Do you feel overwhelmed by the mountains of enterprise data from different sources and no respite in sight? Despite this information overload, does insight seem to elude you? This proliferation of data across multiple platforms and the need for faster insight has made the traditional ETL (Extract, Transform and Load) data process ineffective at handling and integrating voluminous unstructured and structured information.
Fortunately, data integration methods have evolved to address this challenge in the form of data virtualisation. Data virtualisation is a powerful technique that complements existing data warehouse systems by improving business user productivity. It does this by delivering insight and bridging the gap between relational and non-relational information - an inherent requirement for businesses implementing digital transformation solutions.
Data Virtualisation Business Applications
Data virtualisation can help businesses address existing and future business challenges related to data management and analytics. Its role is not limited to the integration of information, but involves abstraction and discovery of enterprise data assets.
Industries such as healthcare, retail, banking and financial services will experience greater ROI by complementing data virtualisation with existing data analytics activities. The five use cases detailed below should serve as a guide for businesses planning to implement the solution.
1) Real Time Analytics
Enterprises must source data in real/near-real time in order to benefit from advanced analytics. Data virtualisation helps achieve this without moving the data from source systems. Instead, it pushes query logic to the underlying data sources and delivers up-to-date data availability for analysis.
2) Data Services
Data virtualisation works by publishing virtual views or virtual data marts such as Data-as-a-Service. The virtual view masks the complexity of sourcing the data and provides an abstraction layer based on governing policies. Once the data is published using REST services, it can be accessed by downstream applications such as mobile applications.
3) Big Data
Data virtualisation supports enterprises on their digital transformation journey by helping them to seamlessly capitalise on big data. Big data technologies are designed to handle large volumes of data at speed and in varying formats. Data virtualisation make this data available without copying it through an abstraction and federation layer that hides the complexities of the big data stores. This makes it easy to integrate data from these stores with structured and unstructured data within the enterprise whilst introducing a layer of security, governance and data services delivery capabilities for big data.
4) 360-Degree View of the Customer
The virtual data layer built using data virtualisation offers a unified, enterprise view of business information. It provides a 360-degree, aggregated view of data from different customer-centric applications, which improves the ability of business users to understand enterprise data.
5) Cloud Data Integration
Most organisations are increasingly adopting cloud computing, which necessitates new cloud sources to be integrated with existing IT environments. Data virtualisation helps by enabling enterprises to maintain a complete view of internal and external information whilst offering the advantages of a cloud-based scalable platform.
Data virtualisation should not be a replacement or used as an alternative to ETL processes. Instead, it should serve as an integral building block in the digital transformation journey. Developed using a modern data architecture frameworks, data virtualisation plays an important role in real-time integration, abstraction and discovery of enterprise data assets.