Innovating with MongoDB | Customer Successes, March 2025

Katie Palmer
March 18, 2025

Hello and welcome! This is the first installment of a new bi-monthly blog series showcasing how companies around the world are using MongoDB to tackle mission-critical challenges. As the leading database for modern applications, MongoDB empowers thousands of organizations to harness the power of their data and to drive creativity and efficiency across industries. This series will shine a light on some of those amazing stories.

From nimble startups to large enterprises, our customers are transforming data management, analytics, and application development with MongoDB's flexible schema, scalability, and robust cloud services.

What do I mean? Picture retailers like Rent the Runway improving customer experiences with real-time analytics, fintech companies such as Koibanx speeding up and securing transaction processes, and healthcare companies like Novo Nordisk optimizing the path to regulatory approvals. With MongoDB, every developer and organization can fully tap into the potential of their most valuable resource: their data.

So please read on—and stay tuned for more in this blog series!—to learn about the ingenuity of the MongoDB customer community, and how they’re pushing the boundaries of what's possible.

Lombard Odier

Lombard Odier, a Swiss bank with a legacy dating back to 1796, transformed its application architecture with MongoDB to stay at the forefront of financial innovation. Confronted with the challenge of modernizing its systems amidst rapid digital and AI advancements, the bank leveraged MongoDB’s Application Modernization Factory and generative AI to streamline its application upgrades.

This initiative resulted in up to 60x faster migration of simple code and slashed regression testing from three days to just three hours. By transitioning over 250 applications to MongoDB, including its pivotal portfolio management system, Lombard Odier significantly reduced technical complexity and empowered its developers to focus on next-generation technologies.

SonyLIV

SonyLIV faced challenges with its over-the-top (OTT) video-streaming platform. Their legacy relational database had poor searchability, complex maintenance, and slow content updates. Critically, it lacked the scalability necessary to support 1.6 million simultaneous users. To power their new CMS— ‘Blitz’—SonyLIV selected MongoDB Atlas’s flexible document model to improve performance and lower search query latency by 98%.

Collaborating with MongoDB Professional Services, SonyLIV optimized API latency using MongoDB Atlas Search and Atlas Online Archive, effectively managing over 500,000 content items and real-time updates. With their new high-performing, modern solution in place, SonyLIV can now deliver flawless customer experiences to the world, faster.

Swisscom

Swisscom, Switzerland's leading telecom and IT service provider, harnessed MongoDB to enrich its banking sector insights with AI. Faced with the challenge of streamlining access to its extensive library of over 3,500 documents, Swisscom utilized MongoDB Atlas and MongoDB Atlas Vector Search capabilities to transform unstructured data into precise, relevant content summaries in seconds.

In just four months, Swisscom launched a production-ready platform with improved relevance, concrete answers, and transparency. The project sets a new standard in Swiss banking, and showcases Swisscom's commitment to driving the digital future with advanced AI solutions.

Victoria’s Secret

Victoria's Secret’s e-commerce platform processes thousands of transactions daily across over 2.5 billion documents on hundreds of on-premises databases. Experiencing high costs and operational constraints with its monolithic architecture, the retailer initially adopted CouchDB but faced challenges like data duplication and limited functionality.

In 2023, Victoria's Secret migrated to MongoDB Atlas on Azure, achieving zero downtime while optimizing performance and scalability. Over four months, they successfully migrated more than four terabytes of data across 200 databases, reducing CPU core usage by 75% and achieving a 240% improvement in API performance. The move to MongoDB also allowed the retailer to introduce additional products, like MongoDB Atlas Vector Search, resulting in significant operational efficiencies and cost savings.

Video spotlight

Before you go, be sure to watch one of our recent customer videos featuring the Danish pharmaceutical giant, Novo Nordisk.

Discover how Novo Nordisk leveraged MongoDB and GenAI to reduce the time it takes to produce a Clinical Study Report (CSR) from 12 weeks to 10 minutes..

Want to get inspired by your peers and discover all the ways we empower businesses to innovate for the future? Visit our Customer Success Stories hub to see why these customers, and so many more, build modern applications with MongoDB.

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Modernizing Telecom Legacy Applications with MongoDB

The telecommunications industry is currently undergoing a profound transformation, fueled by innovations in 5G networks, the growth of Internet of Things applications, and the rapid rise of AI. To capitalize on these technologies, companies must effectively handle increasing volumes of unstructured data, which now represents up to 90% of all information, while also developing modern applications that are flexible, high-performance, and scalable. However, the telecommunications industry's traditional reliance on relational databases such as PostgreSQL presents a challenge to modernization. Their rigid structures limit adaptability and can lead to decreased performance as table complexity grows. With this in mind, this blog post explores how telecom companies can modernize their legacy applications by leveraging MongoDB’s modern database and its document model. With MongoDB, telecom companies can take advantage of the latest industry innovations while freeing their developers from the burdens of maintaining legacy systems. Navigating legacy system challenges Legacy modernization refers to the process of updating a company’s IT infrastructure to align it with the latest technologies and workflows, and ultimately advancing and securing strategic business goals. For telecom companies, this modernization involves overcoming the limitations of their legacy systems, which hinder adjustment to changing market conditions that demand greater system scalability and availability to run real-time operations. The main drawbacks of legacy technologies like relational databases stem from their design, which wasn’t built to support the data processing capabilities required for modern telecom services. These limitations, as illustrated in Figure 1 below, include rigid data schemas, difficulty handling complex data formats, limited scaling ability, and higher operational costs for maintenance. Figure 1. The limitations of legacy systems. Expanding on these limitations, relational databases depend on a predefined schema, which becomes difficult to modify once established, as changes entail extensive restructuring efforts. In telecommunications, handling growing data volumes from connected devices and 5G networks can rapidly become burdensome and costly due to frequent CPU, storage, and RAM upgrades. Over time, technology lock-in can further escalate costs by hindering the transition to alternative solutions. Altogether, these factors hold back modernization efforts urging telecoms to transform their legacy systems to newer technologies. To overcome these challenges, telecom companies are replacing these legacy systems with modern applications that effectively provide them with greater scalability, enhanced security, and high availability, as shown in Figure 2. However, achieving this transition can be a daunting task for some organizations due to the complexity of current systems, a lack of internal technical expertise, and the hurdles of avoiding downtime. Therefore, before transforming their outdated systems, telecom companies must carefully select the appropriate technologies and formulate a modernization strategy to facilitate this transition. Figure 2. Characteristics of modern applications. Getting onboard with MongoDB Enter MongoDB. The company’s document-oriented database offers a flexible data model that processes any information format, easily adapting to specific application requirements. MongoDB Atlas —MongoDB’s unified, modern database—delivers a robust cloud environment that efficiently manages growing data volumes through its distributed architecture, ensuring seamless connectivity and enhanced performance. Moreover, as telecom providers prioritize cybersecurity and innovation, MongoDB includes robust security measures—comprising encryption, authentication, authorization, and auditing—to effectively protect sensitive information and ensure regulatory compliance. Additionally, leveraging MongoDB’s document model with built-in Atlas services like Vector Search , Atlas Charts , and Stream Processing allows telecommunications organizations to streamline advanced industry cases, including single customer view, AI integrations, and real-time analytics. Figure 3. Core MongoDB modernization features for Modernization. Recognizing these benefits, leading telecom companies like Nokia , Swisscom , and Vodafone have successfully modernized their applications with MongoDB. However, selecting the right technology is only part of the modernization process. In order to ensure a successful and effective modernization project, organizations should establish a comprehensive mod Data-driven modernization: this approach transfers all data from the legacy system to the new environment and then migrates applications. Application-driven modernization (all-or-nothing): this approach executes all reads and writes for new applications in the new data environment from the start, but leaves the business to decide when to retire existing legacy applications. Iterative modernization (one-step-at-a-time): this approach blends the previous paths, starting with the modernization of the least complex applications and incrementally moving forward into more complex applications. Read this customer story to learn more about telecoms migrating to MongoDBL With this overview complete, let's dive into the migration process by examining the iterative modernization of a telecom billing system. Modernizing a telecom billing system Telecom billing systems often consist of siloed application stacks segmented by product lines like mobile, cable, and streaming services. This segmentation leads to inefficiencies and overly complex architectures, highlighting the need to simplify these structures. With this in mind, imagine a telecom company that has decided to modernize its entire billing system to boost performance and reduce complexity. In the initial stage, telecom developers can assess the scope of the modernization project, scoring individual applications based on technical sustainability and organizational priorities. Applications with high scores undergo further analysis to estimate the re-platforming effort required. Later on, a cross-functional team selects the first component to migrate to MongoDB, initiating the billing system modernization. This journey then follows the steps outlined in Figure 4: Figure 4. The modernization process. First, developers analyze legacy systems by examining the codebase and the underlying architecture of the chosen billing system. Then, developers create end-to-end tests to ensure the application functions correctly when deployed. Later, developers design an architecture that incorporates managerial expectations of the desired application. Next, developers rewrite and recode the legacy application to align with the document model and develop APIs for MongoDB interaction. Following this, developers conduct user tests to identify and resolve any existing application bugs. Finally, developers migrate and deploy the modernized application in MongoDB, ensuring full functionality. Throughout this process, developers can leverage MongoDB Relational Migrator to streamline the transition. Relational Migrator helps developers with data mapping and modeling, SQL object conversion, application code generation, and data migration—corresponding to steps three, four, and five. Additionally, telecom companies can accelerate modernization initiatives by leveraging MongoDB Professional Services for dedicated, tailored end-to-end migration support. Our experts work closely with you to provide customized assistance, from targeted technical support and development resources to strategic guidance throughout the entire project. Building on this initial project, telecom companies can progressively address more complex applications, refining their approach to support a long-term modernization strategy. Next steps By revamping legacy applications with MongoDB, telecom companies can improve their operations and gain a competitive edge with advanced technology. This shift allows telcos to apply the latest innovations and free developers from the burdens of maintaining legacy systems. Start your journey to migrate core telecom applications to MongoDB Atlas, by visiting our telecommunications solutions page to learn more. If you would like to discover how to upgrade your TELCO legacy systems with MongoDB, discover how to start with the following resources: Visit our professional services to learn more about MongoDB Consulting YouTube: Relational Migrator Explained in 3 minutes White paper: Unleash Telco Transformation with an Operational Data Layer White paper: Modernization: What’s Taking So Long?

March 17, 2025
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IFG Life Modernizes and Unlocks the Power of Data With MongoDB

Financial services customers across the Asia-Pacific (APAC) region speak often about the need to modernize their infrastructures to unlock the full potential of data. Frequently, companies are encumbered by legacy data architectures that create inefficiencies and complexities for their developer teams. IFG Life overcame these challenges by migrating to MongoDB Atlas . Garry Alfanzo, Head of IT and Architecture for IFG Life, shared insights during a presentation at MongoDB.local Jakarta 2024 . Modernizing by migrating to the cloud A subsidiary of Indonesia Finance Group (IFG), IFG Life specializes in life and health insurance. The company was established in 2020 with an initial purpose of migrating policies from former state-owned insurance company Jiwasraya and bringing them under the IFG umbrella. IFG Life had to overhaul an outdated infrastructure, which included multiple legacy systems and coding frameworks. To kick-start its modernization journey and enhance agility, IFG Life migrated to the cloud and adopted a microservices architecture. This led to the creation of the Customer 360 model, a platform designed to manage IFG Life’s four main user applications: customer, corporate, sales force, and hospital network. “The purpose of developing this division [was] to create a single integrated platform. In this digital era, we realized that just having an outstanding app is not the only thing that matters. Data is also crucial,’ said Alfanzo. The need for a robust database solution to support Customer 360 operations became evident. Getting a false start with a graph database “In 2022 the hype around graph databases was very high, leading up to a surge in various graph database solutions,” said Alfanzo. IFG Life began pursuing this option. However, while implementing a graph database, the company experienced resource and knowledge challenges. “In 2022, the hype around graph databases was very high, leading to a surge in various graph database solutions. However, this excitement was not matched by sufficient resources in Indonesia,” said Alfanzo. This prompted IFG Life to shift its focus and seek a database that would meet its engineering team’s needs. Finding the optimal solution with MongoDB Atlas IFG Life needed a database with integrated graph capabilities—one that was widely adopted and supported by a large community in Indonesia. Additionally, the company needed to ensure access to a robust pool of local expertise. “The solution we aimed to implement revolves around data integration, allowing us to unify our various cores and streamline our storage solutions,” said Alfanzo. “Data mining is essential to extract the collected data, and we require data science to perform analytics, including data streaming through CDC [change data capture] and batch processing, alongside visualization.” Alfanzo’s team had already determined that it needed to move away from a relational approach, which was deemed ill equipped to meet IFG Life’s requirements. The team evaluated several NoSQL vendors, and MongoDB Atlas ticked all the boxes, and more. “When we evaluated MongoDB, we identified features that were essential for us,” said Alfanzo. Among these were $graphLookup , MongoDB Atlas Triggers , and MongoDB Atlas Device SDKs . “There’s also MongoDB Aggregation Pipeline , which is crucial for crunching data, creating views, and performing queries,” Alfanzo added. Alfanzo also mentioned how JSON , the text-based data format MongoDB uses, simplified IFG Life’s process of storing, retrieving, and viewing data. However, what Alfanzo highlighted the most was the unique expertise, knowledge, and support that the MongoDB team in Indonesia provided: “One of the critical factors during our evaluation was the ecosystem and community support,” said Alfanzo. “Having access to people with technology skills is essential, as it has been our biggest problem. That’s why we chose MongoDB. What I truly appreciate is that [it is] not just focused on selling but also [focused on] providing solutions for the future.” Integrating MongoDB into IFG Life’s ecosystem was a strategic decision that paid off. It facilitated the creation of a customer-to-system model, crucial for the Customer 360 initiative. This advancement enables IFG Life to better understand and serve its customers and has paved the way for more personalized and effective service offerings. Unlocking generative AI capabilities with MongoDB Atlas Vector Search Looking ahead, IFG Life is eyeing new frontiers with MongoDB, particularly as gen AI is fast spreading and being adopted by Indonesian enterprises. “We’ve already used MongoDB for our Customer 360, which is currently ongoing,” said Alfanzo. “To stay aligned with this trend, we recognize that MongoDB offers a feature that enhances query similarity— MongoDB Atlas Vector Search —and we plan to incorporate this into our future implementations.” Moreover, IFG Life intends to implement retrieval-augmented generation (RAG) to handle personally identifiable information (PII) securely. “We understand that PII should not be sent to large language models (LLMs). Incorporating sensitive data can expose the system to vulnerabilities,” explained Alfanzo. “In this context, RAG is a more suitable solution as, when querying RAG, we can add an additional layer to provide context, and this context enhances [the] query—we can ensure that we have real-time context that aligns with the standards of insurance or banking.” IFG Life is a great example of how MongoDB Atlas’s capabilities can help companies modernize their complex legacy systems in the data-dependent, highly regulated banking and insurance industry. This story also shows how the MongoDB Atlas platform can prepare organizations that want to reap the benefits of gen AI. To learn more about MongoDB Atlas , visit our product page. Get started with Atlas Vector Search today by checking out our quickstart guide .

March 18, 2025