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What would have taken months can now be achieved in hours or days .

opportunities . One of Data Virtualisation ’ s key strengths is combining data from multiple sources into a reusable data set / product which also supports many potential use-cases . Here are some of the most common that we typically come across :

Logical Data Warehouse : Combine data from multiple sources to create a virtual data warehouse , lake , mart or data product .

Single View of Party : Combine data from multiple sources to gain an accurate record of customer , employee , supplier or entity .

Application or Cloud Migration : Use the virtual abstraction layer to shield the user from underlying complexity of the data environment . Data teams can migrate from one application to another with zero impact on the user . Same applies to cloud migration as the user experience remains consistent through the virtual layer while the underlying data platforms evolve .

Cross Border Data Sharing : With strict rules regulating how data is shared across regions and borders , Data Virtualisation provides the ability to connect , combine and consume data without the need to move the data and apply security controls dynamically as required .

Data Factory : Build and deliver reusable data products quickly where the ‘ logic ’ remains in the virtual abstraction layer and not the source or analytics and reporting platforms .

Data Fabric / Data Mesh : Both of these architectures seek to decouple the complexity of the underlying data sources from the consumers and deliver reusable data product to the enterprise . Data Virtualisation plays a key role as the data delivery engine .

Why is Data Virtualisation gaining such traction now ?

The main reason is because it ’ s a great way to connect , combine and consume data . Data teams are under increasing pressure to modernise their platforms to meet the increasing demands for data for analytics and insights . As more and more high-profile organisations adopt Data Virtualisation it gives others the confidence to adopt the new approach . In the past Data Virtualisation was seen as niche and new but now it ’ s mainstream and most data teams are becoming aware of and adopting because it works and they can ’ t continue with the old paradigm .

Where is the best place to start with Data Virtualisation ?

Typically we identify a specific use-case or problem to solve and use that as the starting point . Once the rest of the organisation sees first-hand how it works and the impact then there ’ s no shortage of projects to take on thereafter . So the answer is start small and prove the capability and then expand from there . The goal should be a universal data fabric layer but the practical approach is one use-case at a time . p

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