TALKING

Data Virtualisation is a modern data integration capability that inserts a ‘ virtual ’ or ‘ logical ’ abstraction layer between the consumers of data and the data sources . This means we can connect to any data source regardless of type , format or location ; combine any data regardless of type , format or location and consume any data regardless of type , format or location .

How does Data Virtualisation Work ?

Unlike ETL solutions and the more traditional approach of replicating and storing data in a central repository , Data Virtualisation leaves the data in the source systems and rather uses the underlying metadata to create a ‘ virtual view ’ of the data . The virtual views can be created in minutes and users can then use these views to build virtual data sets , data marts , data warehouses or data products .

Many organisations are using Data Virtualisation as the ‘ data delivery engine ’ for Data Fabric and Data Mesh architectures or even a ‘ universal data access layer ’. Others are simply using Data Virtualisation to address tactical challenges like delivering urgent data sets quickly and easily or combining disparate data to build a Single View of Party . Whatever the use-case , Data Virtualisation is the quickest , easiest and safest way to deliver enterprise data and is being widely adopted by data teams big and small .

What are the main benefits of Data Virtualisation ?

First of all Data Virtualisation is by far the most cost effective way to deliver data to the enterprise . Eliminating the need to transform , replicate and store data offers a significant saving over the traditional approach . Many users report well over 50 % reduction in infrastructure and integration costs as a result of Data Virtualisation .

Next is how quickly data can be made available to the teams that need it . To connect a new data source is simply a matter of entering access credentials and settings to create a virtual view . This can be completed in minutes and the virtual view is then ready for consumption or combining with other virtual views . The demand for data is increasing daily along with the volume and complexity . The traditional approach simply cannot keep up and a new way is needed .

In most legacy environments we ’ re seeing a hybrid

approach where the traditional , embedded platforms run alongside the more agile virtual layer .

Another key benefit is the ability to integrate any

data with any other data regardless of type , format or location . What would have taken months can now be achieved in hours or days . We ’ ve seen data from multiple sources combined into a reusable data set / product in minutes with zero ETL and zero replication .

While there are numerous other benefits , the final one that I ’ ll mention is security and governance . This is a major challenge for all data teams not only from a technical perspective but more importantly from a user adoption standpoint . How do we get our users to adhere to our security and governance policies ? Simple ! Make the easiest and quickest way to access the data you need also the safest way .

The main reason users create their own rogue data sets is because they can ’ t afford to wait for the data team to deliver . Business often has to wait for months for data and in some cases even longer . The result ? They bypass the data team and do it themselves going directly to the data owners and making copies that either end up in the various reporting and analytics platforms or even in spreadsheets on analysts laptops … With Data Virtualisation , all data accessed through the ‘ virtual layer ’ is auditable and secured based on user roles with encryption and masking applied according to the sensitivity of the data .

What are the main use-cases for Data Virtualisation ?

Fundamentally Data Virtualisation is used to deliver data to the organisation in the quickest , easiest and safest way possible which opens countless

Vincent Gaorekwe , CTO at BITanium

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