Data Virtualization: 8 Industry-specific Use Cases in 2023

ByPaul Christiano Last Update on

Data virtualization enables unified data access across disparate sources without replication. Let‘s explore 8 real-world examples of data virtualization delivering business value.

1. Real-time Analytics for Trading in Capital Markets

Capital markets firms like investment banks run on real-time data and analysis to identify trading opportunities. Data virtualization is uniquely suited for low-latency analytics by providing quick access to live data.

For example, an equities trading desk needs to analyze real-time market data, news feeds, research reports, risk models, transaction data and more to inform split-second trading decisions. Replicating all this data into a warehouse is slow. Data virtualization can instantly query and join data from multiple sources for analytics at wire speed.

According to Gartner, "Analytics use cases requiring subsecond response times are best served by data virtualization."

A major European investment bank implemented data virtualization from AtScale to empower real-time analytics across global equities trading desks. This enabled faster insights and better-informed trading decisions.

Here are some key benefits data virtualization provided:

  • 50% lower latency compared to querying data in silos
  • Millisecond query response for time-sensitive analytics
  • Virtual access to live feeds like market data and news for real-time decisions
  • No disruption to existing infrastructure and data stores

2. Customer 360 View for Personalization in Retail

Retailers strive to engage customers with personalized experiences across touchpoints. This requires a unified 360-degree customer profile. Data virtualization helps retailers achieve this single view.

Let‘s see how a multichannel retailer can leverage data virtualization:

  • In-store purchases – Point of sale and inventory data
  • Online orders – Ecommerce platform data
  • Mobile app activity -clicks, adds to cart, purchases
  • Call center interactions – service call logs
  • Loyalty programs – purchases, rewards, tiers

Rather than moving this customer data into a central warehouse, data virtualization leaves it in source systems and accesses it on demand. Retailers can stitch together a comprehensive profile to drive personalization.

The Home Depot implemented data virtualization from Denodo to gain a 360-degree customer view across in-store, online, mobile and catalog channels. This improved customer satisfaction through personalized promotions and consistent experiences.

Here are some results:

  • 360-degree view across 15 different customer data sources
  • 20% increase in email/SMS open rates with targeted campaigns
  • 10% growth in loyalty program membership through personalized incentives

3. Compliance Reporting in Pharma

Pharma companies undergo stringent regulatory audits to verify compliance with rules for drug safety and efficacy. This requires reporting across fragmented lab, clinical trial, and monitoring data. Data virtualization simplifies compliance reporting by integrating distributed data sources.

For example, a pharma company may need to produce reports spanning:

  • Clinical trial management systems
  • Drug safety databases
  • Bioassay analysis data
  • Manufacturing quality systems
  • Regulatory submissions

Rather than building point-to-point integrations or staging all this data centrally, data virtualization federates it on demand for reporting.

Astellas Pharma implemented Denodo data virtualization to accelerate compliance reporting across labs in over 20 countries. This reduced compliance report generation time by 50% and lowered data warehouse costs.

Key results included:

  • 50% faster compliance reporting
  • 30% reduction in compliance costs
  • Virtual access to 200+ global data sources

4. M&A Analytics in Banking

Mergers between banks require consolidating customer data like accounts, transactions, and products across different systems. This can take months of risky data migration.

Data virtualization reduces M&A integration effort by virtually consolidating distributed data for analysis. Key data remains in source systems without disruption.

For example, when merging with another bank, data virtualization can instantly unite for analysis:

  • Customer accounts and profiles
  • Transactions and transfers
  • Loans and mortgages
  • Card payments
  • Wealth management assets

This enables executives to operate as a combined bank on day one and accelerate strategic decisions.

According to 451 Research, "Data virtualization is being leveraged across capital markets and investment banking, particularly to connect data trapped in siloed repositories following mergers and acquisitions."

5. Supply Chain Analytics in CPG

CPG companies need broad supply chain visibility to improve forecasting, reduce waste and optimize inventory. Data virtualization delivers an integrated view across the value chain:

  • Supplier ERP data
  • Manufacturing outputs
  • Inventory levels across distribution centers
  • Transportation milestones
  • Point of sale scanner data

Rather than moving supply chain data through complex ETL, data virtualization leaves it in source systems and integrates it virtually for analysis.

The CPG firm Mondelez uses Denodo data virtualization to unify data across manufacturing plants, suppliers, and distributors. This improved supply chain efficiency 6-8% by optimizing production and inventory.

Key benefits included:

  • Unified view across 80 internal and external data sources
  • Optimized supply chain with 6-8% efficiency gains
  • Faster access to supply chain data reduced analytics time by 90%

6. Logical Data Warehousing in Telecom

Telecoms have vast amounts of subscriber, network, and operations data spread across legacy systems. Consolidating this into a physical warehouse is challenging.

With data virtualization, telcos can create logical data warehouses that connect disparate data virtually on demand. This eliminates duplication across siloed repositories.

By federating data from CRM, network, billing and other systems, executives can analyze key metrics like:

  • Customer lifetime value
  • Subscriber churn
  • Usage trends
  • Revenue patterns

Data virtualization reduces the need for ETL since data isn‘t replicated across systems. This lowers costs while improving analytics agility.

According to Gartner, "Data virtualization capabilities are critical across logical data warehouse environments in telecommunications."

7. Data Lakes Exploration in Media

Media companies accumulate huge volumes of clickstream, social media, customer profile, and multimedia data. Often this resides across owned systems, third-party services, and cloud storage.

Analyzing this data can yield valuable insights but each source has different access methods and semantics. Data virtualization creates a unified virtual layer above the data lake for easy exploration.

This enables media analysts to query and report on petabytes of multi-structured data as if it were one coherent database. Data virtualization eliminates the need to normalize or move the data lake.

For instance, Viacom implemented data virtualization from AtScale to explore consumer insights across a massive data lake. This led to improved targeting and personalization that boosted advertising and subscriptions revenue.

Key results included:

  • Unified access to a 190+ PB data lake
  • Days to minutes reduction in reporting timelines
  • Millions in incremental revenue from data-driven decisions

8. Embedded BI in SaaS

SaaS apps can leverage data virtualization to embed analytics capabilities for customers without managing warehouse infrastructure.

By virtually integrating data from app databases, third-party services, log files and more, data virtualization powers embedded BI in a scalable way.

For example:

  • Analyze usage metrics to improve app UX
  • Integrate payment data for financial reporting
  • Build customer dashboards and reports

Leading SaaS firms like Zendesk use data virtualization to embed analytics into their apps. This empowers customers with self-service insights.

According to Gartner, "Augmenting a SaaS application with embedded data virtualization and analytics can increase its appeal and perceived value."

Data virtualization delivers a semantic data layer that unifies access to distributed data in real-time without replication. As these industry examples illustrate, virtualization helps enterprises accelerate insights and decisions across a variety of data integration use cases.

Leading vendors like Denodo, AtScale, and Hitachi Vantara provide robust data virtualization platforms to drive analytics agility at global firms. With solutions tailored to diverse integration needs, data virtualization reduces complexity and unlocks the full value of enterprise data.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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