Digital evolution has brought several improvements: the volumes of personal data collected, their diffusion across corporate systems and the opportunities generated in order to produce personalized customer experiences.
At the same time, the regulatory obligations for personal data managers have also increased (e.g. GDPR, IVASS Regulation no. 38/2018, Bank of Italy Circular 263 and 285, etc. ..).
A correct management of this data is therefore crucial to avoid economic and reputational damage due to improper use.
Data privacy aims to de-identify the data management through various anonymisation techniques in order to allow its use while reducing the regulatory constraints that need to be observed in order to be compliant.
Among the main problems related to the lack of data privacy management we can find:
The cloud data platform exploits the peculiarities of the cloud to acquire, transform, store and make accessible a potentially unlimited amount of data, reducing operational costs and increasing development agility.
Once acquired in batch or real-time mode, the data is stored and consolidated within the data lake by on-demand integration processes.
On top of the data lake consisting of a scalable and cost-effective storage system (i.e. object storage), query engines are grafted to access the data. Depending on the type of access pattern, it is possible to have different types of query engines.
It is also possible to have a data visualization tool to provide analytical consumers with a single semantic data access layer, thus masking the underlying fragmentation into multiple query engines.
