Scalability is a term that we all use. Scalability is desirable and necessary. The size and scope of our corporations, government and other institutions are so large that without scalability, we will fail.
If we want to achieve pay equity by 2025 we cannot use the manual processes that currently rule the day.
Scalability Requires Automation
The manual processes that most of us currently follow have these drawbacks, and more:
- Require herculean effort to pull data together
- Use statistical tools that need to be programmed for this type of analysis
- Require reinvention every year of objectives, remediation strategies, etc.
- Rely on teams that are not intact throughout the year, but are only brought together to do this work
There is a huge inconsistency here. No major corporation or government would consider doing other vital business processes manually and with an ad hoc team. Yet that is exactly what they do when it comes to pay equity.
Furthermore, there is no large modern corporation (or even small to medium) entities that are happy with getting data about key business outcomes and processes only once a year or less. For example, real time financial and customer analytics are nearly universal.
Real Time Data is What We Need
That’s why we are recommending a scalable data-driven in-house approach to the assessment & remediation of pay gaps. This approach should be able to give us real time or near real time data.
Gartner defines real-time analytics as a discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time means the analytics is completed within a few seconds or minutes after the arrival of new data.
When it comes to data about our employees, most of us are happy with once a year (or twice a year) data gathering exercises. In the hiring process, fast changing market conditions are not reflected. Neither does manager A typically know what Manager B has negotiated with incoming hires and is unlikely EVER to find out what the patterns are.
Stated like that, it seems pretty ridiculous, doesn’t it? There are many more points in the employee lifecycle that are equally opaque.
A Laundry List of Bad Practices
When it comes to pay equity analysis, (and other DEI analytics) here are just a few of the bad practices we tolerate:
- A lot of work is done by consultants (or internal temporary teams) on a one off, usually yearly, basis, resulting in data that is old before the analysis is finished. If this worked, once pay gaps were remediated, they would not reappear. They do.
- Analysis is usually only conducted once per year. Go tell the CFO that they can only have financial updates once a year.
- There is no methodological commonality or transparency. When pay gaps are reported, how do we know they are accurate?
- There are no common data models or data definitions and the ones that DO exist are lacking detail and/or common sense.
It is on these last two points that the EPIC work will focus next: in two weeks, we’ll unveil the Pay Equity Reference Model. Stay tuned.