Latest news with #datachallenges
Entrepreneur
5 days ago
- Business
- Entrepreneur
MongoDB Doubles Down on India's Database Boom
"Our focus remains on startups, late-stage digital-native businesses and enterprises. We'll continue working closely with customers to solve their data challenges," says Sachin Chawla, Vice President, India and ASEAN, MongoDB Opinions expressed by Entrepreneur contributors are their own. You're reading Entrepreneur India, an international franchise of Entrepreneur Media. "India is very important for us as a market. We have almost 700 employees here. We also have an engineering team now in India, which is part of the overall MongoDB strategy," says Sachin Chawla, Vice President, India and ASEAN, MongoDB. MongoDB's customer base in India spans early-stage startups such as RFPIO, high-growth unicorns like Zepto and Zomato, large financial institutions such as Canara HSBC Life Insurance, digital platforms like SonyLIV, and Indian ISVs including and Darwinbox. According to Grand View Research, the Indian database management system (DBMS) market generated USD 2.5 billion in revenue in 2024 and is expected to reach USD 7.5 billion by 2030, growing at a compound annual growth rate (CAGR) of 19.8 per cent. Interestingly, China's DBMS market stood at USD 8.7 billion in 2024 and is projected to reach USD 19.8 billion by 2030, growing at a CAGR of 14.6 per cent from 2025 to 2030. Moving beyond legacy systems Chawla says MongoDB is helping Indian enterprises move beyond legacy systems through two distinct approaches. "The first one is when customers decide to build a completely new modern application, gradually sunsetting the old legacy application," he explains. "We work closely with them to build these modern systems." He gives the example of Tata Neu, a loyalty app that integrates over 40 Tata brands, including Taj Hotels and Tata Croma, into a single digital platform. "That entire application is built on MongoDB," he says. "The second approach is application modernisation," Chawla adds. "Here, companies want to retain their existing application but upgrade it. Over the last two years, we've developed a methodology using AI tools and our expertise to modernise the full stack, not just the database." Tackling myths around modern databases Despite this fast-paced growth, Chawla points out several lingering myths in India. "A lot of customers still haven't realised that if you want to build a modern application especially one that's AI-driven you can't build it on a relational structure," he explains. "Most of the data today is unstructured and messy. So you need a database that can scale, can handle different types of data, and support modern workloads." Another misconception Chawla observes is the belief that each use case requires a purpose-built database: one for time-series data, another for geospatial queries, and so on. "The problem with that approach is if you have four or five different use cases, you end up managing five different databases. It becomes a nightmare in terms of management, scalability, and integration," he says. Even those trying to move away from traditional databases often fall into the trap of viewing PostgreSQL as a modern alternative. "PostgreSQL is still relational in nature. It has the same row-and-column limitations and scalability issues." He also adds that if companies want to build a future-proof application especially one that infuses AI capabilities they need something that can handle all data types and offers native support for features like full-text search, hybrid search, and vector search. Other NoSQL players such as Redis and Apache Cassandra also have significant traction in India. Redis sees over 12 million daily downloads and reportedly earns 60 per cent of its revenue from national database projects. Apache Cassandra holds a 14.95 per cent presence in India, with enterprise users including Infosys, Fujitsu, and Panasonic. Amazon's DynamoDB commands around 9.5 per cent of the market. What's next for India? MongoDB operates from Bengaluru and Gurugram and plans to deepen its presence across India. "Our focus remains on startups, late-stage digital-native businesses (DNBs), ISVs, and enterprises. We'll continue working closely with customers to solve their data challenges," Chawla affirms.
Bloomberg
27-05-2025
- Business
- Bloomberg
How investors can navigate the sustainable data disclosure gap
Tackling gaps in sustainability data However, for the foreseeable future, investors will continue to rely on legacy voluntary disclosures. To help investors face this large, sparsely populated dataset, Bloomberg provides a curated set of waterfall fields (i.e. providing the best available data from a prioritized sequence of fields) and derived metrics, and standardizes the different units of measure used by companies around the world. Our standard dataset for entity-level sustainability disclosures, covering a universe of companies equivalent to ~95% of global market cap, illustrates the challenges of voluntary reporting. The absence of binding requirements and a lack of standardization result in a large number of related but ultimately distinct disclosures for a given metric, together with low levels of disclosure (Figure 1). More specifically, sustainability analysts must navigate between hundreds of fields, knowing that environmental fields are reported by 1 in 5 entities, while social metrics are available for 1 entity in 3. The extent of disclosure depends on the topic and type of business activity (see Figure 2. Note: These differences in company focus will be captured by the double materiality assessment under CSRD). For instance, a mining company's investors may prioritize details about community engagement and water consumption, while a hardware manufacturer might emphasize aspects of the circular economy. In contrast, an entertainment company with a smaller environmental impact may have fewer data points to report. The level of disclosure varies by company size. For instance, large companies with revenues in excess of US$10 billion report twice as many environmental metrics as smaller firms with revenues under US$100 million. Large companies tend to attract the most sophisticated investors and are better positioned to allocate more resources toward measuring sustainability performance. The level of disclosure also varies by region (Figures 3 and 4). Based on Bloomberg coverage of over 16,000 companies, the average field completeness is similar in Europe and APAC. The former has a slight edge on climate metrics while the latter comes out slightly ahead in the total number of environmental and social datapoints (108 versus 104 populated fields). Meanwhile, firms in North America lag behind with approximately 20% fewer datapoints across those categories, but US regulations and long-standing practice result in more governance datapoints than firms in other regions. Investor demand for sustainability data has contributed to a twofold increase in the rate of disclosures over the past 20 years (Figure 5). Investors seeking to understand the impact of sustainability on financial performance ask for more transparency, but voluntary reporting has shortcomings. The standardization of sustainability reporting around mandatory reporting frameworks like CSRD and IFRS S1 and S2 should result in more useful datasets. As a result, companies should receive fewer bilateral data requests from their investors, suppliers and customers. In due course, investors may be able to do more with less, as interest in non-mandatory disclosures with lower completeness should fade over time.
