3 Pillars of GDPR Compliance
The Forrester report “Predictions 2018: A year of reckoning” predicted that 80% of firms affected by the GDPR will not be able to comply with the regulation by the time it comes into force on May 25. Of those noncompliant firms, 50% will intentionally not comply.
Compliance does not have to be difficult. Imagine if it were possible to facilitate GDPR compliance with a mature technology that facilitates the significant reduction in costs associated with traditional replication methods of data integration. This is possible! Data virtualization is a mature, cost-effective technology that enables the three pillars of GDPR compliance.
GDPR compliance requires the right combination of people, processes, and technology. From a technology perspective, no single tool can implement compliance; Companies need multiple technologies, such as data governance, data quality, and data integration solutions. Data virtualization can play an important role in this compliance ecosystem, supporting the three most important pillars of GDPR compliance:
1. A Single View of Data Entities — The ability to create a complete view of a data subject in an agile manner, which enables companies to present the most accurate data about an individual
2. A Self-Service Data Catalog — enabling companies to find the data assets that are in scope for GDPR compliance, and then categorize, annotate, and document them
3. Privacy by Design — ensuring that privacy is considered during the design process so that data delivery and access mechanisms are compliant from the outset, rather than added in retrospect.
Companies may have existing master data management (MDM) systems that consolidate and master entities, but these lack the context of the transactional information that may exist in other systems. Data virtualization is ideally suited to combine the sources of master data with transactional information distributed around the enterprise to provide a full 360-degree view of up-to-date customer information.
Data virtualization greatly assists with providing single views of entities like customers and is typically used in MDM initiatives in three ways:
Often, self-service initiatives designed to provide easier access to data for consumers fail to deliver. Exposing more data sources to consumers ends up in more requests to IT, more complexity and backlogs, and more data replication, resulting in increased governance and compliance issues.
Data virtualization provides a solution to this: exposing curated information in a business-friendly form that is easy to access. However, unlike traditional approaches, which physically consolidate and transform the data, approaches that are also slow and expensive to maintain, data virtualization provides a more agile means of virtually consolidating the data.
With data virtualization, data sources can be quickly exposed through a curated logical layer via a canonical model that is more easily consumed and understood by the business. Business analysts and developers are then able to build their own views of the data based on this model to suit their needs, with less reliance on IT.
To support this, data virtualization platforms provide a data catalog that exposes the metadata of the curated views defined in the virtual layer. This allows users to discover the data that is available in the virtualization layer through a web browser, promoting re-use of the data and self-service for business users.
From a compliance perspective, users can use the catalog to categorize and annotate information published via the views provided in virtual layer, by adding business terms and descriptions, business categories, and tags. These can be searched, together with view metadata and lineage information, enabling companies to understand where their sensitive information resides and how it is published to consumers.
