While enterprise data virtualization (EDV) has been around for more than a decade, it has recently been gaining momentum. Many enterprise architects see the opportunity in data virtualization (DV) and are not standing idly by — 56% of global technology decision makers in Forrester’s 2017 survey told them they have already implemented, are implementing, or are expanding or upgrading their implementations of DV technology, up from 45% in 2016. The reasons include:

  • Enterprise data virtualization simplifies complexity:Data architects use data virtualization to present a rationalized view of data made increasingly complex by increasing data volume, regulatory issues including the EU's General Data Protection Regulation (GDPR), and the need to integrate a broad and varying set of data sources such as data lakes, Hadoop clusters, and multiclouds.
  • DV better serves real-time requirements.Enterprise architects are finding that traditional data architectures are failing to meet new business requirements, especially around data integration for streaming analytics and real-time analytics.
  • Enterprise Architect (EA) pros have been applying it to more industries and use cases.In the early years of this market, data virtualization solutions were mostly used in the financial services, telecom, and government sectors. However, over the past five years, Forrester has seen a significant increase in adoption in other verticals, such as insurance, retail, healthcare, manufacturing, oil and gas, and eCommerce. In addition, many implementations have moved from single-use case deployments to more enterprisewide strategies supporting multiple use cases, as architects have realized EDV's importance to address their growing data needs.

Data virtualization provides an agile data platform to support new and emerging business use cases. It delivers a data services layer that integrates data and content on demand from disparate sources in real time, near real time, streaming, and batch to support a wide range of business processes. Automated processes can update, transform, or cleanse data provided through the data services layer. A critical component of data virtualization is the metadata catalog, which keeps track of all data, its location, availability, and state and ensures trusted and timely data. Data virtualization also supports transactions that write back to the original data sources, whether online or offline, on-premises, or cloud. EA pros like its automation and self-service capabilities for data integration, access, and management, which reduce time and effort to support new business use cases. They have been expanding beyond customer analytics to support analytics for social media, the internet of things (IoT), fraud detection, and integrated insights. The top DV use cases are:

  • A 360-degree view of the customer.Emerald may want to learn about customer reaction to a product launch to compare it with previous launches or find ways to minimize customer churn. When properly configured by architects, DV solutions can quickly extract, transform, and process data from social networks, such as Facebook and Twitter, and then integrate with clickstreams, data warehouses, and CRM applications to create a 360-degree view of the customer with no coding or technical programming knowledge. In addition, data virtualization is agile enough to support new sources quickly and adapt to changing business demands.
  • IoT analytics that deal with integrated data.Data virtualization offers the ability to leverage large volumes of IoT data from sensors and devices stored in Hadoop, Spark, or NoSQL repositories, along with historical data to perform analytics. IoT analytics on DV platforms enables manufacturers to predict machine failures, minimize or eliminate production slowdown, and support reordering machine parts proactively.
  • Real-time data sharing and collaboration data platform.Data virtualization is more than a data access layer — it's a data platform that enables multiple lines of business (LOBs), business analysts, and technology organizations to work together to enable data sharing and collaboration across the enterprise. Data virtualization offers the ability to seamlessly deliver a unified and trusted view of trusted enterprise data to application and analytical tools.
  • Securing sensitive data through centralized access.Several financial services and healthcare organizations are using data virtualization to protect and control sensitive data. Data virtualization helps them centralize data access through a common data layer, ensuring the regulated control of sensitive data, especially when it comes from disparate sources. In addition, some solutions offer a bypass option when necessary to sidestep the security policies at the DV layer and let the data source deal with authorization and access control.
  • A self-service data platform for both technology and business users.For business users, a DV platform helps deliver easier and faster discovery, navigation, and consumption of data. For the tech organization, data virtualization enables developers and architects to map data sources quickly, ensure granular data security and continuous availability, and focus on business issues rather than deal with technology challenges.
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