Masterminds - When Data Becomes Your Competitive Advantage | Shantanu Prakash скачать в хорошем качестве

Masterminds - When Data Becomes Your Competitive Advantage | Shantanu Prakash

In this latest Masterminds episode, Success Alchemists’ Nimisha Sainani sits down with Shantanu Prakash, Director of Data & Analytics at CashKaro, to explore how data transformation is reshaping business growth. Shantanu shares why customer segmentation, clearly defined KPIs and a robust data pipeline are foundational to turning data into meaningful business outcomes. He also discusses the evolving impact of AI on data teams and how organisations can move beyond dashboards and reports to drive sharper, more informed decision-making. Shantanu brings a thoughtful blend of analytical depth and leadership insight to the conversation. With extensive experience in building scalable data ecosystems, he highlights why data governance, centralised data repositories and the right operating model are critical for long-term impact. His perspective reinforces a key theme of the discussion: data-driven cultures are built top-down, where leaders ask better questions, avoid vanity metrics and create psychological safety for teams to experiment, reflect and innovate. Through practical examples and candid insights, Shantanu offers a clear, future-ready view on how businesses can leverage data as a strategic advantage rather than operational noise In the full podcast episode, they explore: ✅ Why clearly defined KPIs are critical for business alignment and scale ✅ The importance of centralised data pipelines for growth ✅ How AI is reshaping data teams beyond service functions Inspired by our previous webinar, we're diving deep into how to future-proof your brand and stay ahead of the curve. Subscribe to Success Alchemists for more insights on scaling up your business. Chapters: 00:00 The Power of Data in Business Transformation 08:56 Building a Robust Data Pipeline 17:46 KPIs and Team Structure for Data Success 26:34 AI's Impact on Data Dynamics 33:19 Common Mistakes in Data Utilization