1. Engineer compliance with regulatory requirements

Government regulators are implementing laws to protect the privacy and integrity of data related to citizens. Examples of legislation include GDPR and the California Consumer Privacy Act, going into effect in 2020. Such laws will safeguard the data of private citizens by holding accountable organizations who willfully violate consumer privacy by selling or distributing customer data without permission, or who expose consumer information through negligent security practices. In GDPR this requirement is called “Privacy by Design” where data systems must be engineered to secure data from exposure or leakage by default.

As a centralized conduit for orchestrating secure and governed access to the multitude of data sources within the enterprise, intelligent data virtualization is the perfect vehicle for implementing Privacy by Design. Working with virtualized data eliminates the need to create extracts, greatly reducing the chance data will be mishandled.

Intelligent data virtualization also covers a variety of data protection scenarios, including data encryption at rest and in flight, further ensuring Privacy by Design.

2. Create a single logical interface within an increasingly distributed world

Intelligent data virtualization establishes a highly performant, agile and governed view of an enterprise’s analytical information regardless of the user persona or query interface. Every user connecting to the intelligent data virtualization fabric has the ability to track the lineage of their data, resulting in greater visibility and certainty that data sources are reliable and consistent with those of their peers. Data analysts are able to create a shared data intellect where recommendations can be made with confidence that they are aligned among fellow analysts and data scientists.

3. Ensure agile, accurate access to data

Organizations can facilitate the creation of the insight-driven enterprise by leveraging autonomous data engineering and acceleration structures. Intelligent data virtualization applies advanced AI and Machine Learning to study query patterns and database statistics to create highly secure and governed data structures that accelerate a broad range of analytical workloads. In many cases, this creates an order of magnitude performance enhancement while simultaneously reducing overall query cost and complexity. Queries return interactively in seconds rather than minutes or hours. The dramatic increases in performance and agility facilitate the creation of insights while simultaneously steering the experimentation of new avenues for research.

4. Provide Consistent Results Across Analytical Tools

Different departments will naturally develop their own preferences in AI/ML and BI tools. Supporting a variety of user interfaces to the data creates the broadest opportunity for team enablement, but also amplifies the challenges of a single, fully compliant data experience that maintains comprehensive security and governance.

The key in any AI/ML or BI user experience is consistency—without a single location for defining the “business context” within the data in a highly scalable, performant and secure way, skepticism rules the day and room for “interpretation” persists. Yet another benefit of intelligent data virtualization is the consistency created by alleviating the different characteristics of each of the individual tools. When leveraging a variety of data management interfaces and heterogenous physical or virtual data platforms, the semantic translation provided by intelligent data virtualization ensures users receive the same responses, irrespective of platform(s) of choice.