CXOToday has engaged in an exclusive interview with Aparna Hanumantu, AI and Analytics Leader Delivering Impactful Solutions Globally
Q1: How do you see the role of analytics in shaping the future of work, particularly in redefining workforce strategies to align employee performance with dynamic business goals?
Analytics is transforming workforce management, aligning employee performance with dynamic business goals. Five years ago, HR applications mainly stored employee data like joining dates, salary details, and leave records etc. Business goals remained consistent by large year after year, limiting the analytical capabilities of these systems. As data collection grew during the last couple of years, analytics expanded within HR, helping companies with recruitment, training, and employee sentiment analysis through surveys, optimizing productivity in real time. Historical data was monitored to improve training, skill development, and team matching recommendations for employees.
Today, with the rise of Gen AI and similar tools, tracking and monitoring productivity has evolved, processing data faster and offering deeper insights on employee’s performance aligned with business outcomes to leadership. Managers now have real-time access to reports, enabling data-driven decisions, targeted strategies, and better team alignment with changing business goals. This shift is not just about efficiency, but with the help of AI companies can personalize development plans, offer tailored feedback, and continuously support employee growth across all stages of their careers within the organization
Q2: What are the hidden costs of neglected or poorly managed data, and what strategies can organizations implement to effectively address them?
Data is often referred to as the new oil and is a key asset for almost every organization. Companies leverage this data to gain valuable insights, strategize day-to-day operations, and create personalized customer experiences. Data analysts examine historical data to extract insights, while data scientists apply these insights to machine learning or AI models to generate predictive results helping leadership in active decision making.
However, neglected and poorly managed data could lead to significant hidden costs, massive financial losses, legal liabilities and reputational damage for the company. For example, in the banking sector, imagine two teams running models based off the same data tables but they end up pulling from different sources leading to inconsistent and inaccurate intelligence reporting. This is going to have a major impact on end users, internal applications and finally on the leadership making active decisions on the reports that are produced ultimately putting the entire company at regulatory risk.
That is why it is crucial to handle data properly by ensuring that data quality, data security, and data governance policies are in place, making the data reliable, consistent across systems, along with proper data storage and integration framework from various sources to ensure effective data across departments through cross functional team collaborations.
Q3: How is artificial intelligence transforming traditional leadership roles by empowering decision-makers to prioritize strategic vision over operational tasks?
An organization with strategic vision can help providing direction with clear goals with a sense of certainty even in uncertain times making informed decisions that support long-term success.
During the last five years, with the advent of analytics evolving, companies can make quality predictions and forecasts with greater accuracy. By using advanced data models and machine learning algorithms, companies can now forecast key metrics such as the number of units they can sell or the number of customers they can acquire. Unlike relying on gut feelings or intuition, companies can base their decisions on data-driven insights, providing more reliable and actionable estimates.
Historically, analysts used statistics to summarize data and report key trends, but these methods couldn’t handle large volumes of data, limiting power of advanced analytics reporting. With AI, organizations now thrive on big data, using machine learning to track patterns and rules that traditional models couldn’t process earlier. Analytics help companies in day-to-day operations by optimizing workflows, tracking performance, and enabling real-time decision-making.
While data science and AI once required specialized talent such as PhDs or data scientists, with the advent of Gen AI, managers at all levels have access to powerful AI-driven tools at their fingertips, enabling them to make quick, data-driven decisions effectively.
Q4: What strategies can leaders employ to drive the cultural transformation needed for organizations to fully integrate and embrace analytics?
Traditionally, companies used to focus on presenting employees with problems and asking them to find solutions without much emphasis on data-driven decision-making. However, with the advent of analytics, companies are now shifting towards creating environments where people feel comfortable making decisions based on data, encouraging them to take analytical approaches towards problem-solving.
Companies are treating data as an integral part of their operations, helping to drive day-to-day activities, decision-making, and enhance customer experiences. As the next step in cultural transformation, companies are providing employees across all divisions such as marketing, finance, and operations with access to data and analytical tools, empowering them to make data-driven decisions rather than relying solely on intuition or experience. Companies also recognize that tools just alone aren’t enough and hence are also actively investing in training and development to build data literacy across teams. This combination of tool access and training reinforces a shift toward a culture where analytics is viewed as essential for driving cultural transformation for business growth and innovation
Q5: How analytics can drive small, daily decisions that collectively lead to significant organizational impact.
Traditionally, companies measure their business impact or success through large, high-level outcomes across divisions, primarily focusing on key milestones or annual results. However, in today’s data-driven era, companies are shifting their focus to smaller, daily decisions that collectively lead to significant and sustained business impact. By leveraging real-time insights from advanced analytics tools, companies can fine-tune their operations and monitor performance at granular levels, making informed adjustments on a day-to-day basis. These incremental improvements, driven by data across various teams and departments, not only contribute to overall growth, but also enhance efficiency and innovation, demonstrating that even the smallest actions, when guided by analytics, can have a profound organizational impact.
For example, banks might monitor the real-time customer behavior data and identify patterns such as frequent withdrawals or spending habits and through small, data driven adjustments, they can offer personalized financial advice or targeted promotions on daily basis. Over time, these incremental actions can lead to higher customer satisfaction, improved loyalty, and increased cross-selling of products ultimately positioning the bank more strongly in the competitive market.
Q6: What are the key strategies for designing analytics frameworks that adapt and stay relevant amid rapid technological advancements?
Traditionally, companies relied on process frameworks such as Lean, Six Sigma, and other quality management systems to streamline operations, optimize workflows, and improve efficiency in order to stay ahead in the market. However, today organizations are increasingly adopting analytics frameworks to make real-time, data-driven decisions, optimize customer experiences, and identify emerging market trends to maintain their competitive edge. Frameworks such as business intelligence (BI), customer segmentation, and predictive analytics provide deeper insights into consumer behavior, operational performance, and market dynamics, enabling companies to respond swiftly to changes and sustain a strong position in competitive markets.
To design analytics frameworks that remain relevant amid rapid technological advancements, companies must prioritize flexibility, scalability, and continuous learning. Adopting modular frameworks will enable companies to ensure their analytics systems can evolve alongside innovations in AI, making it easier to integrate new technologies and tools as they emerge. Fostering a culture of continuous learning among employees ensures they are well-equipped to leverage new tools and techniques as they become available in the market. By embracing cloud-based, scalable platforms, organizations can quickly update their systems to incorporate the latest technological advancements without overhauling the entire infrastructure.
Q7: How can organizations leverage data-driven strategies to gain a competitive edge in highly saturated markets?
Earlier, companies stayed ahead in the market by relying on established business practices such as product differentiation, strong branding, and operational efficiency, often focusing on maximizing profitability through traditional strategies like cost-cutting, market expansion, and customer loyalty programs. In contrast, today’s companies leverage data-driven strategies to gain a competitive edge in highly saturated markets. By utilizing advanced analytics, machine learning, and real-time data insights, organizations can better understand consumer behavior, optimize marketing efforts, personalize customer experiences, and predict market trends and future shifts allowing them to make more informed decisions, adapt quickly to changing market conditions, and innovate more effectively and stay ahead in a fast-paced business environment.
For example, in the banking industry, AI and analytics are increasingly being used to improve both security and customer experience. A fraud detection model powered by AI can analyze vast amounts of transaction data in real time to identify suspicious activities, helping to reduce fraud rates. Banks are also leveraging AI-driven chatbots to provide exceptional customer service, enabling customers to quickly find information on their accounts and resolve issues independently. By utilizing big data, banks can also optimize spending by analyzing customer habits, offering personalized spending tips, and identifying opportunities to reduce unnecessary expenses, ultimately improving both customer satisfaction and operational efficiency.
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