In Conversation with Prabir Jha on Big Data Analytics


Big data, IOT and HR Analytics are three buzzwords and have great significance in Human Resources; we are privileged to have with us Prabir Jha, Global Chief People Officer at Cipla, for detailed discussion on Big Data and its applications on Human Resources.

Prabir has been the CHRO at two NYSE-listed Indian majors, Dr.Reddy’s and Tata Motors, a Fortune 500 company. Prior to his current assignment he was the Group CHRO at Reliance Industries Ltd, a Fortune 100 conglomerate and India’s largest private sector company.

He is one of the most widely followed CHRO’s globally with over half a million followers on LinkedIn and a consistent LinkedIn Power Profile.  He is a sought-after global speaker, executive coach and a visiting faculty at various business schools and top management training institutes.

Prabir has consistently been featured in the “List of Most Powerful HR Professionals in India” and has several awards to his credit. Among others, he is the recipient of the National Institute of Personnel Management (NIPM) Medal, the Reckitt & Colman Award and the Citibank Leaders Award at XLRI, for the “highest level of academic performance, competence, originality, creativity, communication skills and leadership” as also the Director’s Medal in the Civil Services Foundation Course. He was conferred the Asia HRD Award – 2012 for “outstanding contribution to the field of Human Resource Development” and “The Distinguished Alumni” Award at XLRI in 2016.

Q- What is Big Data to you and how big data applies to Human Resources?

Big data refers to massive and exponentially growing quantum of employee, customer, and transactional data available in organizations. In the case of HR, organizations have huge stated and unstated data of talent or people-related information (such as skills, performance ratings, age, tenure, safety record, sales performance, educational background, manager, prior roles, psychometric profiles etc.) which can be used to better understand the organization’s current composition, performance, and risk as also to improve the development of employees, products, and services. Big Data in HR sets to evaluate and improve practices including talent acquisition, development, retention, and overall organizational performance. This involves integrating and analyzing internal metrics, external benchmarks, social media data, and government data to deliver a more informed solution to the business problem facing your organization

New tools and technology options are needed because big data is so big, fast changing and potentially unstructured. With these tools, HR organizations are able to perform post activity analytics and increasingly predictive or forecasting algorithms.

These can dramatically help to make smarter and more accurate decisions, better measure efficiencies and identify management “blind spots” to answer important questions regarding workforce productivity, the impact of training programs on enterprise performance, predictors of workforce attrition, and how to identify potential leaders. The ability to capture and analyze big data has enabled many companies to both increase revenues by better understanding and more accurately targeting customers and cut costs through improved business processes

Q- What are HR Big Data trends in current scenario?

Big Data can help answer fundamental questions facing organizations today:-

  • Are we hiring right? Whom to Hire?
  • Whom to Promote?
  • Are we training right? Whom to Train? What to train on?
  • Are we rewarding right? Who should be ring fenced? Predicting possible attrition.
  • Organisation Design & People Productivity

It is important not to confuse Big Data analytics with mere automating historical practices. The idea is to know what makes most strategic impact to the corporation. And to collate data, generate hypothesis and offer sharp insights. This is most critical to be mindful of when we start on People Analytics. Otherwise the maze can make the best lose their way.

Q- How Big Data and Analytics are transforming Recruitment and other HR functions?

 In recruitment, Big Data is being leveraged in following areas:-

  • Predictive model of successful recruitment
  • Internal placement (cost savings) basis talent match
  • Inclusion and Diversity prediction model

When you mine historical data, there are sharp and often counter- intuitive insights. It can link hiring decisions with performance and stickiness. It can enable very sharp segmenting of the catchment of identified talent, improving recruitment metrics and on job performance. This can build for tremendous Organisational effectiveness.

Q- How Big Data can help Employee Engagement & Retention?

In Employee Engagement & Retention, Big Data is being leveraged in following areas:-

  • Identification of training basis successful candidates required at time of onboarding
  • Data driven onboarding track
  • Unscheduled absenteeism risk
  • Attrition Model (Health attrition vs. Retention model)

The underlying assumption is that employee demographics is very differentiated. Not everyone learns or feels motivated the same way. Hence, cookie cutter approaches never work. By looking at data sets, one can hone the relevant preference patterns and plan interventions and investments that get the best value and impact.

Q- How Big Data Analytics is a big boon for L&D and Employee Performance?

In L&D and Employee Performance, Big Data is being leveraged in following areas:-

  • Skills and Qualifications needs basis performance
  • Leadership success model
  • Performance based reward prediction model
  • Cost predication model for all levels of organization

A lot of contemporary trends run counter to historically held leadership beliefs and assumptions. There is a huge linkage between organisation design, manager profiles, job experience combinations to both learning and performance. Big data analytics can help design the structures better, improve the quality of team composition (beyond education and years of experience!) and re-shape both the “what” and “how” of learning.

Q- How can an organization transform its culture to be more data driven?

Organizations can transform its culture to be more data driven by embracing a digital mindset and by leveraging analytics. Analytics problems should be Business focused, with a clearly defined strategy and outcome. It is important not only to get the methodology right but also leverage storytelling to compel an actionable outcome.  Analytics should put employees at the center and should be looked at as a long-term investment. Hypothesis must get validated by data. The culture thus has to move away from a hierarchical eco/system to an “information democracy” with far greater transparency. This will need a more open and transparent culture. And this is where organisations fail most as the culture does not change enough to support the technology enablement of a digital organisation.

Q- What are the challenges that organizations are facing, while moving to data driven decision making?

 Not embracing a digital mindset, lack of clearly defined strategy and outcome, expecting quick results through investments made are typically the biggest roadblocks which organizations face today. These tend to rely more on intuition rather than data or trends. Patience, perseverance and the inability to withstand the bulwark action by the older power blocks are other typical challenges. Inadequate sponsorship by the Chief Executive also has huge challenges in the migration to the New World.

Q- Any concluding remarks?

While People Analytics is more complicated than finance or supply chain analytics which have numbers at the forefront, it is important to not forget the ‘H’ in HR while pursuing Qualitative and Quantitative research in this ‘people centric’ domain. There will always be some subjectivity and the need for informed judgment while basing ourselves on data analytics. The marriage between the left and the right brains is the only way the world will go with time. Any angularity is not only half-baked but potentially derailing.

Thank you Prabir!


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