such as job codes, salary and benefits, tenure, demographic characteristics (age, gender), work location, and reporting structures | Data are either never collected or are stored on hard-to-access files and spreadsheets using different databases and structures | Workforce design, compensation, payroll and benefits management, work scheduling, contractor management, certification management, facilities and system access, electronic profiles, role and goal management, interoperable data management |
| Employee interests and capabilities: data describing individual employee attributes such as skills, qualifications, works styles, and career interests | If collected at all, these data are often stored using unstructured forms such as résumés or placed in difficult-to-access files and stand-alone recruiting and career development systems | Electronic profiles, certification management, recruiting management, talent assessments, learning management |
| Employee experience and attitude: data measuring aspects of job satisfaction, engagement, inclusion, and confidence | Data are collected through time-intensive annual or periodic surveys and often stored on stand-alone databases | Experience measurement, electronic profiles |
| Subjective performance: data such as ratings of performance or potential, peer ratings and other data about how employees are perceived by people they work with | Data are primarily collected through individual manager performance reviews and tended to be of questionable accuracy; peer feedback data tends not to be captured at all | Talent management, talent assessments, role and goal management |
| Objective performance: data such as attendance, productivity, sales, customer service, and other performance metrics that do not depend on ratings or subjective evaluations | Data are stored in multiple systems and databases that are difficult to access, lack standardization, or are not easily linked to other types of employee data | Role and goal management, interoperable data platforms |
| Work environment: data such as job schedules, work relationships, team composition, and work locations that reflect how, when, and where a person is working and whom they are working with | Data are not collected at all or are stored in multiple systems and databases that were difficult to access and lack data standardization | Workforce design, work scheduling, work experience platforms, team management, electronic communication, collaboration spaces, role and goal management, facilities and system access, relationship analysis, wearable and biometrics, interoperable data platforms |
| Financial outcome: business unit revenue, operating costs, and revenue per employee | Financial and business operations data are stored in systems that are neither linked nor aligned to employee data | Interoperable data platforms |
As the use of technology grows, the amount of employee data available will only increase. We have already reached a point where the challenge is frequently not about getting data but learning how to manage and use it. The more companies leverage employee data the more business leaders will come to appreciate its value, provided the data are used to address meaningful questions. Revisiting the joke from the start of this section, we are reaching a point when we can shine the data “streetlight” wherever we want, but now we have to know where to look.
Being Aware of Employee Data
People must be able to see data if they are to use it to guide decisions. Historically, employee data were often kept in reports, databases, and systems that were not easily accessible to business leaders. Many leaders did not know the data existed, or if they did, they had to ask someone to run a report that might take several days to return. When the data finally arrived, it might not be presented in an easily interpretable format and may already be out of date. Advances in technology are increasing accessibility and interpretability of employee data, providing functions that pull data from complex reports and present it on dashboards using interactive, easy-to-interpret tables, graphs, and charts. The following are examples of how this technology is increasing visibility and use of employee data.
Displaying Employee Data When Leaders Are Making Decisions
The best time to show leaders employee data is when they are making decisions relevant to that data. Workforce decisions can be broken into two categories: tactical decisions guiding day-to-day operations and strategic decisions guiding the direction of the group or company overall. Technology enables use of data for tactical decisions by embedding data analytics and reports into the systems used to make these decisions. For example, addressing implicit bias is a challenge to making equitable compensation decisions.8 Unconscious biases lead people to base decisions on employee characteristics such as gender that are not relevant to actual job performance. One way to reduce bias is to embed data about employee performance into tools used for compensation decisions. This focuses leaders on criteria relevant to the decision and decreases the influence of implicit biases. Another example is showing compensation data in a way that highlights potential bias for one group over another, for example, detecting and visibly displaying current and future gender pay gaps to managers that might reflect biased compensation decisions. Making this information visible in the moment when leaders are making decisions helps improve decision quality.
Technology is improving the use of data for strategic workforce decisions through the creation of integrated executive dashboards. For example, one company created an interactive touch screen in their corporate boardroom that displays employee data alongside finance, operations, sales, and marketing data. The data can be organized by business functions, geographic regions, cost centers, or leadership reporting structures. Leaders can drill down to look at data based on specific regions, functions, or departments. Having employee data displayed alongside operational data provides a comprehensive picture of the organization. It also changes the nature of leadership meetings. Historically the agenda for executive-level meetings was often split based on function. Finance would present their data, followed by sales, followed by HR, and so forth. Consolidating all this data into one interactive display supports moving from talking about each function in isolation and toward talking about how the functions can collaborate to address broader business issues. For example, instead of HR leaders talking about turnover, finance leaders talking about revenue, and sales leaders talking about account opportunities, all three leaders can jointly discuss the impact of employee turnover on sales account opportunities and its subsequent impact on revenue. Having the data in one system also accelerates leadership decision-making. When different functions bring their own data reports, inevitably certain data will not align, leading to discussions over whose data are most accurate. Having the data in one system helps reconcile data irregularities so all leaders operate from a single source of truth.
Making Employee Data Readily Accessible When Leaders Are Interested in It
Business leaders who have little inherent interest in employee data can become very interested if the data affect the business operations they oversee. The challenge is how to get employee data in front of leaders at the precise time when it is relevant to their interests. Technology is helping address this challenge as illustrated by a story shared by an HR leader. Shortly after the company implemented a mobile-enabled workforce analytics solution, a sales leader complained about missing revenue targets because salespeople were quitting. When the sales leader commented, “I just lost two reps this quarter. We clearly have a turnover problem,” the HR leader showed him turnover data for his region using a reporting tool accessible on their smartphone. After a few minutes looking at the data, the sales leader realized that turnover in their region had actually decreased over the past year and the two reps leaving was an anomaly. The ability to present employee data in real time during the conversation quickly redirected the sales leader's thinking. Imagine how this might have played out if the HR leader had said, “Let me file a request to get a report with turnover data in your region and we can schedule a meeting in two weeks to review it” or even worse if they had no way to access turnover data at all.
Calling Attention to Changes in Employee Data That Affect Leadership Actions
Employee data are useful for calling attention to critical issues such as sudden increases in turnover, shortages of critical staff, or rapid drops in employee engagement. In the past, HR departments would analyze employee data to spot changes and then schedule meetings with leaders to discuss the data. This method is time-consuming, can lead to long presentations going over lots of different data fields, and increases the risk of failing to address changes before they affect business performance. Technology can be used to keep business leaders informed of relevant changes in employee data without overwhelming them with information. These solutions can set triggers that automatically inform managers if employee data reach certain thresholds. For example, a global airline is required to have two certified safety experts on-site in every location, so they built a system that automatically informs regional leaders if there is a risk of falling below that level.
To be useful, business leaders must be able to access employee data at the right time without being overwhelmed by it. Technology can help display the right data at the right time to the right leader. As a result, increasingly the challenge is not about accessing and sharing employee data, it is educating leaders on how to use it.
Interpreting Employee Data
An expert in employee data analytics once told me that limited data in the wrong context are worse than no data at all because it gives people confidence to make bad decisions. This is a risk when sharing employee data with business leaders. Consider the following story. A division of a large company had grown significantly through acquisition but was not achieving its revenue goals. The ratio of workforce costs to revenue was well below the company's profit targets. Company leaders needed to streamline the workforce, so they collected data showing people's job titles, functions, and salaries. Based on this data, the leaders felt they could reduce costs by laying off people in several roles that appeared to be noncritical to business operations. After the layoffs were communicated, the sales team informed these leaders they had fired several people with specialized knowledge critical to winning deals. In an effort to increase profitability the company fired people who were central to driving profit. The decision to get rid of these people made sense based on data the leaders had about job titles and salaries. But what the leaders did not have were data showing how these people actually affected sales performance.
It might look like the leaders in this story made a stupid decision, but these leaders were not stupid people. They were smart people who made a confident decision based on accurate data interpreted the wrong way. What they lacked were the data needed to fully understand the context of their decision and its impact, both positive and negative. A critical part of using employee data is presenting it in a way that leads people to draw appropriate insights and conclusions. This is about providing data in the right context coupled with effective analytical interpretation. To illustrate this concept, consider the following five stories that demonstrate going from ineffective to effective use of employee data.
Interpreting Employee Data Without Any Business Context
A management consulting firm convinced the CEO of a large company that the best way to limit bureaucracy was to ensure every manager had at least five direct reports. The goal was to create a flatter, more agile organization. Department leaders were held accountable for creating organizational structures that met this rule regardless of the nature of their teams. This created problems for leaders who had given high-potential employees teams of two or three people so they could develop managerial skills before assuming roles with larger spans of control. The company looked at reporting structure data and forced the leaders to comply with the “five or more direct reports” rule without considering why they had smaller teams. This lowered the morale of the groups, hindered development of future leaders, and increased turnover of high-potential employees.
Interpreting Employee Data Without Enough Business Context
Hiring freezes are a common method used to control costs. Stopping hiring halts growth in operating costs caused by salary. What leaders implementing hiring freezes do not see is the financial losses they create. It starts with wasting the time spent recruiting skilled candidates, only to tell them they cannot be hired. The best candidates usually have multiple offers, so when a company implements a hiring freeze it is giving many of its top candidates to other companies. In many cases these are direct competitors. Employees are also hired to generate revenue and run efficient operations. When a hiring freeze goes into effect, the financial gains achieved by hiring employees is delayed and potentially lost forever. The problem with hiring freezes is not that they never make sense. The problem is they are often implemented based solely on data showing the costs saved by eliminating new hires, without considering data showing the financial gains that new hires generate. If the logic of using hiring freezes to improve financial performance was totally sound, then companies could become infinitely profitable by getting rid of their entire workforce.
Interpreting Employee Data with the Right Amount of Context
A major challenge for business operations is determining the optimal number of employees to hire to maximize profit and growth without generating excessive workforce costs. This is particularly important in low-margin industries where small differences in operating costs can make the difference between profit and loss. One retail organization made creative use of employee data to determine the optimal number of store managers to hire in a region. Historically the company strove to keep costs low by only hiring a new store manager when an existing store manager left. An HR leader observed that when a store manager left the engagement of employees in the store suffered, turnover increased, and sales declined. Pressure was placed on adjacent store managers to cover the store until a replacement manager was hired, and consequently the performance of adjacent stores suffered as well. He used store sales and turnover data to demonstrate that the cost incurred by waiting to hire store managers until after an existing manager quit was greater than the cost of employing an extra “floating” store manager in each region who could immediately step in and run a store when a manager left. This story illustrates the benefits of looking beyond simple employee data such as staffing and salary and incorporating contextual data that illustrate how people affect business operations.
Interpreting Employee Data with the Right Amount of Context Plus Analytical Insights
Some employee data need no explanation. Business leaders do not need guidance to understand the impact of results such as “high performers are quitting at twice the rate of low performers.” As one statistician told me, the best data have interocular significance—its meaning hits you right between the eyes. But not all employee data provide such easy interpretation. This is why there has been rapid growth in the use of machine learning and other nonlinear mathematical modeling solutions to draw insights from employee data that might otherwise be overlooked.9 Analytical methods such as machine learning do not magically turn employee data into meaningful information. The data must meet certain conditions related to data quality and sample size. The information must also be tied to business metrics and presented in the right way. Advanced analytical methods can greatly enhance the value of employee data when these conditions exist. For example, a company wanted to develop staffing assessments to predict sales performance. The company had observed that better salespeople tend to be socially confident (e.g., they initiate conversations) and personable (e.g., they are interested in learning about others). Based on this, the company favored hiring candidates who were socially confident and personable. The company decided to test this assumption using nonlinear mathematical models to determine the relationship between candidate characteristics and sales based on several thousand employees. The analysis confirmed that good salespeople do tend to be socially confident and personable. However, the best salespeople were socially confident but not highly personable. These individuals sold the most because they were task focused. They were not starting conversations with customers just to talk; they were starting conversations to sell products. This insight made total sense to the sales managers once it was called out. But without advanced analytics the company would not have made this realization and modified its hiring profile.
Interpreting Employee Data with the Right Amount of Context Plus Analytical Insights Combined with Expert Interpretation
The highest level of data interpretation combines multiple sources of data, mathematical modeling methods, and the skills, intuition, and knowledge of subject matter experts. Analytical techniques such as machine learning can identify patterns in data that people could never see. However, people can make inferences based on data that no machine learning algorithm could ever consider. This point was made in an analysis of turnover data for a chain of hotels. The turnover in one hotel suddenly increased over a very short amount of time. A range of employee data was examined to explore whether something had changed in terms of how employees were being hired and managed. The analysis failed to provide any clear insights. At this point, the data experts working on the project asked to talk with the hotel staff. One of the hotel managers commented that the local bus company had changed its routes. A bus that used to stop in front of the hotel now stopped a half mile down the street in front of another competing hotel. The turnover had nothing to do with what the hotel was doing. It was a result of an external change in the commuting time of hotel employees who relied on public transportation to get to work. No amount of advanced data analysis would have led to a simple insight that was immediately obvious to any person familiar with the actual hotel and its employees.
Employee data can provide valuable insights that improve business operations, but data can also mislead leaders into making bad decisions. The key lies not in the data itself, but in ensuring the data are interpreted in the right way at the right time in the right context and remembering that the most insightful interpretations of data tend to combine advanced mathematical analysis with old-fashioned human knowledge and experience.
Managing Employee Data Risks
Employee data often contain sensitive information about employees and organizations. For example, employee data may suggest that a company has a history of inequitable treatment toward certain demographic groups. It may highlight things about employee performance and compensation that can trigger emotional responses and difficult conversations. Or it may cast doubt on the leadership skills of company executives by surfacing issues related to employee trust. Part of using employee data is managing risks related to its sensitive nature. These risks tend to fall into three categories.
- Data privacy. Employee data contains information that could be misused if it fell into the wrong hands. Many countries have strict requirements about how and where employee data must be stored, who can access it, and when it must be destroyed. Anyone working with employee data should pay careful attention lest they violate regulations that can result in significant financial penalties and undermine the trust of employees.
- Cultural concerns. Increasing the transparency of employee data may reveal organizational characteristics that could create difficult conversations within the company. Foremost is the potential to uncover inequitable trends related to compensation and staffing. Leaders must be educated on how to appropriately respond to data that may suggest unfair employee treatment.
- Legal exposure. Employee data may surface patterns that could put the company at legal risk such as evidence of potential discrimination based on gender, age, or ethnicity. When dealing with such data, it is wise to consult corporate counsel and take precautions that minimize the potential of becoming a target of legal actions.
The risks associated with employee data are real, but they are also manageable. This starts by educating the people handling employee data on how to properly protect its confidentiality and security. It continues by controlling who sees employee data and ensuring the information is used only for appropriate purposes. Finally, it is important to educate leaders in the company on how to effectively discuss patterns in the data so they lead to constructive change and avoid destructive criticism. Some companies are so afraid of the risks inherent in employee data that they only use it for minimally required reporting. This fear of exposing sensitive data serves to perpetuate inappropriate practices rather than identifying and addressing them. When it comes to employee data, companies have two choices. One is to hide data in hope that any inconvenient truths will never come to light. The number of corporate scandals occurring in the media suggest this strategy is ultimately bound to fail. The other is to use employee data to better understand the world as it is, problems and all, and leverage the data to develop methods to improve the world for the better. This approach requires more active risk management upfront but leads to more positive outcomes over the long term.
Linking Employee Data to Business Results
The primary reason a company employs people is to deliver business results, but relatively few companies seriously analyze the link between workforce management practices, operating costs, and business outcomes. Even large companies with considerable resources may not be able to determine the financial value associated with investments used to recruit, hire, develop, manage, and pay employees. Consider this story: a compensation director of a large organization told me their company spent over $500 million annually on merit increases and bonuses. When I asked what the return on investment was on those millions of dollars, he replied, “To be honest, the only thing we know for sure is employees don't quit too often and don't complain too much.” Imagine how a Chief Financial Officer would react if someone proposed spending millions of dollars per year on a project in which the criteria for success was “People won't quit or complain.” But when it comes to investing money in people, that is what companies do every year.
The reason companies rarely look at the impact HR methods have on business results such as market growth, profitability, or customer satisfaction is largely because business results data are not stored in the same systems used to collect and manage employee data. Figure 9.1 illustrates the state of most companies' employee data and business data. Determining the business value of HR methods requires linking employee data (the left of the figure) to operational data (the right of the figure). The business operations data that leaders care the most about, such as financial performance, operational efficiency, and customer service metrics, are not stored in HR and work technology solutions; they are stored in business operations solutions. Furthermore, business data are often stored using data structures that are different from those used to organize employee data. As a result, it can be challenging to link these different types of data together. The primary problem of linking HR to business operations is not about having data. It is about being able to link the data companies already have. This is why the development of interoperable data management technology is so valuable. It enables companies to link employee data that describe a company's workforce with business operations data that show how the workforce is affecting profit, growth, and other key business outcomes.
FIGURE 9.1 Relationship of employee data to business operations data.
Employees must be engaged and not quit if they are to provide value to a company, but companies do not pay employees to be engaged and not quit. The value of a company is not measured based on employee staffing, engagement, and retention levels. Companies pay people because the contributions employees make to the organization's value outweigh the cost of employing them. These contributions typically involve improving profit, growth, efficiency, brand image, and customer satisfaction. These factors are not measured by employee data. The only way to truly measure the business value of employees is to link data about employees to the data that is used to measure company value.