Today, April 2, 2019 is Equal Pay Day in the United States. Last year, it was on April 10. In 2017, it was April 4. Why does the date change each year? Well, Equal Pay Day is held every year on the day that women have to work until they earn the income that men earned in the prior calendar year. In other words, women had to work from January 1, 2018 until April 2, 2019 (today) to earn what men earned in the calendar year of 2018.
When Equal Pay Day moves to earlier in the year, it means the gender wage narrowed in the past year, and when it moves to later in the year it means the gap widened. Since we’re all responsible for nudging this day closer and closer to January 1, here’s a quick primer on pay equity and new Pay Equity metrics included in Namely’s HCM Benchmarking Package.
Wage gap metrics in HR come in two forms: unadjusted and adjusted. An unadjusted gender wage gap is the difference between median male pay and median female pay, without accounting for any legitimate pay-related factors. A median salary is the “middle-most salary” if you lined all salaries up from lowest to highest. To get your pay gap, you do that for men and then again for women, and the difference between the two medians is your pay gap. This unadjusted gap answers the question: what is the raw difference between what men and women are earning, regardless of whether or not they are performing equal work?
In contrast, the adjusted pay gap commonly considers factors like performance, job level, experience, etc. to identify if comparable employees (in terms of type and level of work performed) are paid similarly or not. For example, you might identify the pay gap separately for each level of your job tier system. The adjusted pay gap gives you a sense of whether you’re compensating fairly for comparable work.
These metrics are most useful when considered together. Do you have a large unadjusted gap that narrows or disappears when you adjust for job level? This likely means you lack balanced gender representation at different levels in the organization, so you might want to check that your promotion or executive hiring practices bring employees across both genders into leadership.
Do you have both adjusted and unadjusted wage gaps that concern you? Start by investigating which employees in each group are outliers (even within their own group), and consider plans and timelines to help bring them more in line with their own group and the broader company. Step by step, and alongside improved and standardized salary practices at the point of hire, you should see your wage gap begin to narrow.
Organizations with mature compensation practices will often conduct more robust statistical testing on their wage gaps to identify which gaps are “statistically-significant,” and thus need to be prioritized. If you’ve already begun basic pay equity analysis and are looking to take the next step, there are many legal and organizational professionals who can assist you in conducting more advanced analyses.
If you’re one of the many organizations that are just getting started, consider looking at Namely’s HCM Benchmarking Package, which includes a board-ready quarterly report that covers headcount, turnover, diversity, retention, and now pay equity metrics. The Pay Equity chapter of the report provides both unadjusted and adjusted pay gaps in your organization, as well as views of the pay gap by department and tenure. Better yet, the report covers not only gender gaps but diversity gaps as well, looking at the same set of metrics across majority ethnicity employees versus non-majority ethnicity employees. Better yet, our industry benchmarks help you see exactly where you stand relative to peer companies.
Please join Namely on Equal Pay Day 2019 to take the first step towards equal pay in your company!
Namely recently launched our Benchmarking Package to clients, an offering which provides quarterly reports of company-specific insights with tailored benchmark data layered directly on top. Never before has it been so easy for mid-sized companies to understand the health of their talent compared to other companies just like them.
As part of the offering, we leveraged data science and machine learning to make it easier than ever before to compare the specific reasons employees are leaving one company relative to its peers. In light of this exciting news, I thought it would be fun to introduce all of my fellow data nerds out there to the process behind developing, testing, and launching Namely’s benchmarks.
What’s Behind a Namely Benchmark?
When we started this benchmarking work in 2018, we knew that Namely was in a unique position to explore and provide aggregated insights back to our clients and the broader HR community. While most providers of HR benchmarks collect their data through surveys sent to a variety of organizations, Namely’s historical data is a direct link to the realities of thousands of mid-sized organizations and hundreds of thousands of employees, no intermediary necessary. As a result, our benchmarks are not subject to biases resulting from incomplete benchmark survey responses or poor response rates. In other words, we can provide a more comprehensive view of the workforce at any point in time without repeating large scale surveys. This means we can produce a greater variety of benchmarks with greater speed—and continually adapt to meet the needs of the benchmarking clients we work with.
Finally, in an effort to provide never-before-seen benchmarks on the exact reasons employees leave their companies (there are thousands of ways companies label employees terminations), we’ve leveraged data science and machine learning to cluster and classify thousands of departure reasons into a standard set of 32 reasons that can be benchmarked across all companies. All of this preparation led to the launch of the Namely Benchmarking Package and a new era of Namely providing HR insights. Once we decided to research and generate HCM benchmarks, we knew there would be several key decision points along the way that ensure our benchmarks are of a high-quality and great relevance to HR professionals. Three decision points I’ll share with you today include:
How do we determine the minimum number of companies that would form a valid benchmark?
How do we determine the minimum number of employees that would form a valid benchmark?
Should we report the mean or the median of a number (e.g., employee turnover)?
1) Minimum Number of Companies
As part of our research and development process, we reached out to our partner Culture Amp to discuss their process for developing engagement benchmarks. They had used a statistical process called bootstrapping (repeatedly calculating the same metric, like turnover rate, based on a random sampling of different numbers of companies to observe how spread out the metric in each of these ‘samples’ is) to determine that their engagement benchmarks stabilized once about 20 companies were included. We adopted a similar approach to developing our benchmark and found that our results mirrored theirs! Specifically, our turnover benchmarks stabilized when we sampled a minimum of 20 companies.
2) Minimum Number of Employees
Having determined the appropriate minimum number of companies, we turned next to a number of employees. In fact, we took a very similar approach to identify the optimal employee minimum in Namely benchmarks. Here’s a slightly different visual showing that benchmark numbers stabilize dramatically during the first 5,000 employees, and continue very gradually until 10,000 employees, after which the progress is negligible.
The field of people analytics is constantly exploding with innovation: Wegman's reinvented the annual benefits survey, Google taught us what good managers look like, and many other companies have conducted and written about their internal analytics work. But have you taken a moment to think about how you can benefit from your own people data?
After reading the barrage of exciting developments in analytics within HR, it's easy to downplay our own company’s opportunity within the space. In fact, as you read about the analytics work of others, you may find yourself assuming that companies who are "doing People Analytics" are all doing it the way we read (or think) about it through transformative project work that alters the course of the organization, or with a perfectly clean set of data. This can be one of the more misleading assumptions in the modern HR function.
In a 2018 report on talent trends by LinkedIn, 42 percent of respondents cited data quality as an ongoing obstacle to their talent data work. However, this fact doesn't stop many of these organizations from actively working to improve their data quality and driving their people analytics efforts forward, and it shouldn't stop you. The majority of unsung People Analytics work gets done with less-than-perfect data, or with very targeted data cleaning (e.g., if you’re going to analyze turnover data, you clean up that data to prepare for analysis). By ignoring these subtle but promising realities of People Analytics, you may be holding back your team's potential to dig in and answer important organizational questions.
Adopting an internal strategy for People Analytics marks a key milestone in the evolution of HR. Bersin by Deloitte recently found that 56 percent of HR functions are still in a phase of reactive and operational reporting. Applying the blanket assumption that your organization is "not ready" for people analytics is an easy resignation to remaining at a lower level of HR maturity.
Learning what you can do well today and what you can't yet tackle is the first step to embracing a new data-focused direction for your HR team. Feel like this is easier said than done? Start with the following three tips.
Just because many organizations work around imperfect data doesn't mean we should aspire to do so. Get to know your data well, teach others what they can to improve quality, and map data availability versus readiness. Familiarize yourself with the limitations of your data, and take on projects that are not heavily affected by those limitations.
2. Start Simple
Take a look at these three quick metrics to help you get started. Each has been incorporated into Namely's own People Analytics strategy without the use of any special software. All three metrics use data that can be collected/calculated using any standard HRIS and/or free survey tools.
You're more likely to garner support and resources for your People data work if you tackle problems that matter to your internal stakeholders (e.g., pay equity, attrition driver analysis, etc.). Talk to your stakeholders and find out what they care about, define a specific research question, then take the leap.
Keeping up with the goings on of People Analytics is fantastic for knowledge sharing and professional growth, but don't paint other organizations with a broad brush of analytics potential and exclude your own from the bunch. It’s not always as complicated as you might think, and the resulting insights you can glean from HR analytics software can have a huge impact on your organization.
How do you currently measure employee diversity in your organization? Like many others, your business probably measures the breakdown of gender, ethnicity, and other employee demographics. Such breakdowns (e.g., 45 percent female / 55 percent male) are very common for organizations to monitor and action as “outcomes” of diversity initiatives (e.g., “Did the introduction of a structured interview process increase diversity in our workplace?”). But how do we know if our diversity efforts have succeeded?
Within Namely’s 2018 Workforce Diversity Report, one key table refers to a metric called Simpson’s Diversity Index (SDI), a metric that offers organizations a more robust way to “quantify” diversity. Simply put, this index distills the measurement of diversity into a single, trackable metric. Here’s how you can get started measuring it today.
What is Simpson’s Diversity Index?
Simpson’s Diversity Index (SDI) originated as a tool for measuring the diversity of species in an ecosystem––in our case, we’ll use it to measure employees in an organization. The metric was designed to capture two critical elements of diversity: richness and evenness. Richness refers to the number of different groups represented (e.g., how many ethnicities are present), while evenness refers to the spread across those groups (e.g., whether employees are spread evenly).
Examples of the Simpson Index for Sample Group Breakdowns:
The philosophy behind the Simpson Index is that both of these criteria matter. For example, you are probably not a diverse community if only two groups are represented compared with ten groups (i.e., low in richness), and if you have 90 members in one group and one member in each of ten other groups (i.e., low in evenness). The index incorporates both of these criteria in a single, clean snapshot of diversity. The metric ranges in score from zero to one, where zero represents a complete lack of diversity, and one representing (get ready for it…) infinite diversity!
Why use Simpson’s Diversity Index?
Although infinite diversity would be fantastic, in organizations we typically work with demographics that have a limited number of groups, such as ethnicity. However, this metric is still useful to HR professionals and their stakeholders because it captures the essence of common diversity measures in a single, reportable number.
It’s also readily explained to those who want more detail on what the metric actually represents: the probability that two randomly-selected employees are from different groups. By monitoring this metric as a supplement to traditional breakdowns, you can now more objectively determine if shifts in representation across your company could be considered increases or decreases in diversity.
How do you calculate Simpson’s Diversity Index?
The formula for Simpson's Diversity Index is:
D = 1 - ( ( Σ n(n-1) ) / ( N (N-1) ) )
- n = number of individuals of each ethnicity
- N = total number of individuals of all ethnicities
- The value of D ranges between 0 and 1
To make calculating this metric even easier for you, download this free spreadsheet or use our Diversity Index Calculator to simply enter employee counts for each group of the demographic you’re interested in studying. You can’t change what you don’t measure, so get started now!
Calculating Simpson's Diversity Index for your organization can help you gauge just how diverse your organization is, but it won't help you understand how you compare to your competitors and peers. For that added context, you might want to consider using Namely's quarterly benchmarking reports to see just how your business stacks up. In addition to comparing your SDI score to Namely's database of over 1,300 companies, Namely benchmarking reports gives you an in-depth look at your company's diversity, pay equity, and more. Learn more about Namely benchmarking reports here.
Quality of hire has long been considered the “holy grail” metric for recruiters. Though the methods used to build the metric typically vary by organization (and rightfully so, given how unique organizations are), the objective remains the same: understand how well the hiring process is working. Historically, quality of hire has largely been the responsibility of talent acquisition teams—but it’s time for all members of the HR team to reap the benefits of this rich metric.
Here are three steps to help your team maximize insights from the Quality of Hire metric:
1. Define What Quality of Hire Means in Your Organization
To do this, we first need to expand our ideas about what the quality of hire measurement is. It’s a metric, sure, but when you drill down, it’s easy to see that quality of hire is actually a collection of data points (and for good reason). There is more than one thing that makes for a “high quality” new hire. To calculate quality of hire, many companies score employees on criteria such as company values, job competencies, engagement levels, and turnover rates, and then they combine these into a singular summary metric. The end result is often reported to leadership as an indicator of success in recruiting and employee selection.
2. Break Down Data Points to Derive Insights
While the singular metric is useful for reporting, each data point offers its own actionable insights for the entire HR team. For example, new hires might be rated on job-specific competencies that were evaluated during the recruiting and interview process. With two time points of data (interview and on-the-job) across numerous candidates, a picture begins to form about which competencies an organization is consistently predicting well—e.g., candidates scoring high on Communication in interviews also tend to score high on the job. You can also identify competencies that hiring managers are not predicting so well—e.g., high-scoring job candidates who to score lower, or less predictably once on the job. With this knowledge, an organization can revisit the way it interviews for poorly-predicted competencies, and inspect what it might be doing right when interviewing for the well-predicted competencies.
3. Use Data to Inform Internal Programs
The above example is yet another way quality of hire is helpful for talent acquisition professions. So who else in HR might want to know about early proficiency in competencies? As resident training experts, your Learning and Development (L&D) team has much to gain from the quality of hire assessment as well. The same new hire competency scores that allow organizations to improve their interview effectiveness can help pave the way for new training offerings. Which competencies are new hires consistently scoring low in? These scores could reflect skill or competency gaps that can be addressed through new learning insights or onboarding plans.
Useful applications don’t stop there: each piece of an organization’s quality of hire metric (e.g., values, competencies, engagement, turnover, etc.) has potential relevance and impact for a different part of the broader HR team, from recruiting to performance management. Often, these basic insights don’t even require advanced data analytics. All your team needs to glean actionable insights is reliable and clean data. With a little digging, the right quality of hire metric can help HR teams establish a cycle of continuous improvement that is bound to benefit the entire organization.
Over the last few years, the differing experiences of men and women in the workplace have been subject to both media attention and national legislation. As the workplace evolves, employers continue to face opportunities to ensure that they’re building a happy and healthy workplace for their employees. While it may seem obvious to say “consider gender in your workplace!”, sometimes it’s the most basic needs of employees that are overlooked.
To help you on your journey, here are three tips on monitoring the workplace and building a better employee experience, regardless of gender:
Write Job Ads that Speak to Everyone
There is a wealth of information online about how to market open positions. Pay attention to how certain job titles and role descriptions may attract specific genders. For example, research suggests that making job titles gender-neutral (e.g., changing policeman to police officer) can be somewhat effective in reducing disparities in your applicant pool.
Job descriptions frequently include gender-skewed wording. Words like competitive, strong, and assertive can implicitly make a job posting appear more attractive to a male than a female. The reverse can be said for words like nurture, thoughtful, and understanding. When writing job ads, be aware of these words and ensure that the language you use is truly representative of the characteristics required for the job.
Provide Equal Benefits
Another consideration is whether men and women are offered similar flexibility and resources to maintain a personal, professional balance. This is critical, as discovered in a recent study conducted by myself and a cohort of researchers across four universities. We found strong empirical evidence that men and women report equal levels of work-family conflict (and family-work conflict). These findings contradict conventional wisdom, which suggests a disparity in men and women’s work-life balance.
This data highlights how critical it is for workplaces to ensure they’re offering equal levels of flexibility to both men and women. As many state and local jurisdictions do not extend paid leave benefits to both mothers and fathers, employers need to make independent decisions about parental leave and the distribution of that leave by gender. Ensure that you’re considering the needs and experiences of both men and women during these decisions.
Audit Your Data to Ensure Equal Opportunity
Finally, employers should be monitoring data such as hiring selection rates, compensation, and promotions to ensure that their workplace is fair, just, and compliant. Organizations like the Society for Human Resources Management (SHRM) provide resources and toolkits to assist employers in ensuring that none of their practices adversely impact protected characteristics like gender. Leveraging these resources while consulting with internal legal counsel can ensure your company is on the right track to gender equality.
Taking steps to be mindful of the experience of both male and female employees contributes to a better and more human workplace. Don’t forget, building a great place to work is a journey, and you should regularly check-in on each of these areas to ensure employees feel like they’re working in a supportive and inclusive environment.
For years, many companies have relied largely on intuition and 6 important HR metrics when it comes to people analytics. But in the face of rapidly-evolving ideas, methods, and tools in this space, many people have been challenged to consider their trust in data and the science that it is—ideally—based on. Even when these new data-gathering technologies have been relentlessly tested and their insights supported, HR data can still be met with some degree of skepticism.
These challenges in trust are becoming increasingly familiar to analysts and researchers who are looking to leverage data in more rigorous and scalable ways. This skepticism isn’t new—nor is it unique to people analytics. Past research has found that people lose trust in data and algorithms far more quickly than they lose trust in humans when both make the same error. Instead, humans tend to rely largely on their own intuition to maintain a sense of “control” over the outcome. However, the pace of growth in data and analytics could make it difficult for those who don’t get on board now to catch up later.
With an increase in data and analytics capability, it is more essential than ever before for HR professionals to begin making the case for data-driven HR. In the face of skepticism about data, the first major step to building a successful people analytics function is ensuring that your stakeholders are prepared to trust your data and methods. Here are three rules to help you gain and maintain that trust:
1. Maintain Great Data Hygiene
No HR data set is perfect. Whether you studied HR/analytics in school or fell into the field, we all start out a bit naive about the cleanliness of our HR data sets. Some of these challenges are the result of system limitations; some are the result of human error. Often, data is messy simply because the organization was not leveraging its data before and never had a true need for superb data quality. Whatever the reason, a lot can be done to improve the quality of your HR data.
First, take a look back and audit the cleanliness of your HR data. Are all the dates reasonable (e.g., no one born in 1899)? Are there any mistakes in the spelling of your departments, termination reasons, or other group names? As for your numeric data points, are the numbers all possible?
It’s not uncommon to find many inconsistencies in our data on first review. Be sure to clean up this data and establish standards for every field. Perhaps you require that all employees be assigned to a subdepartment (whereas it wasn’t required before), or you only capitalize the first letter in your job competency names. Establishing these seemingly small standards can actually work wonders for keeping your data clean and consistent.
Then train those who enter the data into your HR system about your standards. If you enter the data yourself, learn to be extra vigilant about following your own process. When training others, help them understand why the little details are so important. For example, maybe if they accidentally include a space after someone’s first name (“Eric ”), it becomes harder to link that HR record to other data sources where the employee’s name doesn’t have the extra space.
Better data hygiene will help you feel more confident, and you’ll be able to explain to stakeholders the rigor with which you and others maintain it. If you aren’t prioritizing data cleanliness, how can you expect others to trust the work that comes out of it?
2. Anticipate Questions and Concerns
Before you dive into an analysis, acknowledge your own expectations about what you might see in the results. Then, think about what other stakeholders and employees may expect to see in the same results—what are their beliefs about your workplace?
Recognizing these expectations upfront will provide clarity on how the analysis supports them and where it disproves them. Draw on your real-life knowledge of your organization to identify which findings might be of particular interest, and drill down into the data to understand those findings at a greater depth. Some results may have an associated shock factor, which will naturally require greater supporting detail to garner trust from others.
Perhaps one team had an exceptionally high Q1 turnover rate. But that means little without context. How much of the turnover was voluntary or involuntary? How small is the team? This team’s stakeholder will likely dig for greater detail, and it would be helpful to anticipate the follow-up needs. If presenting this finding to a broader audience, you’ll want to provide a clear context, so there is no misunderstanding or objection to the number.
By anticipating potential questions and concerns before analyzing your data set, you’ll be better prepared to tackle them with confidence—in turn inspiring the confidence of others.
3. Know When to Act—and When Not To
When stepping into the world of People Analytics, we often feel a bias toward acting on our findings. After all, if not to act, then why are we measuring anything at all? Ironically, just as important as the actions we take with data are the actions we don’t take.
Take that earlier example of high turnover. If the turnover was part of a planned workforce change, it may not reflect a true turnover problem. If we drive to action on something that doesn’t warrant it, we’re likely to waste money, time, and other resources building a solution for a problem that may not be real. So action itself can jeopardize the credibility of data efforts if it 1) tries to solve a non-existent problem or 2) fails to solve the root cause of a real problem. In either case, your effort won’t result in direct benefit to the organization, calling into question the value of the effort. These risks all point to the importance of preemptively understanding the organization and the true problems of its stakeholders.
As you improve at differentiating actionable from non-actionable findings, you’ll be demonstrating that you can leverage your organization’s data thoughtfully and efficiently. By driving impact on meaningful problems, you can start to earn greater trust from other stakeholders who recognize that their own challenges can also be addressed with data.
People analytics and HR metrics are taking on increasingly meaningful roles in the workplace. That’s where you come in: it’s up to HR professionals to ease their organizations into the experience of data-based decision making. Your analytics don’t need to be advanced or complex to result in meaningful change. Even simple reporting and analysis require that others trust the data you’re working with. Just remember our three rules: keep data clean, anticipate objections, and know when to act.
Together, these suggestions will set your data and analytics work up for success!
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