In today’s dynamic landscape of HR, how are data-driven strategies reshaping the way organisations approach talent acquisition and employee engagement? How crucial is the role of HR analytics in helping young organizations make data-driven decisions for growth?
Divya Mohan, Chief Human Resources Officer at InsuranceDekho shares her expert take. With a distinguished career spanning over 16 years, she orchestrates the entire spectrum of HR functions, encompassing talent acquisition, business partnering, learning, compensation and benefits, HR analytics, and payroll. Her professional journey has seen her steering talent planning in diverse, high-growth environments and nascent enterprises. Prior to her tenure at InsuranceDekho, Divya held pivotal roles at major conglomerates and startups, including Mahindra & Mahindra, Jubilant FoodWorks Ltd., VLCC Healthcare Ltd., Volvo-Eicher, OYO, and Innovaccer Analytics.
In conversation with People Matters, she shares her incredible learnings on how organisations can unlock the power of data-driven decision-making, ignite hiring success with analytics, strategically balance tech and touch and more. Here are some key excerpts from the interview.
In the context of young organisations, how do you envision the role of HR analytics in shaping strategic decisions and driving growth?
Young organisations indeed hold a significant advantage. They're essentially constructing their entire framework from scratch. This means that when data takes centre stage in decision-making, they can build their organisation in a way that aligns perfectly with their goals, all backed by solid data. So, being young sets you up for a habit of making decisions powered by data, which is a tremendous advantage.
Now, looking at it from an evolutionary standpoint, newer organisations are stepping in and taking over as soon as they start incorporating data into their daily operations. A few decades ago, the kind of focus on data that we see today simply wasn't present. For older organisations, this shift represents a learning curve. That's why we're witnessing the adoption of tools around data analytics, chatbots, and AI. This integration is happening because they recognise the significance of keeping up with today's data-driven landscape.
For emerging businesses new to HR analytics, what initial steps do you recommend to establish a strong foundation for collecting and analysing workforce data?
In my experience, I've noticed a critical factor: it's not just about having data; it's about having the right data and knowing what you want to do with the data. Analytics can easily go off track if your data isn't accurate or if you're inputting one thing and expecting something entirely different as an outcome. Here are some steps I would recommend:
- Ensure data accuracy and understanding of the information fields collected by using appropriate tools and having a clear comprehension of data collection.
- Define the purpose of data analysis, especially in demographic analysis, to avoid creating misleading dashboards or management information systems.
- Understand the specific cuts or segments of data you're interested in to effectively analyze and draw meaningful insights.
- Avoid the common pitfall of creating dashboards or systems without a clear purpose, as this can lead to misleading conclusions.
Let's take an example: If I want to foster a particular culture or behaviours within my company, I should kickstart the process with data. What's the current situation? Are there any discernible trends? We can examine factors like attrition rates and leading indicators such as engagement and pulse scores, collected through various employee communication channels. This data becomes the foundation for designing and implementing an intervention.
So, my strong recommendation to cultivate a data-driven culture is that every leader should commence any brainstorming session with the team by first examining the data. This holds true whether you're planning for the short term, the long term, or anywhere in between.
How has AI and HR analytics transformed your approach to talent acquisition? Can you share an example of where data insights led to successful hiring decisions?
- AI-Enhanced Hiring Processes: AI has revolutionised hiring processes, especially in scenarios involving mass and niche recruitment. Take video interviews, for instance. Recruiters are no longer required to oversee these sessions. Algorithms, pre-set with specific talent markers, streamline the screening process, enabling lean and efficient operations on the AI side.
- Shaping Success Profiles through Data Analytics: Moving to the data analytics front, it plays a pivotal role in shaping success profiles. Our experience often tells us that certain personality types, educational backgrounds, or demographics tend to perform better and others may take longer to settle in. Data substantiates these insights, providing a clear trend on what kind of experiences and people align best with the organisation.
- Minimising Dropouts in Hiring: Digging deeper into hiring metrics, organisations aim to minimise dropouts, a costly and time-consuming setback. By analysing data on offer dropouts, we can gain valuable insights into the reasons behind them. This informs strategic decisions to refine the hiring process, reducing dropout rates and enhancing the joining experience for candidates.
- Employer Branding through Data-Driven Insights: Beyond recruitment, data extends its impact on employer branding. Surveys shed light on how the brand is perceived, helping identify strengths and areas for improvement in the Employee Value Proposition (EVP). Data-driven insights empower leaders not only to address challenges but also to amplify existing strengths, creating a well-rounded approach to organisational growth and efficiency. In essence, data analytics serves as a dual force—rectifying weaknesses and fortifying established successes.
Employee engagement is crucial for organisational success. How do you utilise HR analytics to identify engagement drivers and tailor initiatives that resonate with the unique needs of a young workforce?
Engagement strategies have undergone an evolutionary shift, moving from once-a-year surveys to more agile and frequent pulse surveys, driven by chatbots. This allows organisations to gain real-time insights into employee sentiment. Distinguishing between static engagement scores and pulse data has become crucial, recognising that waiting months for feedback is no longer necessary. By slicing and dicing data over various timeframes, from daily to yearly, organisations can discern short-term engagement drivers from those that hold sway over the long term.
In our current phase of growth, we're meticulously building the company brick by brick. This affords us the opportunity to implement a range of interventions. We're keenly focused on understanding different employee cohorts, their expectations, and their perception of the evolving company culture. This insight is gathered through one-on-one interactions, surveys, and even simple tools like our HRMS's Mood-o meter, which provides daily snapshots of employee sentiment. These structured interventions also include focused group discussions, onboarding surveys, and engagement surveys. This multi-pronged approach ensures we capture a comprehensive view of the organisational pulse, which, coupled with personal experiences and insights from managers, HR, and peers, informs our decision-making.
It's worth noting that while data is invaluable, it's not the sole driver of decision-making. Recognising the human element and the myriad biases that come into play is crucial. Data provides a framework and a clear reference point, offering a black-and-white perspective. This, in turn, allows for a holistic approach, where wisdom, personal insights, and data converge to facilitate informed decision-making.
Creating a fair and data-driven culture is integral. What best practices can you share for integrating analytics into the daily workflows of HR and other functional teams to strengthen this?
- Establishing a data-informed and fair culture starts with a clear definition of "fairness". Take diversity and inclusion, for instance. If these are identified as top priorities, it's imperative to precisely articulate what inclusion means within the organisation.
- Determine methods to measure the presence or absence of inclusion for ongoing evaluation.
- Focus on defining priorities and setting measurable benchmarks as foundational steps.
- Incorporate personal insights and conclusions from leaders, peers, and team members alongside data analysis. It's the synthesis of these perspectives that contributes to the overarching goal.
Looking ahead, how do you see the role of HR analytics evolving, and what steps are you taking to ensure your HR team remains at the forefront of this transformation?
There's no denying the impact of data analytics on decision-making and organisational success. It's a tool that reinforces the efficacy of interventions, assuming you're measuring the right elements. However, an essential mindset shift is required. We must embrace technology while simultaneously acknowledging its limitations. The key bias to overcome is the misconception that technology and analytics are infallible, when in fact, they are not. They come with their fair share of "ifs" and "buts."
So, beyond the technical aspects, there's a crucial need to nurture a mindset within the organisation—one that wholeheartedly accepts the power of analytics, data, and technology and also the indispensability of human judgment. This shift is paramount. Additionally, defining your objectives and what success looks like is fundamental. Establishing clear metrics allows you to gauge progress and proximity to your goals. The continual discourse around these metrics ingrains data-driven thinking into the organisational DNA.
We are focusing on constant upgrading of our team to understand the value analytics can bring across the domains of hiring, employee experience, performance management, attrition and reduction of NVAs. Our team works not just on analytics but also on visualisation software which helps in presenting data in an ingestible form.