July 2019 Zachary Chertok Research Analyst Human Capital Management

KB

FROM WORKFORCE MANAGEMENT TO FULL WORKFORCE ANALYTICS

2

For the average company, the earliest form of deployed automation is workforce management (WFM), which includes time-and-attendance, payroll, and compensation management. Fifty-eight percent of companies start their analytics strategies with WFM. However, as companies grow — and as HR and management look for better ways to expand automation — is WFM that matures into workforce analytics enough to build a complete, human-capital-management (HCM) ecosystem?

The Automation Evolution

The average small business starts HR process automation with payroll and branches into other HCM automation. Small businesses are 96% more likely than medium and large-sized companies (76% vs. 39%) to launch digitization with payroll automation. As organizations grow, payroll management constitutes the most considerable compliance risk, especially with the creation of new positions and the addition of new staff.

Following payroll, organizations next expand time-and-attendance, scheduling, and task-management automation. While some of these WFM functions may apply to certain industries intuitively (i.e., retail, healthcare, or trades with billable hours), as many as 75% of companies (across 14 different sectors) use WFM functions as part of their labor-management strategy. At one end of the spectrum, organizations track time for compliance, cost management, and shift-based management. At the other end, time management constitutes an early form of performance management, where HR and managers track task-time to understand patterns in skills development, employee acclimation, motivational indicators, and engagement.

As companies grow, HR faces a strategic challenge: the increasing employee-to-manager ratio limits the amount of time that managers spend engaging employees. The quality of relationships between managers and employees concerns 68% of companies, while 73% find disengagement rising the longer an employee remains with the firm. These characteristics feed a growing trend: low employee-engagement rates that alarm 83% of companies.

Small businesses are 96% more likely than medium and large-sized companies (76% vs. 39%) to launch digitization with payroll automation.

3

When organizational growth poses an interaction challenge for managers, performance management needs to become more predictive and more sophisticated to help managers allocate their time better and to help HR understand when hiring or promoting more managers from within becomes necessary. As firms pass the threshold to medium-sized, can early workforce analytics scale far enough to serve the company reliably across all potential options for continued growth.

Figure 1: Three Stages of the Automation Evolution

Figure 1 illustrates the simple model that more than 70% of organizations follow to scale their HCM ecosystem organically, in tandem with corporate growth. Required automation pushes 76% of firms to pursue payroll automation, which leads to WFM task automation and data collection. As firms outgrow the small-business stage, the average HR organization needs either more resources or more staff to manage the expanded personnel and the data they generate.

Up to this point, payroll and WFM automation include engines that collect and report data but leave the in-context analysis to HR and managers to interpret between solutions. Best-in-Class companies are 44% more likely than All Others (46% v. 32%) to recognize that multiple, end-point solutions — those that output separate analytics rather than contribute raw data to a consolidate engine — force HR and managers to scrutinize multiple dashboards for performance analysis and workforce planning.

The Decision Separation

The average HR organization spends as much as three to four hours, per day, reconciling disparate analytics views — and another hour compiling findings daily — into a workforce and performance plan, which employees often carry out with or without guidance from management. Over a week, this effort can consume one- to two-thirds of the time that HR or management would otherwise spend engaging employees on individually strategic issues, like stress management, career development, or skills training.

Required Automation

Ecosystem Expansion

Data Unification Required automation

pushes 76% of firms to pursue payroll automation, which leads to WFM task automation and data collection.

4

Table 1: Top Pressures from Individual Solution Management Best-in-Class All Others

Slow HR response times 78% 71% Weak bargaining power 75% 64% Management by consensus 60% 29% Employee disengagement 55% 73% Poor employee-manager relations 54% 70%

Table 1 shows the top pressures resulting from individualized solution management. None of these pressures should come as a surprise considering the amount of time HR and management spend scrutinizing individual analytics dashboards rather than being pointed in the right direction to engage the workforce with automation. At the top of the list, Best-in-Class organizations find that their response times to employee problems or issues are too slow either to resolve the issue productively or to save the talent from becoming a flight risk.

Today, under pressure to stem high turnover, 74% of HR organizations reduce labor costs starting with the top. Shortened tenure and heightened turnover increases new-hire onboarding requirements, increases resource demand for stronger hiring practices, decreases business continuity and consistency, and lengthens the employee return-on-investment payback period. The natural response among HR professionals is to increase demand for technology resources (to more proactively assess and manage additional employee-performance indicators) or to increase headcount (to reconcile performance metrics while increasing employee engagement).

The problem? Fewer than 20% of medium-sized, HR organizations have a chief human-resource officer (CHRO) to act as a champion for new resource deployment, which leaves HR decisions to finance and operations. When the available technology results in slow reaction times and increases the likelihood that multiple representatives must weigh in on minor decisions, it is much harder for HR to justify and / or request expanded resource spend. In other words, HR faces weaken bargaining power and increased management-by-consensus. The more time HR and management spend decision-making, the less time they spend engaging employees, which results in more decisions made at the upper echelons — all of which leaves employees wary of management.

Under pressure to stem high turnover, 74% of HR organizations reduce labor costs starting with the top.

5

Faced with a no-win situation for approval to acquire the functionality needed to complete the HCM ecosystem, HR doubles down on the resources that they can justify with finance and operations, which leads to a strategic schism: More functionality means more data, which increases the need for easier and faster ways to analyze. The resulting conundrum points to one of two solutions:

Organically expand the HCM ecosystem with best-of-breed solutions to solve key HR problems as they emerge. HR justifies new deployment as necessary — based on the specific problems faced — and chooses the most competent functionality for the cost. HR builds an ecosystem from different solutions that solve the automation challenge but disregards the disparate datasets, each of which requires separate analysis to resolve critical workforce problems.

Evaluate the data and analytical needs across the organization to identify an all-in-one solution that provides a ready-to-go HCM ecosystem and addresses all existing workforce data challenges. While the all-in-one HCM ecosystem may not be best-of-breed for each specific challenge or need, together, the functions provide an out-of-the-box solution that does not require additional finance or operations approval to expand. In the short run, the rip-and-replace requirements of this implementation may be costly, but HR may find that it has an easier time justifying expansion without the need for a CHRO champion, in the long run.

Data unity is an ongoing challenge. Regardless of the strategic model chosen, 71% of organizations pursue a data-unity strategy once they deploy three or more disparate solutions. The Best-in-Class are 2.2 times more likely than All Others (32% vs. 15%) to integrate data as part of their long-term performance analysis and management strategy.

The Best-in-Class Pattern

When it comes to analytics across the 14 industries that Aberdeen surveys, the Best-in-Class define a best-practices roadmap. Table 2 shows the top analytical models that the Best-in-Class use to unify the HCM ecosystem, as compared to All Other firms. The Best-in-Class consolidate, first, though workforce analytics; they are 26% more likely than All Others (71% vs. 56%) to unify data from a base in workforce management as they merge analytics and planning. The early base makes it easier to incorporate legacy data as the firm adds functionality for talent acquisition, talent and performance

The Best-in-Class are 2.2 times more likely than All Others (32% vs. 15%) to integrate data as part of their long-term performance analysis and management strategy

6

management, and compensation and benefits planning, which includes wellness, well-being, rewards, recognition, and learning and development.

Table 2: Top Analytical Platforms to Unify HCM Ecosystems Best-in-Class All Others

Workforce analytics 71% 56% Machine learning 62% 22% Predictive analytics 60% 52% Platform-as-a-service 43% 32% Cognitive AI 18% 3%

Workforce analytics provide a strong foundation to unify skills development and engagement analysis via employee, task-time management data in common. Linked to the individual employee record in the HRIS, this data helps to individualize the analysis establishing the benchmark indicators of employee progression and potential development within the firm. The Best-in-Class are 3 times more likely than All Others (36% vs. 12%) to be able to link employee performance directly to changes in revenue generation, which unifies complex and individualized employee metrics through workforce analytics. The Best-in-Class are also 2.8 times more likely than All Others (59% vs. 21%) to be able to establish management goals based on a stronger understanding of employee motivational and stress factors (based on task-time analytics that include performance criteria.)

With workforce analytics as a base, organizations can develop foundational data and map employee performance indicators against strategic changes in organizational metrics. Eventually, this leads the Best-in-Class to graduate to predictive analytics before moving into more complex analytical environments like machine learning.

As Table 2 shows, predictive analytics and machine learning are the second and third most-prevalent analytical environments among the Best-in-Class. Following the evolution of Best-in-Class unified analytics, benchmark organizational data — founded by early workforce analytics — helps to set the stage for more predictive environments that pull individual performance data into the analytical framework to provide behavioral context to task management. Eventually, the Best-in-Class go one step further to deploy solutions that move the benchmark forward as the algorithms set new time goals based on changes to organizational and individual performance.

The Best-in-Class consolidate, first, through workforce analytics; they are 26% more likely than All Others to unify data from a base in workforce management as they start to merge analytics and planning.

7

By the Numbers

While 43% of the Best-in-Class diverge from pursuing platform integration and data unity within an analytics layer that sits beneath disparate best-of-breed solutions, the average Best-in-Class progression favors a foundation in workforce analytics that evolves into a predictive framework leading towards machine-learning possibilities.

Table 3: Analytics Increase Revenue per FTE and Productivity Revenue per

FTE Productivity

Platform-as-a-Service (PaaS) 3.7x 0.79x Workforce analytics 2.8x 0.80x Machine learning 2.6x 0.76x Predictive analytics 2.2x 0.82x

Table 3 shows the likelihood that organizations will increase revenue per FTE and productivity for every 1% they are more likely to deploy various analytics to unify their data. While PaaS produces the most-considerable immediate benefit to revenue per FTE, it lags behind the cumulative progression from workforce analytics through predictive analytics to machine learning. Individually, each of these analytical frameworks improves operational performance, in the short run, while simultaneously establishing long-term, incremental improvements for the firm.

The Best-in-Class are 65% more likely to launch their data-unity strategy with workforce analytics than by inserting a PaaS layer beneath an existing HCM platform that’s a compilation of best-of-breed solutions. By the end, the Best-in-Class are also 44% more likely to evolve to machine learning — by way of predictive analytics — than to explicitly develop a PaaS layer (62% vs. 43%).

Technologically, developing a PaaS layer or pursuing a strategy of analytical evolution will meet with the same result: An analytics layer (implemented beneath existing HCM functionality) that links all generated data together to produce a centralized, multi-dimensional analytics view for strategic HR decision-making. What differs between the two outcomes is the strategy followed to achieve them.

A PaaS layer (designed and deployed explicitly for HR) limits how HR analytics extend across to the organization. For more than 50% of firms, organizational metrics plug into the HCM ecosystem framework for cross-

8

functional analysis, which shifts more power to HR and away from operations. Considering the low CHRO statistic, this solution can be very problematic for more than 80% of organizations.

Workforce analytics become a natural point for the average HR organization to launch a data unity strategy. Workforce analytics provide cohesion within the HCM infrastructure by mounting all others on the common metric of task-time completion. In this way, HR can continue to expand HCM infrastructure while quantifying time linked to both labor cost and productivity. Gradually, the time-based benchmark data establishes a framework for predictive analytics that feeds into a more comprehensive strategy for cross-functional operations management thoughout the firm.

9

About Aberdeen

Since 1988, Aberdeen has published research that helps businesses worldwide improve their performance. Our analysts derive facts-based, vendor-neutral insights from a proprietary analytical framework that identifies Best-in-Class organizations from primary research conducted with industry practitioners. Aberdeen provides intent-based marketing and sales solutions that deliver performance improvements in advertising click-through rates and sales pipelines, resulting in a measurable ROI. Aberdeen is headquartered in Waltham, Massachusetts, USA.

This document is the result of primary research performed by Aberdeen and represents the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen.

17946