The world as we knew it has been changing rapidly. Today we want to access our applications and servers that host, safely, continuously, without fail, in extended hours (24x7), from any device, anywhere.
The information collected in all business processes, the proliferation of devices and the expectations of users/consumers are growing exponentially. Data volume, structured and unstructured, continues to grow, and organisations continue to struggle to leverage all of the data to make the best business decisions. Even our working methods have changed with the advent of the pandemic COVID-19.
Which require to organisations quickly monitor these new trends and rapidly adapt to this new “normal”. The focus is now on efficiency, more proximity to the customer and cost optimisation.
But how can we achieve seamless data integration of data, providing the possibility of obtaining insights and information in the most agile, flexible, and cost-effective way? Well, with data virtualisation.
Data virtualisation eliminates the need for physical data replication through an intermediate logical layer, which integrates data virtually, regardless of the location and format of the data. It enables business stakeholders to define their semantic models and maintain the traceability of data from the source, including all transformations and definitions.
Compared with data warehouses and data lakes, data virtualisation has more flexibility, no data replication, real-time delivery, data abstraction, is more agile and simple. Data virtualisation forms a unified data-access layer above the different data sources. And it provides data scientists with integrated real-time views of the data, across all of its existing locations, without having to move any data from its original locations to a new, centralised repository, such as a data lake or data warehouse.
Data virtualisation has benefits in several areas, and it can really make a difference in any situation where a large amount of data exists in different sources, or when a single data source is very complex, and it must first be integrated and analysed before it can enable business stakeholders to make decisions.
For example, in the financial area, with the help of data virtualisation, banks can provide different data to different departments in the organisation, and at the same time a clear understanding of which data is the certified data because of the ability to easily track the data lineage. Each department needs different information, the data required by the marketing manager is very different from the data used by the counter staff. With data virtualisation, banks can easily create views that integrate all of the necessary data into service queries, and the results will quickly appear in the final application.
Another example, telecom companies with data virtualisation can improve customer care and customer services by accessing real-time data and bring more agility to business services.
These are just some examples of how data virtualisation can be applied, but it is extremely flexible and can bring benefits to any industry facing data integration and data management challenges.
If you need help with your data integration and have data management challenges, please contact us!