People Analytics for Employees with Multiple “Jobs”

The rise of the gig economy and AI has shown that we can no longer afford to think of “jobs” or “work” in the traditional way. There is a fluidity to the workforce, where people move in and out of roles–and organizations–as needed, and technology can be brought in to take over certain rote tasks.

However, assigning people tasks on an ad-hoc basis isn’t some future of work concept. It’s an approach that’s been around for a long time and is known as a project-based management structure.

This practice used to be the domain of niche industries, but it is gaining more traction across a wider variety of organizations. With project-based management, the normal movement or allocation between two (or more) projects or assignments becomes formalized with multiple supervisors and reporting structures.

Project-based structures are common in healthcare, for example, where nurses work in different wards or facilities and doctors may also be instructors at the affiliated medical school, management, and/or staff physicians.  

Likewise, professional services firms, such as accounting and management consulting, are naturally project-based. Consultants can be deployed to work on multiple clients concurrently, and the clients they work on will change over the course of time as projects complete and new engagements are sold.

While the assignment can be an actual job, such as professor and physician, or projects under a single job, such as customer engagements for the consultant, the relationship between the individual employee and their assignment is not one-to-one, but one-to-many. This is where the challenge can arise, especially for your various HR systems.

Project-based management structures mean the normal movement or allocation between two (or more) projects or assignments becomes formalized with multiple supervisors and reporting structures.

Why Counting People Makes Project-Based Management Hard

Many HR solutions are created to support the “one person, one job” structure and treat the employee–and their job–as an indivisible object.

For example, to accommodate the nurse who works at different facilities, this means that only one facility is associated with her record and nothing is tracked for the other. If she were to stop working in the second facility, there would be no record of the turnover because there was no record of the hire or movement to the facility in the first place.

Some HR solutions are able to accommodate the multiple jobs scenarios by splitting the job from the person record.

Data associated with the employee, such as name and demographic data, resides on their record, and one or more job records are attached to that employee. In our example, this means the nurse would have one person record and two job records, accurately capturing in the HRIS where they worked.  

But traditionally, analytics has continued to count people. We count headcount and turnover in units of whole employees. When employees can be committed to multiple jobs or assignments in an organization at the same time, and each of those can change independently at any time, counting people fails us. And therefore, any analysis we do on our people fail as well.

Data visualization of an employee record with multiple assignments

The Analytics Challenges of Project-Based Management

Let’s say a nurse with two placements transfers from the burns ward to the intensive care ward, but keeps her place in the emergency department. Would you say this is one move or half a move? Should the nurse be counted as two partial-FTE headcount or a single headcount?

Consider this scenario: another nurse with two placements decides to end one and work only part time in the remaining placement, in order to spend more time with his kids. Is this considered one turnover unit or a partial one?

And this isn’t just a problem with formal jobs: it also impacts the less formal management of assignments, where the firm is concerned with an employee’s utilization.

For example, a consultant is working on two clients in March and when one of those projects completes, she is left with one client for a week in April. However, right after the firm deploys two additional clients to her at the end of the month. The firm pays this consultant a full-time salary, and the week where they are underallocated represents waste. How can the firm’s partner accurately identify and minimize instances when their consultants are “on the beach” or “on the bench”?

Building in that same one-to-many relationship in analytics enables users to pivot between looking at the person, the assignment, and the person in the assignment has proven incredibly challenging.  

When employees can be committed to multiple jobs or assignments in an organization at the same time, and each of those can change independently at any time, counting people fails us.

Just as many off-the-shelf HRIS solutions weren’t able to accommodate the requirement, pre-built analytic solutions couldn’t either. Building that relationship into the already incredibly complex model of HR data was also out of reach. But without it, it’s impossible to get accurate headcount reporting, hire and turnover statements, and more.

These systems often create duplicate employee records for each job and analysts run the risk of double-counting employees who have more than one assignment. If this happens, headcount is off and you get an overstatement of hires and turnover, as duplicate employee records are added and terminated to reflect the changes in assignments. The analyst must be careful to exclude new employee records that are not true external hires, and termination records that indicate only the end of a project–and not a resignation or termination.

Visier Introduces Multiple Assignments

As we observed the increasing adoption of this management approach and heard the challenges of our customers, we identified a way to adjust our core data structure to enable the meaningful analysis of employees with multiple assignments, thus enabling users to get insight at both the person perspective and the job perspective. Here’s how it works:

  • Visier People™, the leading people analytics and workforce planning solution, creates one core record per employee that stores the core attributes unique to that employee–independent of job–such as name, age, demographics, tenure, and so on.  
  • Assignment records contain their own metrics, independent from each other.
  • The assignment record stores the job role, time allocation, and organizational rollup of the assignment.  

This enables Visier to work with the data in a way that doesn’t confuse the two and allows for accurate measures at the employee level and at the job level.

Also since assignment start and end metrics examine only the changes on the assignment records, the analyst and business user can understand the speed at which projects are completed, or the amount of internal churn that occurs as employees transfer between assignments or departments.  

On an employee by employee basis, the total time allocation of their assignments can be compared to the their nominal FTE rating to discover who is under- or overutilized, both at any point in time and over time. This helps the organization optimize their workforce by deploying their staff more efficiently.

Data visualization showing the trend of active assignments versus headcount

Given this data, Visier People helps business users answer questions such as:

  • Are junior employees assigned and reassigned to projects more often than senior ones?
  • Does changing assignments frequently contribute to voluntary turnover?
  • Does holding too many concurrent assignments degrade performance?
  • Are any employees underutilized, and could be assigned to additional work to generate more revenue?

With Visier, you can trust that we’re always watching for changes, trends, and requirements that impact you, and are able to build features in our people analytics solution that will help you be prepared for whatever comes your way.

Author Photo
Wayne Hoy |
Wayne has always had a passion for answering business questions with data. He has been a people analytics pracitioner since 2012 in roles ranging from a front-line analyst to leading a team of them. When he’s not working at Visier to expand the HR analytics solution, he enjoys watching and playing basketball, and he often thinks about cycling to work, but ultimately he rarely ever does.