The Rise of People Analytics and the HR Role
In God we trust. All others must bring data
~W. Edwards Deming~
The above quote from W. Edwards Deming, widely considered as the father of quality management, tells the importance of data and its value for making decisions. Data-driven insights have exploded in the last few years, but it has roots go back a long time ago. HR was not far behind in quantifying and measuring data according to Jac Fitz-Enz, a pioneer in the HR benchmarking. He talks about quantifying and measuring accurately the productivity of all major HR functional areas in his 1984 HR classic book “How to Measure Human Resources Management” (now into 3rd edition). The technological advancements, cloud-based HR systems, advanced analytics, AI, chatbots, VR, AR, gamification, automation, blockchain etc., have taken the HR world by storm and has transformed the way HR works. Of the many tools, we will discuss People Analytics and its impact on the organization. In their book "Competing on Analytics" authors, Tom Davenport and Jeanne Harris say “the organizations competing to identify, retain and nurture the right talent are looking for distinctive business processes as a point of differentiation this has shifted the spotlight to talent management and on HR Analytics”.
What is HR Analytics?
HR Analytics is the process of analyzing the available people-related data to measure the effectiveness of the HR programs and identify patterns in order to make meaningful business decisions. The advent in analytics has helped HR grow from being transactional and reactive to strategic and proactive, by helping to grow from basic reporting to BI tools with dashboards, and data warehouses to the advanced analytics. HR Analytics gained prominence as software providers like SAP, Workday, Oracle, UltiPro offered the HR Analytical tools with their HRIS offerings. The terms talent analytics, workforce analytics, talent analytics were often used synonymously. HR Analytics uses mostly people-related data i.e.., payroll, HR etc., and encompasses people-related data and the business operational data.
Different Types of Analytics
There are four broadly defined types of analytics as described by Gartner’s Business Analytics Maturity model in Figure 1 below.
Fig.1 Adapted from Gartner’s Data Analytics Maturity Model
- Descriptive Analytics: Considered as the foundation of the business intelligence, primarily focused on what happened for i.e., employee turnover, new hire report, time to hire, number of openings etc.,
- Diagnostic Analytics: Focuses on why did it happen? It takes a deep dive at the data to understand the causes of events and behaviors. For i.e., in the employee turnover, the diagnostic analysis would help identify the type of separations voluntary vs Involuntary and/or the regions or the business units to the actual location or by managers, to review the hiring process or onboarding process or even training the managers.
- Predictive Analytics: Iis the advanced stage of the analytics model and basis of the Big Data. The predictive analytics focuses on statistical analysis, forecasting, co-relations and build predictive models based on the historical people-related data, in essence, it’s a future-focused analysis that predicts the future patterns based on the historical data. For i.e., identifying the flight risk employees helps to reduce employee turnover and improve the bottom line, the other example is in hiring, identifying applicants with a propensity to join or who can be successful at the organizations, thus helping the talent acquisition team to fill the position quickly.
- Prescriptive Analytics: Considered as the future of the Big Data, prescriptive analytics focuses on prescribing potential actions to guide towards a solution. Prescriptive analytics uses machine learning, artificial intelligence to understand the impact of future and determines the best outcomes based on those scenarios, helping organizations to mitigate future risks. Prescriptive analytics is in its infancy, according to 2014 Gartner’s prediction, it would take 5-10 years before prescriptive analytics is embraced by all. Currently, it’s used widely in transportation, oil and gas industries, travel industries.
Areas People Analytics Used
People analytics has wide application within the HR and the business context and the areas of applications are likely to grow multifold in the years ahead. The growth of people analytics is global and not confined to one country. Figure 2 shows the percentage of respondents rating people analytics as very important or important is universally higher. According to Deloitte’s Human Capital Trends, the highest percentage comes from fast-growing economies, around 82%, and the lowest coming from France at 48%.
Fig 2. Adapted from People Analytics Recalculating the route 2017 Global Human Capital Trends
The areas of people analytics are wide within the organization from talent acquisition in identifying right talent to minimizing bias in hiring, employee retention, increasing employee engagement, measuring culture, workforce readiness, and employee experience to name few. For example, Google has turned people analytics into a winning culture with project oxygen. The tech giant has turned into one of the best companies for which to work, hiring the best, and retaining them with the highest engagement rate. Many sports teams use people analytics as well, the most famous being the Oakland Athletics. The team uses analytics to perform baseball player evaluations, which was well documented by Michael Lewis in his book “Moneyball”. New England Patriots, 5 time Super Bowl Champions, deploys people analytics extensively in selecting its players.
Should People Analytics Stay within HR
As an HR professional, I would shamelessly say it should remain with HR, but looking pragmatically and strategically the response is no. In a changing mobile world – millennial demographics, digitalized environment and exploding automation, organizations are looking for ways to innovate and prepare the workforce for the future of work. So, a network of teams is gaining popularity alongside the traditional team structures. Additionally, the people analytics in addition to the people/ HR data also combines the business data to get the groundbreaking new insights as shown in Figure 3. So, people analytics should be a shared responsibility with other business leaders.
Fig.3 Adapted from Bersin by Deloitte – The Geeks Arrive in HR- People Analytics is Here
How HR Can Contribute to People Analytics Success?
Focus on Right Problems: It’s absolutely important in understanding the business priorities, pain points and identifying the right problems. Work with the business leaders to identify the right problems.
- Build a Strong Coalition: People Analytics success depends and involves cross-functional teams and data across the operations, so, build a strong, cohesive cross-functional team that are skilled with the data function, institutional knowledge, data visualization and consulting skills, so that team becomes Organization’s Analytics Team
- Leadership Buy-In: People Analytics involves investment in terms of money, time and most importantly to execute the actionable insights that are drawn from analytics, the action matters most after the findings.
- Develop an Analytics Roadmap: Develop an analytics investment roadmap for wide spectrum of analytics
- Enhance Analytics Fluency: Understanding the complex data points, insights are critical for the right follow-up actions, so train, prepare group of important leaders and managers
- Avoid GITO: Ensure right process. ensure clean people-related data, because the analysis is as good as what goes inside, “Garbage In Toxic Out” leads to the wrong outcome and creates data havoc.
- Establish Data Strategy: People analytics rely on the intersection of data across the organization and external sources, so it’s important to have a data strategy to align/integrate the structured and unstructured data.
Baskaran Ambalavanan is a business-focused HR/Workforce Technology leader and is the Principal Consultant at Hila Solutions, LLC.