Augmented Intelligence
Sep 08, 2017

Beginning to converse with data in HR

Many of us, across companies, have begun to talk to data. The discussion is getting richer and more insightful every day, and is helping us to take better decisions and solve more complex problems. At Genpact, we have always had a data-driven culture, backed by a strong Lean Six Sigma legacy, and we are now finding that this culture of conversing with data can be hugely augmented with the recent advances in in analytics, machine learning and artificial learning (AI).

Here are examples of 3 insightful conversations with data.

Project 1: Differentiating between the traits of high performers

In today’s volatile and ever-changing business world, good managers are more than just ace performers on paper – they are people equipped with special skills to think on their feet, adapt to unpredictable situations and survive disruption. But how do we measure this? How do we gauge traits that are so subjective? And what are the traits that differentiate the ordinary from the great? In order to get answers to these questions, Genpact, with the help of a partner, did a study of a large data set of performance and psychometric traits of our front-line managers– and fed the facts into a machine. We found some clear traits that differentiated our best performing managers from our average managers. We bought learning modules on these traits and built them into the learning path of all new front line managers (FLMs). This project has helped us to focus our learning resources on the most important traits for success as an FLM.

Project 2: How do we identify our experts?

As we design and transform more and more work for our clients, it is increasingly important that the best minds across the company contribute to solving our clients’ most vexing problems. Given Genpact’s massive employee base, which is spread across 19 geographies, a problem often faced by our internal employees is – How do you identify the relevant expert in a particular field? And how do you reach out to them? To solve this problem, we first curated a list of experts in the system and then crawled a multitude of our systems to aggregate the key information related to them – like their area of expertise, total years of work experience, their current geography, past projects and a host of other data points residing in various disparate systems. The resulting information helped us create a master database with a lot of relevant content that our internal employees could leverage to identify an expert. The beauty of this solution was the speed – from concept to delivery within a quarter, at almost no cost. This could never have been done without the support of the new technologies that are now available. 

Project 3: Driving simplification and productivity in people processes 

We hire over 20,000 people a year and our hire rate is between 4-8% depending on the type of profile. So there is a lot of work that goes into screening and selection. Over the last 2 years we have been collecting a great deal of data on the hiring process (post the implementation of Taleo.) Now, we are starting to talk with the data to see how we can improve our process. One thing we discovered when we combined the selection and performance data sets is that a test that we had been leveraging to screen close to 20,000 associates each year could be removed. We found that there was no correlation between the scores on the test and the performance of people we had hired. It helped us to eliminate this step from our candidate screening process, thereby making our process more Lean, enhancing our candidate experience and of course saving costs. 

Scientists believe that 90% of all of world’s data was created in just the last few years. Given this incredible rate of data generation, it’s imperative that we leverage this wealth of information to make more informed decisions in the workplace. Fortunately, the cutting edge tools and technologies now available to us are making it even easier to store, process and analyze huge volumes of data.  If we can continuously overlay our domain expertise on statistical intelligence, we will most likely be able to identify and solve for problems that just a short while ago would have seemed impossible. We have just started our journey of conversing with the data sets that we at Genpact have been collecting over the last few years… and this journey will rapidly evolve. 

About the author

Amit Aggarwal

Amit Aggarwal

Senior Vice President, Training

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