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2026.05.0622 min readLv.2 Beginner

SeriesWhy Enterprises Choose MongoDB · Part 2/4View series hub

Why Enterprises Choose MongoDB Part 2 — Industry Case Studies In Depth

Seven enterprise case studies across financial services, healthcare, retail, telecom, logistics, and AI security show why organisations chose MongoDB and what they measured afterwards. Wells Fargo's card platform modernisation, McKesson's 300x transaction scale-out, Novo Nordisk's CSR automation, Victoria's Secret's zero-downtime migration, Deutsche Telekom's digital channel consolidation, 99Minutos's hypergrowth survival, and Sentra's 180x query improvement — each case is structured as problem, selection rationale, and verified outcome.

Series outline

• Part 1 — From NoSQL Underdog to Enterprise Standard (previous post)
• Part 2 — Industry Case Studies In Depth (this post)
• Part 3 — Technical Reasons Enterprises Choose MongoDB (coming soon)
• Part 4 — Escape Legacy: Migrating from RDBMS to MongoDB (coming soon)

Table of Contents

1. Introduction — Numbers as proof of adoption
2. Financial Services — Wells Fargo: Escaping the Mainframe, Rebuilding the Card Platform
3. Healthcare — McKesson & Novo Nordisk: Data Infrastructure That Protects Patients
4. Retail — Victoria's Secret: An E-commerce Platform Handling 2.5 Billion Documents
5. Telecom — Deutsche Telekom: Unifying the Digital Experience for 30 Million Customers
6. Logistics & Startups — 99Minutos: The Database That Survived 7,500% Growth
7. AI Security — Sentra: 180x Query Speed Improvement
8. Cross-industry Comparison and Common Patterns

1. Introduction — Numbers as proof of adoption

A database choice is not a purely technical decision. It determines how fast a company can grow, what its cost structure looks like, and ultimately how it competes.

Part 1 traced the broad forces behind MongoDB's resurgence. This part goes into the field — financial services, healthcare, retail, telecom, logistics, and AI security — and examines what seven organisations actually measured after choosing MongoDB.

Every figure in this post comes from MongoDB's official customer stories and published case materials. No numbers have been estimated or extrapolated beyond what the source documents state.

2. Financial Services — Wells Fargo: Escaping the Mainframe, Rebuilding the Card Platform

Background: the "Cards 2.0" challenge

Wells Fargo, one of the four largest banks in the United States, faced an acute need to modernise a credit card platform that had relied on mainframe infrastructure for decades. Maintenance costs were high and the platform lacked the horizontal scalability to handle explosively growing transaction data. The initiative was known internally as "Cards 2.0."

Selection: MongoDB-powered Operational Data Store

Wells Fargo built an Operational Data Store (ODS) with MongoDB at its core, designed around a reusable API layer and a microservices architecture spanning more than 80 services.

Nadeem Kayani, EVP/CIO of Consumer Lending at Wells Fargo, put it this way:

"We want developers to think innovatively, solve problems themselves, and write great code. The developer experience MongoDB pursues is exactly what we pursue."

Outcomes: measurable change

The Wells Fargo ODS now handles rewards and loyalty programme operations as well. Moving off mainframe dependency created the foundation for real-time, personalised financial services — making this a business model transformation rather than a simple infrastructure migration.

Wider adoption in financial services

Beyond Wells Fargo, MongoDB adoption in financial services continues to spread. Goldman Sachs has maintained a close engineering partnership with MongoDB for over a decade. Lombard Odier — a Swiss private bank founded in 1796 — is modernising toward an AI-integrated architecture together with MongoDB. The fact that a 240-year-old institution chose MongoDB is itself a strong statement of trust.

3. Healthcare — McKesson & Novo Nordisk: Data Infrastructure That Protects Patients

McKesson: solving compliance and 300x scale simultaneously

McKesson is the largest pharmaceutical distributor in the United States, handling roughly 30% of the country's drug distribution volume. 2025 was a pivotal year: the Drug Supply Chain Security Act (DSCSA) came into full effect, requiring McKesson to track more than 1.2 billion medication serial numbers annually across the entire supply chain — a legal obligation that its existing systems simply could not meet.

McKesson chose MongoDB Atlas and deployed the new system in under six months.

The result was striking. Transaction processing volume expanded 300 times compared to the predecessor system, with zero downtime. In a mission-critical system directly linked to patient safety, that figure is not merely a performance benchmark — it is a measure of lives protected.

Novo Nordisk: 12 weeks to 10 minutes, GenAI-powered clinical report automation

The global Danish pharmaceutical company Novo Nordisk uses MongoDB in a different but equally impactful way. Clinical Study Reports (CSRs) — mandatory documentation in every drug development programme — previously took 12 weeks to complete from start to finish.

Novo Nordisk combined MongoDB Atlas with generative AI to automate the process. The result: CSR generation time fell from 12 weeks to just 10 minutes. By eliminating this bottleneck in the regulatory approval process, Novo Nordisk dramatically accelerated the time-to-market for new treatments.

Healthcare demands absolute data integrity and compliance. The fact that 14 of the world's top 15 healthcare companies trust MongoDB reflects its ability to deliver on both simultaneously.

4. Retail — Victoria's Secret: An E-commerce Platform Handling 2.5 Billion Documents

Problem: the limits of a monolithic architecture

Victoria's Secret operated an e-commerce platform processing more than 2.5 billion documents spread across hundreds of on-premises databases. The company had initially adopted CouchDB, but persistent data duplication and functional limitations kept holding it back. The monolithic architecture drove up operating costs and slowed new feature releases.

Solution: MongoDB Atlas on Azure, zero-downtime migration

In 2023, Victoria's Secret decided to migrate fully to MongoDB Atlas on Azure. Over four months, the team moved 200 databases and more than 4 TB of data without a single moment of service interruption.

Outcomes: dramatic operational improvement

A 75% reduction in CPU usage translates directly into lower cloud infrastructure costs; a 240% improvement in API performance fundamentally changes the consumer experience. Following the migration, Victoria's Secret added Atlas Vector Search to improve personalised search as well.

This case is frequently cited as the textbook example of how a legacy retailer can recover digital competitiveness.

5. Telecom — Deutsche Telekom: Unifying the Digital Experience for 30 Million Customers

Background: fragmented legacy systems

Germany's largest telecommunications company, Deutsche Telekom (DT), serves 30 million customers — but its B2C digital channels were fragmented across ageing legacy systems. Customer experience varied by service and by channel, and developer productivity was chronically low.

Solution: MongoDB Atlas as the Internal Developer Platform (IDP)

DT adopted MongoDB Atlas as the data backbone of its Internal Developer Platform (IDP). The IDP consolidated scattered customer data in one place and gave teams the foundation to ship new digital services quickly. Legacy systems were decommissioned progressively, reducing reliance on physical branches and call centres.

European GDPR compliance was also handled through Atlas's flexible deployment options, keeping customer data sharded within European regions only.

Outcomes: explosive growth in digital engagement

Scaling daily digital engagement from fewer than 50,000 interactions to 1.5 million is not an infrastructure story — it is evidence that the MongoDB transition reshaped the business strategy itself.

DT is not an isolated case in telecom. AT&T, Vodafone, Verizon, and Telefónica all use MongoDB as the foundation for 5G, IoT, and AI/ML solutions. Vodafone and Telefónica are scaling ecosystems of hundreds of millions of IoT devices on MongoDB.

6. Logistics & Startups — 99Minutos: The Database That Survived 7,500% Growth

The reality of 7,500% growth

99Minutos, a last-mile logistics startup in Latin America, recorded 7,500% growth in less than two years after founding. The problem: the PostgreSQL (AWS) deployment the company had started with could no longer keep up.

Every surge in demand destabilised the system, and reconciling the different data requirements across regions into a single rigid schema was practically impossible.

Selection: MongoDB Atlas on Google Cloud

99Minutos replaced its database infrastructure entirely with MongoDB Atlas on Google Cloud. The migration and build-out were completed in under six months.

Outcomes: cost and agility, both at once

The impact was immediate.

• 50% reduction in operating costs: infrastructure spending fell to half of what it was on PostgreSQL.
• Stable scalability: the system now handles traffic spikes without instability.
• Engineering focus restored: time previously spent fighting operational incidents was redirected to market expansion.

The 99Minutos case is a vivid lesson in what happens when a hypergrowth startup outgrows its initial database choice — and how to course-correct.

7. AI Security — Sentra: 180x Query Speed Improvement

Problem: 3-minute queries in a real-time security product

Sentra provides a cloud data security platform that helps enterprise customers discover and protect sensitive data. When data volumes surged on its PostgreSQL-based system, a severe performance crisis emerged: a single complex security query was taking three minutes to return results. In a real-time threat detection service, three minutes is a fatal delay.

Solution: strategic rebuild on MongoDB Atlas

Sentra worked with MongoDB Professional Services to rebuild the system from the ground up. The architecture was redesigned to exploit the flexibility of the document model, and an optimisation effort reduced the search index footprint by 70%.

Outcomes: queries from 3 minutes to 1 second, assets scaled 50x

The platform now manages more than 1 billion assets — roughly 50 times the previous scale — while delivering faster and more reliable query responses than before. Sentra uses MongoDB Atlas Search to support complex filtering and aggregation, providing enterprise customers with real-time threat detection and compliance insights.

8. Cross-industry Comparison and Common Patterns

MongoDB adoption outcomes at a glance

The common pattern: why did these companies choose MongoDB?

Three themes run through every case.

First, flexible schema. The ability to evolve data structures freely as business requirements change was the decisive difference from relational databases. Victoria's Secret's schema rigidity problem with CouchDB and 99Minutos's difficulty unifying regional data variations in PostgreSQL both trace back to this constraint.

Second, horizontal scalability. Whether a company faces unpredictable growth like 99Minutos or a sudden 300x volume surge from regulatory change like McKesson, MongoDB Atlas scales out without forcing a wholesale infrastructure replacement.

Third, cloud-native and multi-cloud flexibility. Wells Fargo ran a multi-cloud strategy. Deutsche Telekom satisfied European GDPR requirements by sharding customer data within European regions only. 99Minutos chose Google Cloud; Victoria's Secret chose Azure. Each company selected the cloud environment that fit best, without being locked into a single provider.

What's next in Part 3

Part 2 established what these companies achieved. Part 3 goes one level deeper to explain why those results were possible. We will examine why the document model is particularly well-suited to the AI era, how Vector Search works, and how multi-document ACID transactions earned the trust of regulated enterprise environments.

If this post was useful, bookmark the full series. Part 3 will be published soon.

References

• Innovating With MongoDB: Customer Successes, October 2025
• Innovating with MongoDB: Customer Successes, December 2025
• Innovating with MongoDB: Customer Successes, March 2025
• McKesson Scales Operations 300x with MongoDB Atlas
• Deutsche Telekom Case Study — MongoDB
• MongoDB for Telecommunications
• MongoDB for Financial Services