Data virtualization is an increasingly important data integration capability. As we explore in this short white paper, data integration enables organizations to make informed decisions across their businesses and operations and is foundational to end-to-end automation. This level of automation and data-based decisions define native digital organizations, as they boost customer experience and operational efficiency, enabling faster time to market and working within an ecosystem, among other things.
So successful are these approaches, native digital companies are among the world’s most valuable and profitable, and their non-native counterparts are scrambling to emulate their strategies and success through digitalization.
Bringing data together is the crucial first step. Data virtualization is one option to achieve this, as it is a unified, virtual data layer through which enterprise applications and users can access any information regardless of its location, format, or protocol. Enterprises can choose to use methods that best suit their work function, such as data discovery and search.
Data virtualization helps organizations automate, secure, and integrate disparate data sources across hybrid and multi-cloud environments, in real-time or close to it, according to Forrester Research. It notes that enterprises are increasing investments in data virtualization to gain insights for business initiatives from operational dashboards to real-time analytics, customer intelligence, reporting, and more.
Data virtualization can support staff, external users, and partners who need instant access to information and insights to make informed business decisions. It can also be used to pull together, combine and share data sets to enable collaborative efforts between partners.
Introduction – What is data virtualization, and how does it fit into the integration landscape
In effect, data virtualization is a logical data layer that secures and governs the unified data centrally. It then delivers it to applications used by business users in real-time or something close to it, allowing access across disparate systems, protocols, data formats and structures, and more.
It provides real-time data access, which overcomes the limitations of older extract, transforms, load (ETL) tools, and supports centralized security and access control for data across the organization for internal applications. For external applications, virtualized data can be securely exposed through an API gateway.
A data virtualization platform can be a terrific asset for companies wanting to get the most from their data.
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