July 25, 2019 | Point of View

Redefining the role of analytics in workforce productivity

Companies that look beyond data and technology and shift toward organizational elements can extract more value from their analytics initiatives

Executive Summary

  • Workforce analytics solutions are insufficient on their own. Executives across industries recognize that basing workforce productivity on data and analytics alone is precarious at best and counterproductive at worst. Finding the right blend of the human element in data and analytics starts with a comprehensive view of the workforce productivity program. 
  • Cross-functional perspectives can elicit more valuable insights from analytics. Cooperation among departments to interpret and apply analytics insights will ensure that any improvements will have positive effects beyond a single department or goal. Looking at analytics with the overall business in mind will also help avoid isolated efforts that may hinder the operation. 
  • Organizations must develop new capabilities. It’s critical to equip employees with a baseline understanding of analytics and how to apply them. The most successful companies will factor in time to train their workforce to use data and analytics rather than implementing tools and hoping employees embrace them. 
  • Ensure alignment between analytics efforts and business goals. Policy governance is essential to verify that analytics are being integrated into processes and adopted by employees. By emphasizing governance, organizations will be able to connect investments in analytics to business results and track progress. 

Introduction

Workforce productivity was recently in the spotlight when Amazon used analytics to monitor productivity at the individual level and provided automated termination recommendations. The resulting outcry pinpointed a crucial element that was missing: input from a supervisor or colleagues. 

Analytics are often viewed as a silver bullet that will help companies identify the root causes of workforce or organizational issues. It’s not. As organizations invest significant resources to apply data and analytics to workforce productivity, they ignore the human element at their peril. 

In our work with clients, we have seen strategies based solely on workforce analytics actually hinder decision-making, thereby lowering productivity. At the same time, companies can miss opportunities to use workforce analytics effectively to build resilience in the face of unexpected economic downturns, mitigate employee turnover, or identify the traits of top performers to inform hiring processes. 

Finding the right blend of the human element in data and analytics starts with a comprehensive view of the workforce analytics program. Efforts to collect data on productivity using analytics, key performance indicators (KPIs), and dashboards must be matched by investments in talent and capabilities. Organizations often lack skilled employees who can harness both analytics tools and business knowledge to translate insights into actionable decisions and strategies. Executives also need to know how heavily to rely on the analytics and where the human layers—culture, mind-sets, behaviors, and data interpretations—come in. Without such pieces in place, even the highest- quality data and most sophisticated analytics platforms can fail to meet an organization’s needs.

Chapter 1: 4 pitfalls to avoid when developing and implementing workforce analytics strategies

Many companies view people analytics as a high priority for their organization but report that business benefits have been slow to materialize. Executives can be forgiven for believing that applying data and analytics for workforce productivity is a moving target or that their own results demonstrate more hype than utility around people analytics. 

Through our work with clients on workforce productivity efforts, we have identified four common traps that can underlie the issue: 

1| Data aggregation and analysis are overemphasized

In the early 2010s, big data was at the top of the agenda for executives. Large data sets seemed to hold immense promise to support data-driven decision making and improve workforce productivity. Yet the sheer amount of available workforce data can prove overwhelming. Although new algorithms, platforms, and analytics capabilities have emerged, they are only part of the puzzle. Carefully selecting and tracking leading KPIs can help avoid the analysis paralysis brought on by trying to measure and report on absolutely everything. 

2| Technology is viewed as a one-size-fits-all fix

Many executives invest in customized dashboards and modeling software believing that their internal capabilities, coupled with a sophisticated technology platform, will have an immediate impact. In fact, a majority of companies have had high expectations for how a new technology tool or platform will improve operations, only to realize after a long period of 

time those expectations have gone unmet. In our experience, that’s because several other critical factors must be in place. For example, companies that fail to set a clear strategy before making technology investments—and neglect a talent strategy as well— are likely to be disappointed.

3| Culture and process improvements are overlooked.

For most organizations, moving to data and analytics, particularly workforce analytics, requires a culture shift in several areas. Employees need assurance that workforce analytics are being implemented for organization-wide benefit and collaboration, so ongoing transparency and change management efforts are critical. And for employees with little previous exposure to analytics, adopting new platforms or processes—or behaviors—can be daunting. So if employees are being asked to change their behavior as a result of analytics insights, then communication and expectations should be clear and the data should provide logical support for the change. In addition, training needs to focus beyond just accessing data to actually applying it for analysis and decision-making. At the same time, leaders should set realistic expectations for adoption and time frames for impact. 

4| KPIs are isolated

Traditional workforce analytics efforts focus heavily on labor-centric metrics, but our experience shows that such data expresses business leaders to only one part of the problem. Looking at any one KPI is unlikely to provide insights that can have a greater impact on the business. Analysis based on a blend of different metrics—from productivity and efficiency KPIs to employee engagement, customer satisfaction and financials—can provide a more comprehensive view of the impact employee productivity has on the overall business. And when combined with human insight, KPIs translate to not only performance numbers but also opportunities. 

Chapter 2: Getting workforce productivity analysis right

Extracting insights from data and using them to improve workforce productivity isn’t just about the technology or analytics or program design or culture. It’s about all of those elements, and more, working together in a carefully orchestrated way. Companies can ensure that all stakeholders have the necessary visibility and engagement by focusing on the following areas. 

Work across functions 

Organizations that see the greatest impact from their workforce analytics start with cross-team alignment and an understanding of the potential value. To create this alignment, companies should lay out a plan and timeline for new technology adoption, capability building, and continuous support and improvement. Companies must factor in time to realign and readjust, as initial results and findings—in the data as well as employee behavior—will help to shape long-term strategy and planning. 

Alignment across teams will also help organizations define the comprehensive performance view that will aid a bigger organizational impact. The human perspective comes into play when organizations can make connections between labor-centric metrics and 

various other performance indicators, such as bottom- line numbers and client satisfaction. This task requires thoughtfully approaching how the analytics tools are set up and used and moving away from an off-the-shelf solution to a targeted one—with both quantitative and qualitative inputs—that has been designed to assist with a continuous improvement initiative. 

Alignment across teams will also help organizations define the comprehensive performance view that will aid a bigger organizational impact. ”

How to adopt a cross-functional perspective

Looking at any KPI in isolation is unlikely to highlight the complexities, relationships, or connections that can have a greater impact on the business. For example, one organization was looking to reduce costs, and based on one component of its performance, decided to eliminate its local HR representatives. This shortsighted approach led to significant downstream effects on its frontline management, who became the de facto local HR representatives but without the necessary training or tools to perform support tasks. This added responsibility pulled managers away from operations, leading to lower productivity, lower engagement, and lower overall quality. Overall, this change ended up costing the firm more than if it had kept its local HR team in place. 

Invest in talent development and capability building

Talent is one of the key prerequisites in using workforce analytics to make an impact. Employees need to have the institutional knowledge and analytics savvy to extract the most value from the tools at their disposal; without both, they may not understand what they may need to change either. In some instances, developing homegrown talent can be more productive than hiring from the outside. 

And yet, in a recent West Monroe survey, over 40 percent of executives said they cannot successfully determine the skills they have and the skills they need for the future, and nearly half had not assessed the skills of their workforce over the past year due to cost, competing priorities, or because they don’t know how. 

Providing the right programming and opportunities for employees to develop that knowledge and confidence in analytics is the key to creating an environment for change—as well as ensuring ongoing change management. Building on employees’ abilities and embedding analytics into their roles to cultivate a hybrid skill set will allow them to get more value from workforce productivity efforts. 

Building on employees’ abilities and embedding analytics into their roles to cultivate a hybrid skill set will allow them to get more value from workforce productivity efforts. ”

How other companies have approached capability building 

Improved insights also require employees to be familiar with data-and-analytics approaches as well as the time and resources to get up to speed on various tools and platforms. One health system introduced a tool in its call center with the aim of reducing average handle times. As employees were getting acquainted with it, call times rose—a result that made the organization question the 

technology’s effectiveness. However, it soon became clear that employees simply needed time to learn how to use the tool, and the organization instituted a comprehensive training curriculum. This training started with 23 different courses delivered over the course of two weeks, along with ongoing sessions on a quarterly basis to address pertinent topics. Eventually, analytics showed the success of this approach: Average speed of answer accelerated by 92 percent and first-contact resolution jumped to 97 percent (from 86 percent). 

Culture and change management are also important elements in boosting workforce productivity. One bank wanted to enhance customer experience and increase referrals and cross-selling of products and services using a more data-driven approach. It undertook a capability-building change management exercise to help its employees take advantage of a new customer relationship management (CRM) tool and process, which was heavily rooted in data and analytics. The bank noticed that adoption among its employees was low. It responded by aligning on outcomes of the project, defining KPIs, and creating a change network within the organization to provide support. Executives also conducted detailed assessments to quantify impact, gauge awareness and measure readiness for new processes and responsibilities. From there, they developed a role-based change management training to bridge skill gaps and encourage desired behaviors. This included training on how to apply data to support decision-making and how to think differently across roles and processes. 

As a result of this capability-building effort, the company saw increased user adoption, positive reactions to system and process changes, and improvements to pipeline information and overall data quality.

Conclusion

Implement robust governance 

Organizations that approach workforce analytics or change management as a one-and-done activity capture little value from their technology or talent investment. 

Making a sustained impact requires companies to build governance into analytics initiatives. Governance can aid companies in accountability, process improvement, and prioritization, all of which help to produce better results. Without investing in governance, organizations can find it difficult to verify that employees are doing anything differently, much less to connect business results to any particular issue. 

A sound governance program can also ensure that the objectives of the data and analytics effort stay aligned with the organization’s overall strategy and operations and that employees are using the insights to the company’s advantage. If an organization is focused on improving productivity, for example, looking to the data can help not only identify issues, but also tailor and personalize conversations to the individual. With a robust governance strategy in place, organizations can better track the results of such personalization and operationalize their approach across the organization. 

How to approach governance 

At one large company, disengaged employees, redundant manual tasks and a lack of standardization were hindering productivity and consistency. A mix of surveys and observation revealed a huge gap between how executives thought managers spent their time, how managers believed they spent their own time, and how managers actually spent their time. So the company decided to design a program that would engage all levels within the organization. By equipping managers with proactive workforce management tools and training, the company was able to improve productivity by 24 percent across its network. Thanks to an emphasis on governance and workforce analytics, the company has sustained these productivity gains year after year. 

When introducing an analytics-driven approach to workforce productivity, technology is just one ingredient of an effective recipe. Companies also need to make targeted investments to ensure the organization has the cross-functional collaboration, talent, and capabilities and governance to capture the expected value of data and analytics. It’s a complex undertaking that requires significant energy from the organization. The benefit, beyond tailored solutions that produce sustainable improvements in workforce productivity, is an organization with increased analytics maturity and the potential to extend this knowledge to other initiatives. 

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