3 complexities of people analytics and how to create order through an ecosystem

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Posted by Kathi Enderes and Matthew Shannon on February 27, 2019.

If you are looking for that silver bullet, that unicorn of a solution to understand and optimize people at work—stop looking. People are complicated, organizations are complicated, and work is complicated. So is the people analytics market that exists to help you make sense of it all. Here’s an approach to manage that complexity so you can turn the promise of people analytics into actual insights for running your business.

People analytics is a hot topic, with 84 percent of surveyed executives seeing it as a key business priority.1 According to BersinTM, Deloitte Consulting LLP’s study of people analytics, organizations with the best people analytics functions see 96 percent higher revenue over a three-year period, compared to their less effective peers.2 Getting to this state requires various different factors, including data governance, analytical capabilities, a data-savvy workforce, scalable delivery of insights, alignment with the business, and an overall data culture. But on top of all those factors, organizations need the right tools and technologies to make it all happen.

Not surprisingly, the people analytics technology market is equally hot. Go to any HR conference, and you’ll see a dazzling array of shiny tools all promising to use artificial intelligence (AI), cognitive tools, machine learning and other next-generation capabilities. Whether it’s organizational network analysis (ONA), natural language processing (NLP), robotic process automation (RPA) or some other three letter acronym (TLA), it can be confusing to navigate this market and determine where to focus.

Complexity 1: People data come in many types.
It would be easy to describe people through simple demographic and employment data – but it’s not true. People are not one-dimensional. They have lives outside of work, relationships with other organizations, interests and passions, capabilities and skills. Their health and well-being are intrinsically related to their performance and productivity. High-performing organizations acknowledge this and create a more holistic picture of the entire person, expanding the types of data beyond job and demographic information to include data on health and wellness, external employee information, even geospatial insights. For example, geospatial data can be used to understand skills available in different locations, determine commute patterns for answering questions on stress and engagement, or understand how people use different space options to optimize office configurations. This multifaceted approach is necessary to create a more holistic picture of people.

Complexity 2: People data reside in many places.
Unconventional types of data reside in many different places. High-performing organizations use an average of seven different data sources (compared to just three for their low-performing peers).3 Beyond traditional employee surveys and HR systems, they mine data from emails or meetings, publicly available sources, posts on internal and external social media sites, and even unexpected places like performance goals or HR support tickets. For example, analytics on performance goals can create alerts for goal overlap, pinpoint voids that need focus, or suggest collaboration of people with similar goals. This allows organizations to “listen” more effectively to people’s input about their experience and productivity, and address issues more effectively

Complexity 3: People analytics solutions are available in many (overlapping) categories.
People analytics solutions are everywhere today – and not just in vendor exhibition halls. The capabilities that power people analytics applications—like identifying high-potential employees or addressing retention trends—boil down to analytical tasks such as data gathering, reporting, analyzing, and suggesting actions. Most organizations already employ solutions that perform these tasks…think of spreadsheet software, statistical analysis tools, and data visualization solutions. In addition, new “pure-play” people analytics solutions offer specialized capabilities like organizational network analysis and natural language processing to generate insights from previously untapped data types and sources. And even as new solutions are available, so too are similar capabilities increasingly embedded in other vendor offerings. Talent acquisition suites, for example, offer predictive analyses of which individuals are best-fit candidates or likely to take a new position, and employee engagement solutions help identify factors that indicate employee attrition and propose actions to managers to counter that.

Cutting through the complexity with an ecosystem approach
As we said, there’s no one silver bullet that can solve all your people analytics needs. Mature organizations manage an ecosystem of tools to generate rich insights and complex actions for business impact, pulling together the right tools for each problem (see figure 1). These tools have unique capabilities and strengths—from reporting to analysis, from visualization to action-oriented nudges, and from measuring activity to analysis impact.

Figure 1: Use of people analytics technologies
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Source: Bersin™, Deloitte Consulting LLP 2019

Focusing investments to build capability and capacity
Many mature organizations also invest much more heavily in people analytics than their less mature counterparts, and the amount of investment in people analytics directly correlates to better business and workforce outcomes.4

But not only mature organizations are investing. Over 70 percent of organizations surveyed invested in building people analytics capabilities in the last year. One in three organizations built or improved a data warehouse—not surprising, as the vast majority of organizations are at the stage of building a solid people analytics foundation and getting to a “single source of truth.”5 Tools for aggregating data are at the tail end of the spectrum (see figure 2).

Figure 2: Investment in people analytics technologies
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Source: Bersin™, Deloitte Consulting LLP 2019

Which specific tools an organization needs depends on various factors, including unique business challenges, existing technology infrastructure, and where they are in their analytics journey. For example, high-performing organizations are almost three times as likely to invest in data aggregation tools than low-performing organizations6—because they have sufficiently built up the infrastructure to have the right data to aggregate.

To separate hype from reality, organizations need a guide to navigate the people analytics landscape. At Bersin, we explore people analytics capabilities to create one such guide. Our research helps organizations understand which capabilities supplement existing tools and how analytics solutions can help them understand and optimize the people side of business.

If you are a vendor with people analytics capabilities, participate in our People Analytics Solution Survey for consideration in our market research.

Kathi Enderes PhD, is a vice president and the talent & workforce research leader at Bersin™, Deloitte Consulting LLP.

Matthew Shannon is a senior research analyst for solution provider market research at Bersin™, Deloitte Consulting LLP.


1 2018 Deloitte Global Human Capital Trends.
2 High-Impact People Analytics, Bersin, Deloitte Consulting LLP 2017.
3 High-Impact People Analytics, Bersin, Deloitte Consulting LLP 2017.
4 High-Impact People Analytics study, Bersin, Deloitte Consulting LLP.
5 High-Impact People Analytics study, Bersin, Deloitte Consulting LLP.
6 High-Impact People Analytics study, Bersin, Deloitte Consulting LLP.

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