The proliferation of disparate data sources distinguishes today’s data landscape. Easily accessible, well-structured data was once the norm. The “status quo” has been disrupted by the phenomenal growth in the variety and volume of multi-structured data originating from machine and IoT, external, application-oriented, cloud-based, and on-premises sources. Emerging in the wake of this digital disruption is a data-centricity shared by businesses ranging in scale from fast-growing SMB’s to global ecommerce enterprises. These digital businesses are pursuing a common goal of turning raw data into actionable insights quickly. Traditional ETL approaches to data integration are insufficient to meet the necessities of today’s digital businesses which require rapid and agile integration capabilities to support real-time decision making in a highly competitive market. Integration vendors must enable organizations to quickly and iteratively integrate and model data in support of digital business use cases such as those listed at the right. In its Cool Vendors in Pervasive Integration, 2016 report, Gartner advocates “data virtualization” as the go-to technology for digital business data integration:
Internet of Things (IoT), digital business … use cases require data virtualization approaches for integration to achieve fast time to value for supporting analytics and operations.
Data virtualization technology provides the flexibility to rapidly integrate, within a logical data warehouse, multi-structured data from virtually any data store, application or other source. Data can be persisted in memory or in a physical data store as needed for optimal performance. Data virtualization provides the agility to retrieve changes to the data from data sources in real-time. It enables creation of data models to reflect the relationships among data elements and to harmonize the data across disparate data sources. The data models are “dynamic”, able to incorporate changes in a rapid manner. Data virtualization abstracts data consumers from data providers. It exposes data lineage, incorporates data preparation, and enables cross-company data integration. It improves data quality and serves as a common provisioning point from which to access all authoritative sources of data. It supports multiple consumers of the data such as BI/Analytics applications and other applications.
You can read the Cool Vendors in Pervasive Integration, 2016 report here to learn more about the vendors recognized by Gartner for their innovations in support of pervasive integration strategies.