It’s not that we use technology, we live technology. With Google’s new googly on third-party cookies brands, particularly in consumer-facing sectors like CPG, are increasingly investing in cohesive data infrastructures to adapt to the new privacy-centric environment. This shift is reshaping data investment trends, particularly in markets like India, where traditional approaches to data management may not suffice.
In conversation with BrandWagon Online, Sachin Chawla, vice president, India and South Asia, MongoDB shares insights into how the company is addressing these shifts. As organisations of all sizes—from startups to large enterprises—embrace modernisation, MongoDB claims to be at the forefront of this transformation. Chawla discusses how MongoDB supports diverse clients, from digital natives like Zomato and Zepto to large enterprises such as Tata Digital and Adani. They also elaborate on MongoDB’s role in helping companies build a unified customer view through its flexible, scalable data solutions. As the demand for innovative and agile data solutions grows, MongoDB continues to drive digital transformation across industries, providing insights into how businesses can stay competitive and efficient in a data-driven world. (Edited excerpts)
Many brands may not have invested in cohesive data infrastructure, especially in sectors like CPG and other consumer-facing industries. How do you see this shift impacting data investment trends in India, especially for companies that don’t traditionally handle extensive first-party data like banks or fintech firms?
To begin with, we cater to a diverse range of customers across industries and business sizes, from startups to large enterprises and independent software vendors (ISVs). Let me give you some examples. Among the digital natives, we have notable clients like Physics Wallah, Zomato, and Zepto. For instance, every time you order food on Zomato and track the rider, much of that backend is powered by MongoDB, handling millions of requests in the database. Zepto, a rapidly growing quick commerce company, also builds much of its infrastructure on MongoDB. They even spoke recently at our local tech event in Bangalore about scaling their platform with MongoDB.
On the enterprise side, we work with companies like Tata Digital. Their loyalty app, Tata Neu, unifies loyalty programs across 40 Tata brands, including hotels, airlines, and electronics, allowing customers to earn and spend ‘NeuCoins’ seamlessly. This super app is built on MongoDB. Similarly, Adani’s airport app, used across seven airports, allows customers to book lounges, shop, and manage travel. We also support major banks transitioning from legacy databases like Oracle and SQL to MongoDB, modernising their operations.
Finally, we have ISVs, companies that create their own software products. A great example is Darwinbox, an HRMS platform built entirely on MongoDB. Our database serves as a horizontal, general-purpose solution, supporting a wide array of use cases across industries and customers.
Data is often equated with marketing. How does MongoDB help marketers better understand the consumer journey and make more informed decisions?
This is becoming essential, especially in the AI-driven world we are moving towards. AI applications rely on vast amounts of data coming from various sources in different formats. Having a unified view of the customer, often referred to as ‘Customer 360,’ is becoming critical. Many large apps today build this singular customer view by aggregating data from multiple sources. Without this, it’s impossible to deliver the personalised experiences consumers expect.
In most enterprises, there are typically three key systems: systems of record (transactional systems), systems of insight (data analysis and insights), and systems of engagement (customer interactions). These systems often operate with disparate applications, leading to data silos. To overcome this, companies create an Operational Data Store (ODS), which consolidates all this data into one unified source of truth. This is where MongoDB comes in.
MongoDB’s document model is highly flexible, allowing it to store any type of data, whether structured or unstructured—transactional data, PDFs, audio, or video files. Companies use MongoDB as a unified data store, which then enables them to build a comprehensive ‘Customer 360’ view. This helps them better understand and engage with their customers in a more personalised and effective way.
MongoDB reported a significant rise in operational losses for Q2 of fiscal 2025. Could you share the year-on-year growth rate for the Indian market, and where the focus lies between large enterprises and SMEs in terms of business growth?
Globally, we’ve seen strong growth, with a 13% year-on-year increase and a customer base now exceeding 50,000. In India, we are expanding rapidly, with a focus on three key areas. First, we continue to target the startup and digital-native segment, like Zepto and Zomato. Second, large enterprises, particularly in the financial sector—banks, fintechs, and other financial services—are a major focus. Lastly, we’re also prioritising ISVs, as they contribute significantly to the market, with 80% of software still coming from independent software vendors here.
How does MongoDB’s Atlas integrate with generative AI technologies, and what advantages does this integration offer your clients?
Atlas is our fully managed service available across AWS, GCP, and Microsoft Azure, allowing customers to deploy their applications in the region or availability zone of their choice. It takes care of administrative tasks like backups and version upgrades, enabling clients to focus on building their applications without worrying about infrastructure management.
When it comes to generative AI, we’ve introduced vector search, a powerful capability we launched last year. Vector search allows users to represent unstructured data—like video or audio files—in a way that AI models can understand by converting it into a multi-dimensional form. This feature is part of Atlas, making it easier for clients to integrate AI-driven solutions. For example, Intellect.ai has built its platform ‘Purple Fabric’ for FSI customers using both Atlas and vector search. Many of our customers use this for various use cases to optimise their AI applications.
What are MongoDB’s key priorities for the next year to compete with relational databases, and how will you differentiate yourself? Also, how do you tackle the challenges in the Indian market, especially with price sensitivity and legacy brands?
Organisations must modernise their legacy systems as technology evolves and consumer demands increase. MongoDB’s strategy is to support this modernisation, whether on-premises or in the cloud, according to each organisation’s needs. Our priorities are becoming the standard for large enterprise workloads, especially in the financial sector; excelling in AI and generative AI applications; and enhancing our platform for developers to facilitate the creation of modern applications. We are committed to adapting to these changes and helping our clients navigate their digital transformation.
How will MongoDB address cost concerns for large enterprises and convince them to reduce operational expenses? Also, how will you help FMCG companies manage and organise their scattered retail data?
For large enterprises, the key discussion often revolves around reducing time to market and enhancing innovation rather than just cost. Enterprises are facing intense competition from startups that are rapidly capturing market share. Consequently, these companies need to innovate quickly and efficiently to stay relevant. In these conversations, the emphasis is on how MongoDB can accelerate their time to value and help them innovate faster.
For retail companies dealing with scattered data, MongoDB offers a significant advantage as a unified data store. Our platform can integrate disparate data sources and handle a wide range of data types and workloads. This capability allows retailers to consolidate their siloed data into a single, cohesive view. For example, we’ve supported various e-commerce and media companies in creating a comprehensive 360-degree customer view, enabling them to manage and utilise their data more effectively.