For one major Canadian bank, the Capital Markets division plays a critical role in its daily operations. This organization supports institutional investors, corporations, and high-net-worth individuals around the world with mergers and acquisitions advisory, debt and equity financing, corporate banking, trading, and more.
After the division faced exponential growth in the amount of information it managed — and an increase in the complexity of that data — it encountered significant challenges. Fragmented and duplicated information across disparate systems hindered the organization’s ability to gain timely insights and make business decisions. But by working with MongoDB, the organization is modernizing its infrastructure and unlocking the full potential of its data.
To support its services, the bank’s IT team oversaw 76 applications and 113 data domains. The Capital Markets division handled vast amounts of data across these domains, including client information, market data, transactions, and risk metrics. Tens of millions of records flowed through these disparate systems daily, creating a complex and siloed data architecture.
This data was often duplicated across multiple databases, making it difficult to access and use information efficiently. Stakeholders often created localized databases and tools to manage this information. This led to dependencies and roadblocks, particularly as a result of staffing changes, churn, or cross-departmental movement. Without consistent data governance, maintaining data accuracy and reliability became increasingly challenging.
The Capital Markets division had an imperative for a more unified approach. “We needed to implement a central, flexible, scalable, and resilient home for capital markets data,” said a senior director at the bank. “It’s a simple statement, but it required a lot of steps to make it happen and work effectively.”
The organization set out to establish a core data framework that could accommodate its data needs. The solution needed to be flexible and to integrate seamlessly with different analytical tools. The bank also wanted to distribute data ownership to domain-specific teams through a data mesh architecture. This would empower teams to manage, own, and service data as a product to the rest of the organization, thus improving data quality and reducing the rate of duplication.
MongoDB Atlas, an integrated suite of cloud database and data services, emerged as the ideal solution. Its flexible schema, scalability, and rich feature set aligned with the organization’s compliance requirements. “Data security was instrumental in ensuring that stakeholders across the enterprise found the solution suitable and a good fit for our needs,” said the senior director. “MongoDB Atlas checked all the boxes and would let us adopt features like SSO SAML integration, encryption, and different support tiers that fit the criticality of our use cases. We could also quickly integrate several data sources without needing a large number of developers.”
Senior Director at a major Canadian bank
The Capital Markets division first implemented MongoDB Enterprise Advanced and created an internal data hub on top of the database. With this hub, the bank can consolidate data from various sources into a single location, breaking down silos and providing a unified view of assets across the organization. The division is migrating production workloads from MongoDB Enterprise Advanced to MongoDB Atlas, with plans to go live in August 2024.
The organization worked closely with MongoDB’s consulting teams to optimize its solution architecture and facilitate a smooth transition. The bank also invested in MongoDB training for its internal teams, which helped them harness the full potential of MongoDB’s features and capabilities. “We’ve been able to do all this integration from the ground up,” said the senior director. “MongoDB has proven to be production ready, resilient, adaptable, scalable, and widely used.”
The Capital Markets division has adopted various features to optimize its data management processes. For example, it implemented MongoDB’s flexible document model and schema design principles to help store and manage its data more effectively. This approach has minimized data duplication and can integrate new data types faster than before.
To perform complex data analyses, the division took advantage of MongoDB’s querying and indexing capabilities. The organization also integrated MongoDB with solutions from MongoDB partner Databricks, which it uses to support machine learning, data visualization, and other advanced use cases. “By extending the data hub to other frameworks, we’ve built strategic dashboards using business intelligence tools,” said the senior director. “We’ve also developed solutions such as chatbots, recommender engines, and machine learning on top of this dataset.”
The Capital Markets division deployed multiregional clusters using MongoDB’s replication and failover mechanisms to facilitate data resilience and high availability. With this approach, the organization can distribute data across different geographic locations, which reduces the risk of data loss and facilitates strong disaster recovery.
With streamlined data management powered by MongoDB, the bank has modernized its Capital Markets data infrastructure and unlocked new growth opportunities. The organization can now rapidly integrate new data sources, perform advanced analytics, and gain valuable insights to drive business decisions and bring new initiatives to market faster.
Looking ahead, the division will build upon this success and migrate more data to MongoDB Atlas by the end of 2024. The bank will also use MongoDB Atlas Search to facilitate fast, rich, and relevant full-text search queries, which will remove the need for a separate search engine.
“We’ve quickly iterated and done more and more with MongoDB,” said the senior director. “This is a major journey we have started, and we’re excited to continue in this direction.”
Senior Director at a major Canadian bank
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