People analytics comes of (digital) age

CFO Insights

In the Q3 2016 CFO Signals survey, 40 percent of surveyed finance chiefs expect to make workforce and talent analytics investments in the next three years. In this issue of CFO Insights, we explore the increasing need for people analytics, and why embedding such capabilities in the business can lead to greater understanding of the issues and actionable insights.

People analytics, which involves using digital tools and data to measure, report, and understand employee performance, is going through a major shift. After years of investing in cloud HR platforms and specialist teams, many CHROs and business leaders, including CFOs, are not getting the results they want. In response, organizations are redesigning their technical analytics groups and solutions to better deliver real-time analytics at the point of need in the business process.

Still, as discussed in the 2017 Deloitte Global Human Capital Trends report, it is a slow shift:

  • While 71 percent of companies see people analytics as a high priority in their organizations (31 percent rate it very important), the percentage of companies correlating HR data to business outcomes, performing predictive analytics, and deploying enterprise scorecards barely changed from last year’s study.1
  • Moreover, readiness remains a serious issue: After years of discussing this issue, only eight percent report they have usable data; only nine percent believe they have a good understanding of which talent dimensions drive performance in their organizations; and only 15 percent have broadly deployed HR and talent scorecards for line managers.2

What’s clear, however, is that many CFOs see the potential in people analytics investments. In the Q3 2016 CFO SignalsTM survey, 40 percent of surveyed finance chiefs expect to make workforce and talent analytics investments in the next three years.3 And in this issue of CFO Insights, we will explore the increasing need for people analytics, and why embedding such capabilities in the business can lead to greater understanding of the issues and actionable insights.

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New tools; new mandates

Stakeholders—especially board members and CEOs—are driving this change. Senior leaders are impatient with HR teams that can’t deliver actionable information and insights; therefore, analytics is shifting from a focus on HR to a focus on the business itself. For example,

  • Leading ERP vendors are implementing a set of people analytics dashboards available to the CEO, to help senior leaders understand attrition, hiring metrics, employee cost, and employee engagement by geography, business unit, and manager. 
  • The chief operating officer at a large chain of hospitals uses analytics to understand patterns of patient outcomes and how management and people issues contribute to results.4
  • The sales organization at a major consumer products company has partnered with HR to develop a complete model for sales productivity, which helps predict and diagnose problems, pinpoint training solutions, and improve quality of hiring.5 
  • A retail head of operations now uses business and people analytics to look at customer and employee traffic patterns, identifying new locations where sales people should be positioned to help improve total customer purchase.6

For companies that have been investing in this area for years, it is now easier to get these answers than ever before. Predictive analytics tools from many HR technology vendors have arrived, making it possible to analyze data regarding recruitment, performance, employee mobility, and other factors. Executives now have access to a seemingly endless combination of metrics to help them understand, at a far deeper level, what drives results.7

Moving beyond the analysis of employee engagement and retention, analytics and Artificial Intelligence (AI) have come together, giving companies a much more detailed view of management and operational issues to improve performance. For example:

  • Data-driven tools can now help predict patterns of fraud, show trust networks, conduct ONA, show real-time correlations between coaching and engagement, and even analyze employee patterns for time management driven by email and calendar data.8
  • Artificial intelligence software can now analyze video interviews and help assess candidate honesty and personality.9
  • Tools can now analyze hourly labor and immediately identify patterns of overtime and other forms of payroll leakage, enabling improvements of millions of dollars through improved practices in workforce management.10
  • Off-the-shelf retention models are now available from SAP, Oracle, Workday, ADP, Ultimate Software, and others, making it easier than ever to understand drivers for attrition.
  • Deloitte and other companies are now looking at travel data, billing hours, and other human performance data to help employees improve their levels of energy, wellness, and business performance.

A big trend in 2017 is that these new solutions are business driven, not internally HR focused, challenging HR departments to move beyond their own internal view of data and leveraging people data for a broad range of business problems.

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The case for embedded analytics

Traditional HR organizations set up an analytics team as a separate group. Today, companies are rethinking HR as an “intelligent platform” and embedding analytics into their entire workforce management process. A large telecommunications company in India analyzes the time to productivity of every new hire, giving line managers and corporate leaders a dashboard to note when people are behind in onboarding.11  And several of Deloitte’s clients have now used ONA to analyze the behavior of high-performing teams to understand how work gets done.

While widespread adoption might be limited, people analytics has grown from a technical specialist group to a serious business function that must meet the needs of many stakeholders. And given this shift, there is a growing consensus that the best analytics programs are owned by a dedicated, multidisciplinary group. Some organizations place this in HR, while others build a center of expertise outside HR. For instance, Ford and others have expanded the people analytics function to work across all segments of the business, including finance, HR, and operations.12

One of the main drivers is that analytics is shifting from “pull” to “push,” where the analytics team no longer simply builds models and does projects, but now develops dashboards and tools that help managers and employees see relevant data in real time. One of our clients built a “talent management dashboard” that analyzes 10 different measures of team engagement and performance, and delivers this information to all team leaders and senior managers throughout the company. Versions of this are available to senior executives, helping them understand hiring, management, and performance issues around the company in real time.13

Still, the quality of data in HR continues to be a challenge. New cloud HR technology helps tremendously, but clients tell us the problem requires a systemic solution. Most companies must now worry about data quality at all levels, put in place privacy and anonymity policies, and carefully implement practices to protect employee data from theft and abuse. And many advanced companies now have governance teams that make sure all people-related data are coordinated as the company reorganizes, acquires others, and implements new systems.

Just as spreadsheets were once a tool of finance alone, but are now used throughout business, people analytics is making a similar leap. Businesses and organizations that are adopting analytics are bringing it into the core of their business and using it to inform business strategy.

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Jumpstarting people analytics

Deloitte’s research and consulting work have identified the following eight factors as important to creating a successful people analytics program:

  • Invest at a senior level in people analytics: The function should provide global support, not just technical analysis, and requires CHRO and senior executive support, technical resources from IT, and a strong business-focused leader.
  • Establish clear leadership: A single team and leader should own the initial stages of an analytics effort, even if that capability eventually becomes decentralized. 
  • Prioritize clean and reliable data across HR and the organization: Analyses are only as good as the data fed into tools and software. Working with consistent, timely, and accurate data is foundational to all analytics practices. Take concrete steps to ensure that data quality is a part of every analytics discussion. Educate HR’s stakeholders and implement data governance programs to clean and maintain data accuracy and consistency across HR and operational data stores.
  • Understand that analytics is multidisciplinary: Bring together a multidisciplinary group from across the organization, not just PhDs and statisticians. Technical analysis is only a small part of the function. Data function, data quality, business knowledge, data visualization, and consulting skills are all critical to success.
  • Increase analytics fluency throughout the organization: Regardless of whether the analytics customers do the analysis themselves or have specialists supporting them, training for both HR and other business functions will be critical to operating at scale. Identify a curriculum or other partner to help with education, implementation of standard tools, and standardization of reports and dashboards.
  • Develop a two- to three-year roadmap for investment in analytics programs: This investment is aimed at building a new business function for the company, not just a technical team within HR. 
  • Focus on actions, not just findings: To provide value, the analytics team must translate information into solutions, and stakeholders must take action.
  • Integrate HR, organizational, and external data: Advanced people analytics programs increasingly rely on the intersection of data from HR, operations, and external sources. Organizations require a data strategy that encourages the integration and use of structured and unstructured data from internal and external sources.

Over the next few years, the number of data sources will continue to rise, leading to a fusion of external and internal data in predicting employee behavior. At leading companies, analytics will become even more interdisciplinary, along the lines of ONA. Eventually, people analytics will be fully integrated into systems and always in the background, rather than a separate source of information.

Going forward, analytics technology will have the capability to deliver increasingly personalized recommendations. Due to the sensitive nature of some people analytics programs, organizations will likely need to become far more serious about data confidentiality, local regulation regarding the use of employee data, and the risk of public disclosure of private information on the organization and its employees.

*Adapted from “People analytics: Recalculating the route,” 2017 Deloitte Global Human Capital Trends.

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1 Global Human Capital Trends 2017: Rewriting the rules for the digital age, Deloitte Insights, 2017
2 Ibid
3 North American CFO SignalsTM, Q3 2016, US CFO Program, Deloitte LLP.
4 Executive conversations with the authors.
5 Ibid.
6 Ibid.
7 Josh Bersin, “Workday acquires Platora: Analytics race accelerates,” Forbes, July 25, 2016.
8 Peter A. Gloor, “What email reveals about your organization,” MIT Sloan Management Review, November 17, 2015.
9 Olivia Oran, “Wall Street hopes artificial intelligence software helps it hire loyal bankers,” Reuters, June 7, 2016.
10 Deloitte, “Deloitte analysis: Hidden labor expenses and ineffective labor utilization costing companies tens of millions of dollars,” press release, June 16, 2016.
11 Executive conversations with the authors.
12 Ibid.
13 Ibid.
14 Madhura Chakrabarti, Scaling people analytics globally: Chevron takes a multipronged approach to building organization-wide analytics capabilities (part I), Bersin by Deloitte, November 2, 2016

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