What is a virtual data layer?

A virtual data layer is one virtual place to go for all your data. It combines all of a company’s diverse, distributed data sources, whether on-premises or in the cloud, and enables centralized access to data anytime, anywhere. Data-driven organizations deploy a virtual data layer to access, combine, and provision all of its enterprise data to meet business requirements.

Often, companies waste too much time trying to find, understand, and access the right datasets. Instead of spending up to 40 percent of your time delivering and securing data, you can employ a virtual data layer to break down silos and spend more time analyzing data. With a modern virtual data layer, organizations can ensure that anyone in the business can easily access trustworthy and consistent data.

A virtual data layer allows organizations to integrate disparate data sources without data replication or costs associated with physically moving the data. This ensures that businesses can access and use data more efficiently to deliver greater business value. With a centralized, virtual place to go for all your data, you can optimize operations, create compelling customer experiences, and innovate intelligently with connected digital products.

The importance of a virtual data layer

Data can be both an opportunity and challenge to companies today. Across today’s complex data landscape, organizations must ensure everyone can access the analytic data they need. Those who are able to take advantage of a modern virtual data layer to enable users will become the leaders, while those who don’t will quickly fall behind.

Unfortunately, most data professionals today spend more time tracking down the right data and preparing their data than performing analysis. It is critical that businesses find ways to decrease this time spent describing, searching, preparing, and governing their data and increase the time spent actually gaining insight from the data. Otherwise, there is money left on the table and lost value from costly data investments.

To overcome this challenge, organizations must employ enterprise data virtualization, or a virtual data layer, to deliver agile, self-service access to all their data. A modern virtual data layer eliminates these data bottlenecks to business success, allowing businesses to fulfill their analytic data needs at scale with breakthrough speed and cost effectiveness.

Employing a virtual data layer means providing data that is:

  • Agile and Flexible: A virtual data layer allows organizations to access, transform, and deliver data from distributed data sources, including traditional enterprise, big data, cloud, and IoT at a fraction of the time, cost, and rigidity of traditional physical warehousing and ETL.
  • Immediately Available: Organizations can get data up-to-the-minute and deliver it to analytics and applications whenever it’s needed.
  • Governed and Secure: With a virtual data layer, organizations can ensure data is consistent, high quality, and under control with all the governance and fine-grained data security required.
  • Business-friendly: Virtual data layers transform native IT structures and syntax into easy-to-understand, IT-curated data services that are easy to find and use via a self-service business directory.
  • Enterprise Grade: A modern virtual data layer allows organizations to support multiple lines of business, hundreds of projects, and thousands of users with consistent, trustworthy data.

Implementing a virtual data layer may start small and grow with business needs. For example, a business may start with small data projects that have a time-to-solution focus and accept good-enough engineering but then scale up to enterprise-scale standardized data services that ensure data consistency and engineering for scale.

Benefits of a virtual data layer

Investments in a virtual data layer pay off quickly. By implementing a virtual data layer, organizations can access any data source, combine data silos, and provide 360-degree views of their data to everyone across the enterprise. This holistic data integration approach lends to several benefits, including the following:

  • One Place for All Your Data: A virtual data layer centralizes your data, creating a single place to access all your data, including both data stored on-premises and in the cloud, with semantic consistency.
  • Business-friendly Views: With a virtual data layer, organizations can easily create business-friendly, self-service views of their data. This is done by eliminating the traditionally complex data integration technologies, formats, protocols, and locations so that the data becomes more approachable and user friendly.
  • Current, Up-to-date Data: A virtual data layer allows you to get the most current data on demand anytime in any format necessary for any business use case. Businesses no longer need to wait for a nightly batch run to refresh their datasets.
  • Decreased Data Replication: By eliminating unnecessary data replication with a virtual data layer, organizations can increase agility, mitigate risk, and reduce costs.
  • Faster Data Fulfillment: Virtual data layers enable accelerated provisioning of new datasets, allowing organizations to keep pace with their ever-changing data requirements.
  • Centralized Security and Controls: With one place for all your data, enforcing security and managing data controls for every user across all your datasets becomes much easier.

Required capabilities of a virtual data layer

To successfully implement a virtual data layer, organizations must have the following data management capabilities:

  • Data Virtualization: Access, combine, and provision all your enterprise data with data virtualization.
  • Master Data Management: Model, govern, and share all your master and reference data using master data management.
  • Metadata Management: Discover, catalog, and govern all your enterprise metadata with metadata management.

Virtual data layer use cases

A virtual data layer is useful beyond data management, including the following diverse use cases:

  • Analytics Use Cases: Data virtualization can be useful in analytics use cases such as prototyping for physical data integration, data preparation, logical data warehousing, and opening up data access.
  • Operational Use Cases: A virtual data layer can help improve operations through master data management, legacy system migration, and application data access.
  • Emerging Use Cases: Some new use cases for a virtual data layer include cloud data sharing, edge data access in IoT integration, data hub enablement, and data and content integration.

Furthermore, a virtual data layer can be leveraged by organizations across various industries, including:

  • Financial Services: A virtual data layer can help financial companies manage fixed income risk, improve trading reconciliation, accelerate new client onboarding, address mortgage data complexity, enrich cash management clients, and empower data democracy.
  • Communications and Technology: Communications and technology companies can use a virtual data layer to differentiate market research services, increase revenue per customer, create a virtual customer data lake, enable leading edge innovation, implement a real-time operational data store for billing and marketing, optimize customer care, manage customer entitlements, and improve customer insights.
  • Manufacturing: A virtual data layer for manufacturers means global supply chain optimization, optimizing factories and logistics, improving IT asset utilization, and differentiating via digitization.
  • Healthcare: In healthcare, a virtual data layer can enable new product innovation, accelerated merger and acquisition synergies, efficient claims analysis, and improved patient care.
  • Energy: Energy companies can utilize a virtual data layer to optimize upstream production, manage wells and repairs, perform offshore platform data analytics, optimize cross-refinery processes, and ensure master data quality.
Virtual data layer diagram