Q&A with Subrata Majumdar: Enhancing Workplace Experience with Data-Driven People Decisions

MathCo Team
April 13, 2023 7 minute read

It is now beyond dispute that advanced analytics and AI&ML technology have revolutionized resource management in organizations. These sophisticated algorithms can optimize utilization depending on every variable and greatly increase business value. A natural assumption to make would be that human resource (HR) management, or what is referred to as people science, would be exempt from this development—after all, human nature is too complex for HR managers to be able to predict behavioral trends and optimize performance. In today’s Q&A, Subrata Majumdar, Partner – Talent & Operations at MathCo, debunks this assumption by breaking down HR analytics and answering some FAQs on the subject to help explain how this tool is helping decision-making in HR and could make organizations more people-centric.

1. To start off, what is people science? What kind of companies can benefit from it?

Ans. Think of people science as first collecting and cataloging the data generated from an employee’s work and other organizational interactions. With such a dataset, enterprises can examine work performance trends, measure KPIs, as well as create models that help understand and predict employee behavior. This elevates workplace experiences and employee motivation, leading to better outcomes for the organization.

Investment in the right talent is a critical differentiator for companies of every size in every industry. While the keyword in people science is “people,” we should resist the temptation to categorize people-heavy organizations as the only beneficiary in adopting AI for talent management. With an appropriately motivated workforce, companies that build technology assets with comparably less employees, for example, can vastly improve the quality, reliability, and utilization of those assets.

2. What advantages does an enterprise stand to gain if it invests in making data-driven people decisions?

Ans. The impact of leveraging people science built on AI-based analytics can be broken down into a few components using the Autonomy, Mastery, Purpose framework created by Daniel Pink:

  • Employee motivation: HR leaders usually develop hypotheses on the motivations of their organization’s employees, based mostly on conversations and drivers like rewards and quality of work. However, this approach is unable to fully utilize all available people data, leading to inaccuracies in assessment and a broad-brush view of the motivations and goals of employees. People science provides the in-depth understanding required to understand employee motivation at a granular level and then create interventions that will be significantly more effective than the traditional approach.

  • Skillset mastery: According to Gartner, the number of skills necessary for a job has been increasing by 10% every year, while 1 in 3 skills have already become outdated [1]. Leveraging people science will help map out the secondary and tertiary skills, forming a full picture of the required key skillsets. A learning program that perfectly aligns with the company’s overall strategy can then be described. With this, the skill differentiator is an organization that can use to create leadership positions within their domain of business.

  • Predictive capabilities: Traditionally, HR leaders take a reactive approach to work situation. Often attributing this approach to the intrinsic diversity in people and their needs, interventions are implemented post facto. Leveraging people science in organizations can will mean reconceptualization of this approach, providing HR leaders with critical insights ahead of time, optimizing HR management.

3. The COVID-19 pandemic heavily emphasized the need to leverage AI&ML technologies to make business decisions effectively. How has this transition impacted human resource management at the workplace?

Ans. The pandemic forced almost every organization to drastically alter their methods of operation. HR was no different; in fact, given that employees were compelled to work remotely, HR found itself right in the middle of this change.

The first hurdle that HR faced during this sea change was the inability to observe colleagues, due to the adoption of remote or hybrid work models.

Secondly, HR leaders had to deal with the tectonic mindset changes in employees. In addition to the large-scale relocation triggered by remote work, there were significant shifts in employees’ motivations for selecting jobs, favoring those roles that served purposes of a higher order. Amidst all this, HR leaders also had to content with quiet quitting, a phenomenon that is almost impossible to discern.

Thirdly, and perhaps most significantly, there was a wide cultural gap between existing employees and the employees hired during the pandemic.

In response, forward-thinking HR leaders quickly established data strategies to bridge all these gaps. Data-driven strategies got them started on their people science journey, shifting the focus of the HR department from technology and policies to the people. This created the agility that was much needed following the pandemic in the way businesses responded to people challenges and extracted value from opportunities.

4. Let’s say I am a business leader who is convinced of the benefits of leveraging data to drive my employee decisions. What are the steps I should take to convert my organization’s traditional performance management system to a data-driven HR model?

Ans. While there is always a temptation to address only one specific HR function through the lens of AI—for example, performance measurement—I would advise against such an approach, as it results in silos. In the absence of the adoption of the same HR strategy in other functions, silo creation will result in suboptimal data utilization, leading to sub-par business outcomes for the organization.

The adoption of people science is a large-scale change management process and HR leaders must treat it as such. Investment in communication, awareness, empathy, and adoption are all aspects that must form part of the conversion strategy. Implementation of a data-driven HR model would ideally require change at four levels:

  • Data integration: The company should invest in engineering to gather and consolidate all people data in a modern, scalable repository, where data quality and timeliness would be closely controlled.

  • Functional integration: Every HR function—including employee policies, talent acquisition, rewards & recognition, and performance evaluation—must adopt a cross-functional approach to data utilization. This will ensure that the organization is aware of every employee touchpoint and use functionally integrated data to drive decisions.

  • Business reporting: Multiple forms of reporting, such as using spreadsheets, can easily be replaced by modern, on-demand, and data-science–friendly dashboards, to better suit the new HR model.

  • Advanced AI: Sophisticated technology helps improve the people science game to create HR interventions and initiatives proactively rather than post facto.

5. What KPIs should I rely on to measure the effectiveness of my data-driven HR strategy?

Ans. There are two ways to evaluate success of a data-driven HR transformation. The first is tracking its financial KPIs. The second hinges on the people aspect: here, firms must have quantitative “listening posts”, which are usually multi-dimensional studies that reveal the progress made by the transformation across multiple dimensions. These could be engagement metrics measured multiple times in a year. Additionally, since skill requirements undergo rapid change in today’s world, an organization’s capability index would also an important KPI to measure. This goes beyond hard skills to include next-generation leadership skills. There are, of course, traditional measures like attrition and reward benchmarks as well.

The most important success criterion for a data-driven HR transformation, however, is not a KPI; it is the permeation of a cultural shift throughout the enterprise—one which involves using people science to inform all people decisions across all organizational functions.

6. Finally, in light of the current economic downturn and the rising frequency of layoffs, what would you say to someone who is wondering whether “data-driven” in the context of HR equates to “less people-centric”?

Ans. The raison d’être of transformations in people science always is to become more people-centric, not less. As a result, any enterprise that successfully invests in people science becomes significantly more people-centric because of the transformation. This means that the above-mentioned concern stems from a fallacious perspective on the process and the expected outcome.



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