A Canary in a Coal Mine: Using Employment Data to Identify Early Indicators of Risk
Retroactive analysis of HR data provides an opportunity to spot emerging issues that may result in future litigation.
As the risks facing companies grow and intensify, it’s critical to leverage data to keep looking for earlier and earlier indicators of risk. With the wide-ranging business and reputational risks that come with employment-related litigation, and the increased pressure to recruit and retain employees in a hyper-competitive talent market, HR data provides a particular opportunity to spot emerging issues that may impact an organization’s operations and future success.
Data as seemingly routine as that relating to attendance, hours worked, job performance, and enrollment in benefits can, when viewed in combination, direct employers to potential problem areas. If left unaddressed, such issues—potentially including lack of employee engagement, missed promotion opportunities, noncompetitive wages or benefits, or simply a misinterpretation of company policies—could create significant labor relations issues that have legal and/or HR-related consequences.
How Analytics Can Help
On its face, analyzing these types of data might seem like a fairly straightforward practice. Employers collect the information they need, identify a baseline, and look for deviations that, when viewed in conjunction with other datasets, tell a story. Then, they work to make the necessary adjustments based on trends uncovered in the data.
But the key is to figure out how to bring all the relevant data inputs together in a way in which they can be analyzed holistically. A dip in attendance, for instance, might not mean much in a vacuum. However, if the majority of those deviations come from a specific business unit—and those same employees have lower performance scores, are filing complaints, and/or fail to enroll in the same benefits they did previously—this may be a sign of a lack of employee engagement. Knowing that drops in employee engagement can lead to larger issues, the company can invest resources in getting a better understanding of what may be happening so that it can rectified.
What those investments entail depends, of course, on the organization and the problem at hand. In this example, it might involve additional training for managers or better communication about benefits. Other situations might call for a range of solutions, such as wage and benefit modifications, clarifying or updating company policies, and adopting new ways of responding to employee complaints.
Key Challenges and Best Practices
In practice, an initiative like this comes with its fair share of considerations and challenges. Companies should start by clearly identifying the scope of the program: What are the critical vulnerabilities unique to the organization at this time? What data will we need to collect to mitigate them? Which data inputs are we already tracking, or are most readily available? Which departments and individuals need to be involved in gathering the necessary data?
Oftentimes, collaborating with outside counsel can help identify the key data sources—both within the company and specific to its industry—that can provide valuable insights. Such a list of data sources might include:
- Employee benefits (e.g., who is using which benefit, at what frequency)
- Job performance (e.g., employee reviews)
- Employee engagement surveys
- HR-related questions (e.g., any areas with a higher volume of inquiries)
- Attendance records and hours worked
- Employee complaints (e.g., filed with the government or internally)
- Turnover (both voluntary and involuntary)
Then comes the challenge of data collection. The often-siloed nature of organizations’ data means the required information might be spread across various departments in various formats and systems. For example, salary data that may be used for pay equity audits tends to be siloed within the compensation function of an employer’s HR department. Yet if this information is shared at an early stage and viewed alongside other data inputs, it might reveal broader issues related to overall engagement.
As chief data officer of a global law firm, I’ve seen firsthand the importance of effectively capturing, classifying, and applying data—and have found the following principles to be useful in doing so:
Education is key. The most common objections when it comes to data collection is that it will take too much time, require too many resources, and tack on unwanted costs. That’s why it’s crucial to be able to inform people at all levels about the value of collecting this data. Make the business case: If it costs X to collect Y data, but it might prevent a lawsuit or employee turnover that would cost Z, it’s well worth the investment.
Focus on data governance and management. A crucial aspect of breaking down silos is ensuring that there are effective processes and controls in place to collect the data, organize it, and ensure and maintain its consistent quality. This entails, for instance, mapping data and implementing a governance model to provide a “single source of truth” for the organization; understanding who will have control over what assets; and establishing clear rules and policies related to its use.
Find the right partner. For in-house risk management or legal teams already grappling with increased workloads, finding the time and resources to monitor emerging areas of risk can be a challenge. They also may not have access to in-house analysts and data scientists, or the depth of HR and employment law knowledge needed to execute an initiative like this on their own. One option is to partner with an outside law firm that can bring the necessary legal and data capabilities, offer the benefit of attorney-client privilege, and potentially utilize data on the company’s legal matters that they already have access to as a result of an existing relationship.
A Path Forward
Ultimately this type of data-driven approach is designed to help spot percolating issues before they develop into larger problems or costly litigation. Yet promoting data governance and implementing such an approach can deliver benefits beyond the project’s immediate scope. For example, establishing and measuring key metrics to demonstrate the program’s success can go a long way toward communicating big-picture workplace issues to a C-suite audience.
Many organizations have taken important steps to get started on this journey. In a tight and fast-evolving talent market, where employee satisfaction and engagement are paramount, now is an ideal time to take those data-driven approaches to the next level.
As chief data officer for Littler Mendelson, Scott Forman directs the firm’s use of data to improve internal business functions and client service delivery. He leads a cross-functional data governance program, manages a team of data scientists, and oversees how the firm identifies, captures, classifies, maintains, and applies data and information.
From: Corporate Counsel