When “Good” isn’t Good Enough

According to the recently published LinkedIn Global Talent Trends 2020, 73% of surveyed HR professionals say that People Analytics will be a major priority of their company over the next 5 years. While People Analytics continues to be red hot, the truth is many leaders both inside and outside of HR see creation of slick dashboards and people analytics as the end-point and not a way-point. While dashboarding and reporting are valuable, their utility is still rather low when considering the analytics maturity curve [1]. They reflect a lagging perspective through the rear-view mirror about what’s happening in the business, where, and by how much. The bigger opportunities for people analytics teams is to not only provide deeper insights that can increase the HR efficiency, but also positive outcomes for the business through better insights about potential talent decisions.

A precursor to good analytics that drive outcomes is complete and high-quality data. This basic, foundational step (see below) is often an afterthought for HR organizations as even sophisticated and data-rich organizations struggle to get traction with their people analytics efforts due to bad data.

This situation is reflected in respondents’ assessment of their ability to collect and maintain data per the Global Talent Trends. Nearly 60% of the professionals’ surveyed indicated that their ability to collect data was “Fair” or “Poor”. An equally, if not more important, step after data collection is maintenance, and over 53% of respondents rated their capability as “Fair” or “Poor”.

“Fair” or even “Good” data collection and maintenance practices will not cut it. Companies must strive to achieve “Great” when it comes to data quality. So what steps can companies take to ensure they are starting with, and maintaining, accurate and complete data? In our clients’ experience, there are three main areas of importance:

Streamline collection throughout hiring & on-boarding: The first collection-point for employee data is through the requisition, hiring, and on-boarding processes. These steps also represent a common source of data quality issues, so having strong data collection procedures and technological solutions in place is paramount. Hiring increasingly starts with electronic applications and there are opportunities to collect much of the basic demographic data before any potential applicant even interviews. Furthermore, linking candidates to hiring requisitions provides an opportunity to create alignment to key data fields like manager, department/division, job profile, etc. that would need to otherwise be assigned upon hire which creates the potential for incorrect or incomplete data. Finally, the on-boarding process can and should be a final place ensure that all key data fields have been populated and validated as any errors at the point of entry will be harder to catch and fix downstream.

Good governance for making changes: Once the employee is in the door, the data quality struggles do not end. As people change jobs, move locations, etc. their data must change as well. This is an area where companies are often most challenged. Line managers, who might be tasked with making changes on behalf of the employee are often less familiar with the systems and features of HRIS systems. It is critical that HR Organizations balance empowering employees to make changes themselves vs. the potential for unintended consequences of too much autonomy. This can be accomplished by having in place clear governance and processes (locked fields vs. manager approval vs. employee input, etc.).

Regular auditing and employee involvement: So you’ve nailed down your data collection process and have top-notch governance, but that doesn’t mean you can stop there. It is important to continually monitor and audit the quality of your people data. People are fallible, mistakes happen, and auditing your data at a regular cadence helps ensure that errors don’t have ripple effects into the broader organization. If your data audits reveal issues, there are many approaches to address them from HR handling all changes to broader enterprise efforts. For example, to help cultivate a culture of data-quality at a recent client, we designed and executed a company-wide data clean-up effort that engaged line managers to review and correct key data of their direct reports.

The trends in people analytics are exciting and potentially game-changing, but they also represent a departure from many HR professionals’ fundamental training and experience. Whether you are just trying to get your first HR dashboard off the ground, or you are wading into predictive modeling and network analysis, a thoughtful and comprehensive strategy for data collection and maintenance is crucial.

By Lotis Blue Consulting, June 29, 2020

[1] Analytics at Work: Smarter Decisions, Better Results, Thomas Davenport, Jeanne Harris, Robert Morison