Navigating the Data Deluge: Real-World Triumphs in Strategic Storage Solutions

It’s no secret, is it? We’re swimming in data. In fact, ‘swimming’ feels a bit understated; it’s more like we’re caught in a powerful, constantly growing digital ocean, waves of information crashing around us every second. In today’s hyper-connected, data-driven business landscape, simply having data isn’t enough. It’s about how you manage, store, access, and ultimately, leverage that data that truly separates the market leaders from the rest. Efficient, reliable, and strategically sound data storage solutions aren’t just ‘nice-to-haves’ anymore, they’re the absolute bedrock for any organization aiming to maintain a sharp competitive edge, foster innovation, and frankly, just keep the lights on and the business running smoothly.

Think about it: from the granular details of customer interactions to the sprawling blueprints of global supply chains, every piece of information holds potential value. But unlocking that value? That’s where the right storage strategy comes into play. It’s not a one-size-fits-all problem, and that’s what makes this topic so endlessly fascinating. Different industries, different scales, different compliance mandates—they all demand tailored approaches. Let’s delve into some truly insightful real-world examples where forward-thinking organizations have tackled their unique data storage challenges head-on, transforming obstacles into stepping stones for unprecedented growth.

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Optimizing the Engine Room: Manufacturing Sector’s Data Overhaul

Manufacturing, traditionally seen as a domain of tangible goods, has become incredibly reliant on intangible data. Imagine a leading European sportswear manufacturer, a behemoth in its field, operating globally. Their design teams are pushing out innovative concepts daily, production lines hum with sophisticated machinery, and logistics networks span continents. Every single one of these operations, from the initial CAD file for a new sneaker design to the IoT sensor data streaming from a robotic arm in a Vietnamese factory, generates a truly colossal amount of data. This isn’t just about invoices, it’s about detailed material specifications, intricate production metrics, vast inventories, quality control checks, and real-time sales figures from thousands of retail partners worldwide.

The Challenge: This sportswear giant was wrestling with significant challenges. Their legacy storage infrastructure simply couldn’t keep pace with the sheer volume and velocity of data. We’re talking about slowdowns in design iterations, delays in accessing critical production reports, and a general lack of agility that started to feel like wearing concrete shoes in a marathon. The fragmented nature of their existing systems meant data silos were rampant, making it incredibly difficult to get a unified view of their global operations. This translated directly into higher operational costs, missed opportunities for efficiency gains, and a palpable frustration across various departments. Frankly, it felt like they were trying to run a Formula 1 team with a local mechanic’s garage worth of tools.

The Strategic Solution: IBM’s Storage FlashSystem

Recognizing the urgency, the company made a pivotal decision: to invest in a modern, high-performance solution. They adopted IBM’s Storage FlashSystem, an all-flash array designed to deliver blistering speed and impressive scalability. This wasn’t just about adding more disk space, it was about fundamentally reimagining their storage architecture to leverage the power of flash memory. FlashSystem, with its NVMe capabilities, offers incredibly low latency and high input/output operations per second (IOPS), which is precisely what manufacturing analytics and real-time production monitoring demand.

• Enhanced Data Management: By implementing FlashSystem, the manufacturer fundamentally streamlined their data processes. Imagine the difference: designers could access and iterate on complex 3D models almost instantly, production managers could pull real-time performance data from assembly lines to identify bottlenecks before they escalated, and supply chain teams could quickly analyze global inventory levels to optimize distribution. This wasn’t just a marginal improvement; it significantly enhanced operational efficiency, cutting down the time required for critical data operations from hours to mere minutes in some cases.
• Cost Reduction: You might think high-performance flash storage is inherently expensive, and initially, the CapEx can be higher. However, the gains in efficiency and the reduction in other operational costs quickly offset this. With FlashSystem, the company achieved better data density, meaning they needed fewer physical storage units to house the same amount of data. This led to reduced power consumption, lower cooling costs, and a smaller data center footprint. Furthermore, the ability to process data faster meant fewer delayed decisions, less wasted production time, and ultimately, a more lean and agile operation that saved significant capital in the long run. It’s often the hidden costs of inefficiency that really bleed a budget dry, isn’t it?
• Scalability: The beauty of a well-architected flash system is its inherent scalability. As the sportswear manufacturer continued its global expansion, launching new product lines, acquiring smaller brands, and delving into new markets, their data volumes naturally exploded. The FlashSystem provided a modular and flexible foundation, allowing them to seamlessly add capacity and performance without rip-and-replace overhauls. This ensured their storage solution could effortlessly grow with their expanding data needs, protecting their initial investment and future-proofing their IT infrastructure against unpredictable data surges. It meant they wouldn’t have to constantly worry about hitting a storage ceiling, freeing them up to focus on innovation and market growth.

This transformation truly optimized their data storage, certainly, but more importantly, it established a robust, scalable foundation. It’s a foundation ready to support not just current operations but also future initiatives like advanced AI-driven demand forecasting or the integration of even more sophisticated IoT devices across their manufacturing facilities.

The Financial Sector’s Cloud Odyssey: A Multinational Beverage Corporation’s Journey

The financial sector is a fascinating beast when it comes to data. It’s characterized by immense volumes, stringent regulatory requirements, and an almost pathological aversion to downtime. Now, let’s turn our attention to a Fortune 100 multinational beverage corporation—yes, the kind whose products you probably have in your fridge right now. They weren’t just thinking about fizzy drinks; they were contemplating a monumental task: migrating 200 terabytes of critical operational and financial data from their on-premises data centers to the cloud. This wasn’t a whimsical decision; it was a calculated move driven by clear strategic objectives.

The Challenge: Migrating such a significant chunk of data, especially for a company of this size, is fraught with complexities. We’re talking about petabytes of historical sales data, intricate supply chain logistics, customer loyalty program information, sensitive financial records, and all the intellectual property tied to their beloved brands. Their existing on-premises data centers, while secure, were becoming increasingly expensive to maintain, demanding continuous investment in hardware refreshes, power, cooling, and specialized IT staff. Moreover, the agility needed to respond to market shifts or launch new digital initiatives was hampered by the sheer inertia of their physical infrastructure. Any disruption to operations, even minor, could cost millions and tarnish their global reputation, so ensuring a seamless transition was absolutely paramount.

Clear Objectives, Clear Skies (Eventually!):

• Cost Savings: The most immediate and tangible driver was cost. By migrating to the cloud, the corporation aimed to shift from a capital expenditure (CapEx) model—where large upfront investments are made in hardware—to an operational expenditure (OpEx) model. This meant paying only for the storage and compute resources they actually consumed, offering significant savings by eliminating the need to maintain expensive physical data centers, reduce power bills, and scale IT personnel solely for infrastructure maintenance. Imagine cutting down on those hefty quarterly refresh cycles for servers; it’s a game-changer.
• Efficiency: Beyond cost, the desire for enhanced operational efficiency was huge. They needed a migration process that wouldn’t just be ‘good enough’ but truly seamless, ensuring zero disruption to their 24/7 global operations. This encompassed everything from sales order processing to supply chain management and financial reporting. The cloud promised greater agility, faster deployment of new applications, and improved disaster recovery capabilities that were previously complex and costly to implement on-premises.

Leveraging Advanced Data Migration Tools:

This wasn’t a manual drag-and-drop affair. The company leveraged a sophisticated suite of advanced data migration tools and services, likely involving specialized software for data discovery, replication, synchronization, and cutover management. The process involved meticulous planning, extensive testing in staged environments, and a phased approach to minimize risk. They probably engaged with their chosen cloud provider’s professional services team, too, benefiting from their expertise in handling large-scale enterprise migrations. The key was establishing secure, high-bandwidth network connectivity between their on-premises environments and the cloud, employing encryption for data in transit and at rest, and ensuring rigorous data validation every step of the way.

Ultimately, the company successfully transitioned their 200 TB to the cloud, resulting in significant, quantifiable cost savings and a noticeable boost in operational efficiency. This move freed up their internal IT teams to focus on innovation rather than infrastructure, giving them the agility needed to launch new digital initiatives faster and more reliably. It’s a prime example of how, with careful planning and the right tools, even the most daunting data migration can yield immense strategic advantages.

French Hospitals’ Vision: Clinical Data Warehouses for Better Patient Care

Healthcare data, now there’s a field brimming with complexity and unparalleled importance, isn’t there? In France, a truly ambitious initiative saw 32 regional and university hospitals embark on a collaborative journey to implement Clinical Data Warehouses (CDWs). Their overarching goal was noble and vital: to harness the immense power of real-world patient data to drive insights, improve diagnostics, refine treatments, and ultimately elevate the standard of patient care across the nation. This wasn’t about administrative efficiency alone; it was about saving lives and improving outcomes.

The Challenge: Picture it: 32 separate institutions, each with its own legacy systems, patient record formats, diagnostic imaging archives, lab results, and administrative data. The data wasn’t just disparate; it was siloed, often incompatible, and locked away behind individual hospital walls. This fragmentation made it incredibly difficult for clinicians to get a comprehensive view of a patient’s history if they’d been treated at multiple facilities. For researchers, aggregating data for large-scale studies was a Herculean task, severely hindering efforts to identify new disease patterns, evaluate treatment efficacy, or conduct epidemiological analyses. The sensitivity of patient health information (PHI) also meant that any data solution had to meet incredibly strict privacy and security standards.

The Solution: Collaborative Clinical Data Warehouses

A Clinical Data Warehouse is essentially a specialized repository, designed to consolidate, integrate, and organize clinical data from various sources into a unified, analyzable format. It’s built specifically to support retrospective and prospective analysis, research, and quality improvement initiatives. For these French hospitals, implementing CDWs involved several critical aspects:

• Robust Governance: This was non-negotiable. Establishing clear, comprehensive policies for data management and usage was foundational. This encompassed everything from data ownership and access control—determining precisely who could see what data under which circumstances—to stringent anonymization and pseudonymization protocols to protect patient identities. They also defined meticulous audit trails, ensuring every data access and modification was logged, crucial for compliance with strict regulations like GDPR. Without strong governance, a CDW in healthcare could quickly become a regulatory nightmare.
• Transparency and Accessibility: While governance defined the rules, transparency was about facilitating legitimate access. The initiative aimed for open access to data for authorized personnel, which meant developing standardized data models, common APIs, and secure portals. Researchers, clinicians, and public health officials needed to be able to query the warehouse effectively, discovering relevant datasets and extracting insights without navigating bureaucratic mazes or technical hurdles. This wasn’t about making everything public, but about making it readily available to those with a legitimate need, securely and efficiently.
• Rigorous Data Quality Control: If the data isn’t accurate, the insights derived from it are meaningless, or worse, dangerously misleading. The hospitals implemented robust processes to maintain high data accuracy and reliability. This involved automated data cleansing routines, standardization of medical terminologies (like SNOMED CT or ICD-10), validation rules at the point of ingestion, and continuous monitoring for anomalies. Imagine trying to identify a rare side effect of a drug if half your patient records misspell the medication or use inconsistent units for lab results; it just wouldn’t work, would it?

This collaborative effort was a powerful demonstration of how harnessing the power of well-managed, real-world data can truly drive innovations in patient care. It promised faster diagnoses, more personalized treatment plans based on aggregated outcomes, improved public health surveillance, and a significantly accelerated pace for medical research. It’s a testament to what we can achieve when we treat data not just as records, but as a living, breathing asset for public good.

Unleashing Discovery: Cloud Storage for Genomic Research

Genomic research—now there’s a field where ‘big data’ barely scratches the surface. The human genome alone is an astonishingly vast dataset, and when you start sequencing thousands, tens of thousands, or even millions of genomes, you quickly move into the realm of petabytes and even exabytes. Traditional on-premises storage solutions just buckle under that kind of pressure. This is precisely the challenge that DNAnexus, a leading biotechnology company, faced. They needed a storage solution that was not only massive but also incredibly agile, secure, and globally accessible. Their answer? A strategic partnership with Google Cloud Storage.

The Challenge: Genomic datasets are unique in their demands. They’re not just large; they’re often composed of numerous, relatively small files (FASTQ, BAM, VCF files) that require high-throughput access for analysis pipelines. Collaborations in genomics are inherently global, with researchers spanning continents. Sharing these enormous files securely and efficiently across different institutions and geographies using traditional methods was a monumental hurdle. Furthermore, the sensitive nature of genetic information meant that any storage solution had to adhere to the highest standards of data protection and compliance, including HIPAA and other global privacy regulations.

Strategic Goals for Cloud Integration:

• Unparalleled Data Accessibility: DNAnexus aimed to provide researchers worldwide with virtually effortless access to vast genomic datasets. This meant breaking down geographical barriers and technical silos, allowing scientists in different labs, universities, and pharmaceutical companies to collaborate seamlessly on shared data pools. Cloud storage, by its very nature, enables this kind of global, on-demand access from any internet-connected device, something that would be prohibitively expensive and complex with a purely on-premise setup.
• Elastic Scalability: The pace of genomic sequencing is only accelerating, and the datasets generated are growing exponentially. DNAnexus needed a storage solution that could not only accommodate current immense data volumes but also scale almost infinitely as new projects came online and sequencing technologies advanced. Google Cloud Storage offers this elasticity, allowing them to provision petabytes of storage with a few clicks, without needing to plan for physical hardware procurement, installation, and maintenance cycles months in advance. It’s like having an infinitely expanding library, always ready for the next big influx of books.
• Ironclad Security and Compliance: Storing genetic data is incredibly sensitive. Any breach could have devastating consequences. DNAnexus required a solution that maintained rigorous data protection standards and facilitated compliance with complex regulatory frameworks. Google Cloud Storage provides multi-layered security features, including robust encryption for data at rest and in transit, granular identity and access management (IAM) controls, and comprehensive auditing capabilities. This ensured that only authorized personnel could access specific data, and every interaction was logged, meeting strict regulatory requirements.

By leveraging the power of Google Cloud Storage, DNAnexus dramatically facilitated faster and more efficient genomic research. Researchers could spin up complex analytical pipelines, process massive datasets in parallel using cloud-based compute services, and share findings with unprecedented speed. This acceleration, in turn, has a direct impact on the pace of scientific discoveries, bringing us closer to breakthroughs in personalized medicine, understanding genetic diseases, and developing novel therapies. It’s a powerful example of how cloud infrastructure isn’t just about cost savings, it’s about enabling entirely new frontiers of scientific exploration.

Preserving the Past, Enabling the Future: Media Archiving in Broadcasting

Broadcasting, particularly radio, has a rich and storied history, often intertwined with national identity and cultural heritage. Bahrain Radio, established way back in 1980, found itself at a crossroads. Decades of valuable content—news broadcasts, cultural programs, interviews, music archives—existed across a myriad of formats, from deteriorating analog tapes to various generations of digital media. The looming threat of content degradation and difficult, slow access to this precious archive necessitated a significant digital transformation to modernize its entire infrastructure.

The Challenge: The problem wasn’t just ‘a lot of files’; it was a complex tapestry of formats, some on the verge of becoming unplayable, others simply unwieldy to search and retrieve. Imagine a researcher needing to find a specific news report from 1995, only to be told it’s on a tape buried deep in an unindexed physical archive. The process was manual, time-consuming, and incredibly inefficient. This not only hindered daily program production—making it difficult to reuse historical clips or create retrospective content—but also jeopardized the preservation of Bahrain’s national media heritage for future generations. The cost of maintaining aging, specialized playback equipment and the risk of permanent data loss were also significant concerns.

The Transformation: A Multi-Petabyte LTO Archive System

Bahrain Radio embarked on an ambitious overhaul, choosing a solution focused on long-term preservation, automation, and cost-effectiveness. The project involved several key components:

• Automation: A crucial element of the modernization was streamlining operations. This meant implementing systems that could automate the ingest of new content, intelligently tag media with rich metadata (allowing for much faster searching and retrieval), and manage the archival workflow without extensive manual intervention. Automation drastically improved efficiency, reducing human error and freeing up staff for more creative or critical tasks.
• Digital Archiving with LTO: For the core of their archival needs, Bahrain Radio implemented a multi-petabyte LTO (Linear Tape Open) archive system. Why LTO? Because for deep, long-term archiving of massive media files, LTO tape remains one of the most reliable, durable, and cost-effective solutions available. LTO offers a high capacity per cartridge, a long shelf life, and, importantly, an ‘air-gapped’ security benefit when tapes are stored offline, protecting against cyber threats. It’s purpose-built for scenarios where data needs to be retained for decades with infrequent access, making it perfect for preserving national media treasures and supporting ongoing program production.
• Prudent Cost Management: While a significant investment, the LTO-based solution was chosen precisely because it offered superior cost-effectiveness for long-term storage compared to, say, constantly accessible spinning disks or certain cloud tiers for deep archives. LTO drives consume less power, require less cooling, and the media itself is relatively inexpensive per terabyte. This ensured the archiving solution met the organization’s needs both technically and financially, providing an optimized total cost of ownership over its lifespan.

This comprehensive overhaul didn’t just modernize Bahrain Radio’s infrastructure; it fundamentally enhanced its broadcasting capabilities. It enabled faster content retrieval for new programming, ensured the continuous operation of their broadcast services, and most critically, secured and preserved its rich media content, a vital part of the nation’s cultural memory, for generations to come. It’s a vivid reminder that data storage isn’t just about today’s needs; it’s also about safeguarding yesterday’s legacy for tomorrow’s audiences.

Powering Discovery: High-Performance Storage for Academic Research

Academic research, especially in scientific fields like astrophysics, genomics, materials science, or climate modeling, often pushes the boundaries of computing power. High-Performance Computing (HPC) clusters are the workhorses here, but effectively supporting them, particularly in a virtualized environment, presents a unique set of challenges. The University of Kentucky, a leading research institution, encountered precisely these hurdles when trying to virtualize its HPC resources.

The Challenge: HPC environments demand an incredibly specialized storage infrastructure. We’re talking about extremely low latency, massive bandwidth, and astronomical input/output operations per second (IOPS) to feed the hungry processors running complex simulations and data analyses. Virtualization, while offering flexibility and resource optimization, can sometimes introduce overhead that impacts these critical performance metrics. The university needed a storage solution that could not only keep up with these demanding requirements but also be flexible enough to support diverse research needs, scalable to handle ever-growing datasets, and, importantly, cost-efficient for an academic budget. They couldn’t afford proprietary, vendor-locked solutions that would break the bank every few years.

The Open-Source Answer: Ceph

In response to these challenges, the University of Kentucky turned to Ceph, an open-source, software-defined storage platform. This was a particularly astute choice for an academic institution, embracing the collaborative spirit of open source while gaining powerful capabilities. Ceph is renowned for its distributed architecture, self-healing properties, and its ability to provide unified storage for block, object, and file data—all from a single cluster composed of commodity hardware.

• Unparalleled Scalability: One of Ceph’s most compelling features is its linear scalability. As research projects expanded and data volumes grew, the university could simply add more commodity storage nodes to their Ceph cluster. This not only increased capacity but also simultaneously boosted performance, allowing the solution to grow seamlessly with increasing research data. This avoids the forklift upgrades often associated with traditional, monolithic storage arrays, saving both time and money.
• Rock-Solid Reliability: In a research environment, data integrity and availability are paramount. Losing weeks or months of simulation results due to a storage failure is simply unacceptable. Ceph’s architecture incorporates data replication and intelligent data placement across multiple nodes, ensuring high availability and fault tolerance. If a drive or even an entire server fails, the system automatically self-heals and reconstructs the data from other healthy nodes, ensuring critical research applications continue to run uninterrupted. It provides a level of resilience that’s hard to beat, particularly at this scale.
• Exceptional Cost Efficiency: As an open-source solution, Ceph eliminates the significant licensing costs associated with proprietary storage vendors. Furthermore, its ability to run on standard, off-the-shelf commodity hardware means the university could avoid expensive, specialized equipment. This drastically reduced both the initial capital expenditure and the ongoing operational costs, making it a highly cost-effective alternative to traditional, often rigid, storage solutions for HPC environments. It’s a smart way to maximize research dollars.

This implementation of Ceph truly empowered the University of Kentucky’s researchers, providing them with reliable, high-performance, and infinitely scalable storage infrastructure. It fostered advancements across various scientific fields, allowing for larger simulations, more complex data analyses, and ultimately, accelerating the pace of discovery. It also champions the idea that often, the most innovative and cost-effective solutions come from collaborative, open-source communities.

Untangling the Web: Data Vault Modeling for a Merged Travel Giant

The world of mergers and acquisitions (M&A) is often a wild ride, isn’t it? While the business case for combining entities might be clear, the reality of integrating disparate IT systems and, more specifically, fragmented data landscapes can be a monumental headache. Imagine a large travel company that grew primarily through a series of acquisitions. Each acquired entity brought its own legacy systems, its own operational databases, and often, its own data warehouse, leading to a sprawling, inconsistent, and incredibly inefficient data environment.

The Challenge: The company was grappling with a spaghetti-like mess of disparate legacy systems and data warehouses. Customer data was duplicated across multiple platforms, often with conflicting information. Booking details resided in one system, loyalty program points in another, and historical travel patterns in yet a third. This meant getting a unified, accurate view of a single customer, let alone the entire business, was almost impossible. Their existing data warehouses were rigid, slow to adapt to new business requirements, and the reporting process was sluggish, hindering timely decision-making. Every time a new system needed to be integrated, or a new report requested, it felt like rebuilding the entire data pipeline from scratch.

The Objectives for a Data-Driven Transformation:

• Modernize the Infrastructure: Their primary goal was to replace outdated, inflexible data warehouses with a more agile, efficient, and future-proof system. They wanted to move away from rigid, schema-on-write approaches that struggled with evolving business needs and new data sources.
• Consolidate Data Sources: A crucial objective was to integrate the myriad of disparate data sources—from booking engines and payment gateways to customer relationship management (CRM) systems and marketing platforms—into a single, unified data platform. This would provide a ‘single source of truth’ for all critical business data.
• Enhance Reporting Responsiveness: Business leaders desperately needed faster, more responsive data reporting. They wanted to quickly analyze trends, assess campaign performance, and make real-time operational adjustments, something their legacy systems simply couldn’t deliver. Slow reports mean delayed decisions, and in a fast-paced industry like travel, that can be incredibly costly.

Adopting Data Vault 2.0 Methodology

The travel company wisely opted for the Data Vault 2.0 methodology. What exactly is Data Vault? It’s a hybrid data warehousing approach designed for enterprise-level data integration, combining the best practices of third normal form (3NF) and star schemas. Its core components—Hubs (representing business keys), Links (representing relationships), and Satellites (representing descriptive attributes and context)—create a modular, auditable, and historically accurate data architecture. It’s built for agility, scalability, and flexibility, perfect for complex, evolving environments like those resulting from M&A.

This implementation resulted in a remarkably modular architecture that elegantly streamlined data ingestion, integration, and delivery processes. Data Vault’s design ensures that data from new acquisitions can be quickly integrated without disrupting the existing structure, a huge benefit for a company still growing through M&A. It also provided a complete audit trail for all data, a critical requirement for compliance and financial reporting. Most importantly, it empowered the business with a unified, high-quality view of its operations and customers, supporting smarter, faster decision-making and sustainable business growth. It’s like moving from a rickety, patchwork bridge to a robust, modular highway system that can easily add new lanes as traffic increases.

The Unseen Backbone: Why Strategic Data Storage Matters

These diverse case studies paint a vivid picture, don’t they? They illustrate unequivocally that strategic data storage is far more than just provisioning disk space. It’s about empowering innovation, ensuring operational resilience, unlocking competitive advantage, and safeguarding invaluable assets—be they genomic sequences or national broadcast archives. Each organization, regardless of its industry or size, faced unique pressures and found tailored solutions that fundamentally reshaped their capabilities.

From the blazing speed of flash arrays in manufacturing to the global accessibility of cloud in genomics, the enduring reliability of LTO tape for media preservation, the open-source agility of Ceph in academia, and the structural elegance of Data Vault in complex enterprise environments, the landscape of data storage is rich and varied. The underlying thread connecting all these success stories is a profound understanding of organizational needs and a proactive approach to addressing them with the right technology.

As data continues its relentless growth, fueled by everything from IoT to AI, the importance of foresight in storage strategy will only intensify. What’s next? Perhaps even more pervasive edge computing models, or maybe even quantum storage making its way from labs to production environments. One thing’s for sure: adapting, innovating, and investing wisely in your data’s home won’t just keep you afloat; it’ll propel you forward. It’s time to stop seeing storage as a mere utility and start recognizing it for the strategic asset it truly is.

References

• IBM Storage FlashSystem case studies. (ibm.com)
• Data Dynamics case studies. (datadynamicsinc.com)
• Good practices for clinical data warehouse implementation: a case study in France. (arxiv.org)
• Case Study | Google Cloud Storage. (cloud.google.com)
• XenData case studies. (xendata.com)
• Ceph case studies. (ceph.io)
• Data Vault case studies. (data-vault.com)