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Is People Analytics Reinventing The Current Workplace?

In the future, organization performance will be dependent on the capabilities of their people and the innovation they create.

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By the time you get to the end of this article, YouTube users would have uploaded 50 hours of videos, Facebook users some million pieces of content, Instagram users some 4000 photos and that is not even the beginning of the list for the massive amounts of information and data that is generated and shared each day. Data is no longer an abstract thing from which we can disassociate ourselves. It is a giant flood of bytes being carried by underground cables and wireless and becomes part of our everyday lives.

The situation is no different inside companies with similar reams of data available within and outside the organization. While the data includes sales figures, financial reports, operational parameters, the one pertaining to human resources is something new. The implications of this are dramatic because, suddenly, we have information available about people and the tools to analyze this data and apply it to various areas of the organization. While talent management has traditionally revolved around relationships and decision-making based on managerial experience, today there is an unparalleled opportunity to use data in the people function. Therefore, the question being asked today is more in terms of how we use this data to change the practice of human resources creatively and intelligently.

At first glance, analyzing and using people data is no different from handling sales or financial data. What makes it different is that the insights from people data are usually not linear and require deeper expertise and thought to derive meaning. Imagine being able to predict which of your employees have the strongest networks and can influence others; or which employee has the potential to be a sales leader and drive volumes; or what would be the impact of GDP change on your quarterly attrition; or which of your employees are at the greatest risk of getting poached by your competitor.

A growing number of organizations are now realizing that by applying analytic tools to talent data, they can make decisions that have a direct impact on revenues or margins. What has also helped this changed outlook is the fact that never in corporate history has talent been as critical as it is today and the heuristic or intuitive decisions that organizations and managers make about people no longer work.

Let us now look at how people data can be categorized and used. For the purpose of classification I have loosely labelled these based on the maturity levels of data usage, but it is not necessary that these levels be viewed linearly. It is perfectly possible for a single organization to be at more than one level at a time.

The first level of maturity is using your data for predictive analysis or forecasting. While this looks simple, turning HR data into meaningful forecasting is a relatively new field. For example, a problem statement in this area would be: “How do we use our future order data to evaluate the skills and capabilities in the current talent pipeline, forecast workforce needs, predict gaps and plan for recruitment?” Most of the work in the area is through mining historical data and then creating models to predict future outcomes. Since the field of deploying business analytics as a key part of human capital management is still growing, companies are seeing plenty of early benefits out of performing analytics even at the first level. There is immediate impact in areas such as reducing turnover, increasing engagement, and reducing costs.

The second level of maturity comes with using data for decision making. If you actually study many people decisions made in most companies, they are based on experience or “gut feel” and not on data. A query at this level would be, “What are the key levers in engaging and retaining our critical employees and what investments should we make in their development?” Another example would be, “How do we best choose the location of our future office based on the availability or performance of the talent pool around the region?” An interesting feature of this level is that there is a combination of experience and data that goes together in making the decision.

The third level of maturity comes when you use your data for engagement and ownership. The best organizations realize that people are a collective of individuals. When you can analyze people data at the n=1 level, you can truly engage and work with your people the way they want you to work with them. For example, at Infosys, we have used analytic tools to identify the strength of individual networks inside the company to create a dynamic group that we would like to identify for strategic assignments. In a similar fashion, an organization could use people data for succession planning or developing leaders.

At some point in time, it is expected that the data used in all three levels of maturity will go beyond organizational data and extend to personal data clouds with which each individual would be associated. Equally likely is the fact that analyzed data will be available and this could extend beyond the individual’s employment span in an organization.

There are also ethical & privacy concerns emerging from the way data is handled and interpreted. Today’s laws and practices are still evolving and we will need to wait and see whether this will be a constraint to how this develops. For the current purpose, an organization is best advised to place internal controls on how data is used and have an independent ombudsman to handle issues or complaints.

It needs to be understood that data analytics does not substitute for good people management. Talent analytics helps companies that are committed to strong people practices to do better. The goal should be to tell a better story based on facts or data. The ability to create clear boundaries for data usage and intelligent usage of what the analysis throws up is equally important. We all know that people do not fit into neat categories and neither is behavior fully predictable.

In the future, organization performance will be dependent on the capabilities of their people and the innovation they create. Each individual in an organization will have substantial amounts of data linked to them and the work they do. Organizations that use their data creatively to build and sustain their workforce will have a competitive advantage which will be extremely difficult to beat.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.

Tags assigned to this article:
workplaces human resources data analytics

Richard Lobo

Richard Lobo is a Senior Vice President at Infosys. He joined Infosys in 2000 and had lead several people management functions for the company. Richard Lobo is Head HR, Infosys Ltd. In his immediate previous role, he headed Human Resources (HR) for the entire Global Delivery organization. He has also headed the Employee Relations function for Infosys for several years. In his role, Richard oversees the functions of Business HR, Employee Relations, Geography-HR and HR Delivery for Infosys Ltd.

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