Create meaningful business changes with data per il business con i dati

With the explosion of data, information exchanges and the increasing number of channels, a paradoxical point is being reached.
While information is indeed a fundamental asset, a real differentiator and a great competitive advantage, the explosion of volumes can make the Information System cumbersome and unclear, becoming an obstacle to innovation and transformation.

Data, data, data

The landscape is quite complex: historically on-premises data, alongside those added in the cloud, increasingly frequent exchanges with suppliers and customers. Additions are sometimes made one at a time, sometimes in bulk, putting control and maintenance levels at risk over time. More than half of companies say they want to replace their on-premises data warehouses with cloud ones, but that doesn’t solve a data structure problem.

To cope with the prospects of rising volumes, such as iot sources, many look at the data lake that, as is known, arose from the need to exploit big data and take advantage of raw data, structured and unstructured granular. Data lakes and data warehouses, while both widely used, are not interchangeable repositories. Although we have been in the era of Big Data and Data Virtualization for some time, the request to create data warehouses to allow users to perform high-performance analyses is still current: for some of these contexts we addressed and solved the challenge with data warehouses powered by data lake.

Catalogue, search, discover and govern data

To break down silos and improve access, many organizations are switching to data virtualization to catalog, search, discover, and govern data and its relationships. Unlike ETL solutions, which replicate data, data virtualization leaves data in the source systems, simply exposing an integrated view of all data to data consumers. Data virtualization shows that data connection is far greater than data collection.

As we all know, data virtualization is highly valued for integrated data access and sharing, especially in light of regulatory constraints on physical data handling, but we must not forget that data virtualization is not suitable to support complex transformations, making it less appropriate for heavy operating loads.

Dynamic data virtualization offers all the advantages and functionalities of normal virtualization, but allows a much higher level of flexibility. This hybrid approach allows you to determine which data should be virtualized and which should be kept persistent in a staging datastore, thus optimizing the expected performance levels.

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