Powering India's Data Future: A Candid Conversation with Pranoti Deshmukh of IndiaDataHub

In the ever-evolving world of data and analytics, IndiaDataHub is making powerful strides to redefine how data is accessed, analyzed, and utilized across industries. Known for its robust platform housing tens of thousands of time-series datasets, the company is on a mission to make data-driven decision-making more intuitive and effective.

In this exclusive conversation, Faiz Askari, Founder of SMEStreet, sits down with Pranoti Deshmukh, Co-Founder of IndiaDataHub, to discuss the company's evolving role in India’s data ecosystem, its strategic collaboration with MongoDB, and the game-changing impact of AI in shaping data accessibility and innovation.

Exclusive Interview:

Faiz Askari (SMEStreet): Pranoti, could you start by telling us more about the core offerings of IndiaDataHub?

Pranoti Deshmukh: Absolutely. IndiaDataHub is a comprehensive data platform designed to empower businesses, investors, and policymakers. We provide access to an extensive collection of time-series datasets covering a wide range of sectors like banking, infrastructure, energy, agriculture, climate, and more. Our data is granular—available at national, state, and even district levels. This allows for tailored analysis supporting everything from financial forecasting to strategic decision-making.

Faiz Askari: Your recent partnership with MongoDB has generated a lot of buzz. What are the key objectives behind this collaboration?

Pranoti Deshmukh: Our main focus with MongoDB has been improving the data discovery and search experience on our platform. As our catalog grew, finding the right data became more complex. So, we implemented MongoDB's Vector Search—a cutting-edge AI-driven technology—to help users find semantically relevant datasets quickly. We’ve also applied Retrieval-Augmented Generation (RAG) to refine this process further, making it possible to surface highly relevant indicators, like the top five among 1,000 inflation metrics, with up to 95% accuracy.

Analytics and Visualization

Faiz: That’s impressive. How does this technical backbone support your analytics and visualization goals?

Pranoti: We’ve streamlined our backend operations using MongoDB Atlas, ensuring our data pipelines are both scalable and cost-effective. It’s especially helpful when processing unstructured data like PDFs of mutual fund portfolios. We’ve also significantly enhanced dashboard performance—reducing latency from 2 seconds to just under 200 milliseconds. All of this contributes to a much smoother and faster user experience.

Faiz: Which industries do you think will benefit the most from these advancements?

Pranoti: Definitely the investment and financial services sectors. Asset managers, banks, mutual funds—they all need real-time, actionable insights. Beyond that, corporates across industries can leverage our data for strategic planning, market expansion, and benchmarking. For instance, a marketing team could analyze consumer behavior trends to launch more targeted campaigns.

AI-Powered Data Management

Faiz Askari: What unique challenges did you face in the context of AI-powered data management?

Pranoti Deshmukh: Two major ones: handling multi-dimensional, often unstructured data, and improving discoverability of that data. Traditional databases struggle with the former. MongoDB’s flexible schema design and AI search tools helped us overcome both. We’re also working with them to bring AI into our data quality workflows—automating issue detection and streamlining data sourcing.

Faiz Askari: Could you share any early success stories or promising use cases from this partnership?

Pranoti Deshmukh: One great example is our mutual fund dataset project. We’re building analytics at both aggregate and instrument levels—something investors find invaluable. Curating this from unstructured PDFs is tough, but MongoDB helped us automate much of the process. With our new vector store, users can pinpoint relevant data without digging manually. In the future, we aim to introduce sentiment indices and a full-fledged research copilot, powered by MongoDB’s AI suite.

Faiz Askari: Exciting times ahead! Thanks for sharing these insights, Pranoti. Any closing thoughts for the SMEStreet readership?

Pranoti Deshmukh: I’d just like to say that data is no longer a back-office function—it’s central to decision-making. Whether you’re an SME, a large corporation, or a policymaker, leveraging structured and unstructured data effectively can be your biggest competitive edge. We’re excited to be part of that journey.