Implementing a sound data and analytics strategy is a continuous learning process: Sonal Jain

Implementing a sound data and analytics strategy is a continuous learning process: Sonal Jain

Sonal Jain, Enterprise Head HR and Consumer Health Head HR, Johnson & Johnson India, shares how HR functions can nurture a data mindset and leverage people analytics to extract meaningful, value-adding insights. She also talks about mitigating typical AI biases and how data and intuition can complement each other for sound decision-making.


HR functions have a goldmine of people-related data at their disposal. What are some successful applications of people analytics that you’ve seen in the wake of the pandemic-induced disruptions? Could you share some examples?


The HR Function is an accelerator and catalyst of transformation. By using employee data and insights, HR can enable and integrate business and talent strategies for long-term business success. The new ways of working and collaboration in the wake of COVID-19 provided us with valuable insights and learnings about where and how work happens. We continuously review, evaluate, and adapt policies to maximise productivity based on data and people analytics.


At the onset of the pandemic, Johnson & Johnson started conducting frequent rounds of quantitative and qualitative data analyses to better understand the changing needs of employees. We leveraged these analytics and insights to respond to employee needs and deliver new benefits/expand current benefits (such as caregiver leave, extended sick leave, more flexible working arrangements, ergonomic home furniture, home exercise equipment and wellness subscriptions). Additionally, the caseload on our query management system increased multifold. Hence, we could source vast amounts of data across relevant pools of employees, functions and sectors. We also frequently conducted pulse surveys and hosted interactive virtual town halls to gather and listen to employee feedback, which helped shape our overall approach and response to the new ways of working.


A key focus has been to nurture a data mindset within the HR function to harness the benefits of analytics as part of our ongoing talent lifecycle management process. We leverage data insights from external platforms to bring external talent landscape and insights to our hiring process. Using data analytics for key metrics such as candidate experience, hiring manager experience, and turnaround times across different levels and touchpoints has provided us with deeper insights to enhance the overall experience for both candidates and managers.


Efficient use of data analytics has also helped us analyse attrition trends for further management action. Another crucial aspect includes a better understanding of employees’ learning and developmental needs, which has helped us introduce in-house training and partner with platforms like LinkedIn Learning.


We also use our Reward & Recognition platform ‘Inspire’ to analyse recognition trends, nurture a culture of peer and stakeholder recognition, and celebrate wins. It’s an interactive recognition platform for every employee that recognises significant and outstanding contributions to the organisation.


There’s an adage that says there’s no such thing as a stupid question. However, when it comes to analytics, ill-conceived questions might lead to a time-consuming, costly data exploration process that yields no meaningful insight. What makes for a good question, and how can HR and business professionals query people-related data to extract meaningful, value-adding insights?



Implementing a sound data and analytics strategy is a continuous learning process. A good HR question is aligned with the overall business strategy and also enhances the people experience. Therefore, accurately defining the problem is key.


For example, when it comes to strengthening employer brand, it is critical to define what and where we need to focus given the competitive talent landscape. This can only be done through insightful and qualitative data mining using tools coupled with traditional methods like focus group discussions, perception studies, data from social media platforms like LinkedIn, Glassdoor, Quora, and other key historical data sets such as attrition analysis. All these factors together play a crucial role in developing a robust analytics-powered HR strategy.


Data algorithms can be impacted by the personal prejudice and biases of those who build them. What are some typical biases of artificial intelligence, and how can they be mitigated?


A common AI bias is data bias, as output from machine learning tools is determined by the quality of data set inputs. Therefore, it is crucial to ensure that the overall data collation and input mechanisms are as neutral and bias-free as possible – and this is a learning process. We are learning that AI systems tend to disregard input variables that may exist but are not available; these do not help accurately predict outcomes (in the data available to them). This is in contrast to humans. While using AI to improve decision-making will benefit multiple areas, there are no quick fixes to AI biases. However, research and increased usage are leading to rapid progress in this area.


A positive example of AI in action is Textio – an augmented writing platform for job postings that uses a pioneering AI engine providing real-time guidance on position enhancement and removing human biases. The platform helps create neutral and inclusive job descriptions.



Many HR professionals struggle to use analytical tools and techniques in making business decisions. How can HR executives become more data-savvy, and what would you say to HRs passionate about people management but not as enthusiastic about data and analytics?


In order to help HR executives become more data-savvy, we have undertaken a three-pronged approach:


• Exposure: Organisations would do well to equip HR executives by providing the right exposure and access to digital tools, not just in HR but also in areas of business where we connect with consumers and customers. For example, at Johnson & Johnson, we introduced tools such as Workforce DNA, Diversity dashboard, and Tableau for data visualisation.


• Education: This is an ongoing process, and an introduction to analytical tools and techniques needs to be built out via current educational streams for executives and employees. This could range from online courses, real-time use of tools, or intentional learning programs to build knowledge and proficiency in digital agendas.


• Ecosystem: Creating an ecosystem that incorporates analytics not only within the HR function but across the enterprise requires a strategic mindset. Encouraging data-backed discussions, creating platforms for sharing internal and external best practices, etc., are all essential elements of the wider digital transformation process. Most importantly, ensuring analytics is deeply rooted in the organisation’s business strategy is the goal. To become a partner/ enabler to our business leaders, we at Johnson & Johnson HR strongly focus on developing the required business acumen to make decisions based on accurate insights, which is key to building credibility with business functions.


Should business leaders base their decisions purely on insights provided by data analysis? Or do you believe both data and instinctive intuition complement each other in providing valuable information?


At the start of a decision, intuition can be perceived as unreliable. The patterns identified in the past might not apply to the present. After one has weighed the pros and cons of the decision, intuition becomes powerful. It adds valuable information that your analysis might have missed.


Nobel Prize winner Daniel Kahneman said, “Don’t do without intuition; just delay your intuition.” Data provides the landscape, but marrying it with human experience and insights (from our experience and feedback from consumers, employees, partners, etc.) helps build a robust decision-making process within the organisation. In the end, our role is to identify and trace the unseen patterns in the data that we obtain and use that to shape policy for the future.


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