The data economy is based on institutions and companies' ability to manage the increasing amount of digital information. It consists of an economy in which institutions and companies interpret data correctly and significantly increase their performance.

The actions required for this are essentially the so-called "3Cs": capture data, compute it and communicate the results. Thanks to the Internet, the cost of data transmission and computation has been considerably reduced through the worldwide spread of Cloud Computing and its integration with Cloud Integrated Networks.

The cost of information has decreased and is no longer an obstacle to overcome.

This current situation allows the development of knowledge, the possibility for an organisation or an individual to have more and more relevant information to operate in a specific context or for a given objective.

The data economy covers all sectors, including PA, on a large scale.

AgId has defined the Open Data NoiPA portal, developed as part of the Cloudify NoiPA programme, as an enabling platform capable of achieving a dual objective: ensuring transparency and maximising the information assets of PA staff.

The data economy also includes the concepts of security of sensitive data and greater attention to privacy, the key points of Web Analytics Italia (WAI), a national platform for the collection and analysis of statistical data relating to the traffic of Italian Public Administration sites and digital services.

The whole universe of the data economy revolves around the ability of institutions and companies to make maximum use of the data available, with the main objective of improving their performance and the quality of services.

It is therefore essential to introduce greater integration and more efficient elasticity between information flows, as well as offering a faster and more intuitive reading key in a multi-channel, that is multi-device and multi-platform perspective.

It is very useful for organisations to understand which type of "big data scenario" a specific business need belongs to. It is therefore possible to observe a process divided into two steps:

• The first one consists in associating the business need to a big data type (by carrying out a qualitative analysis of the problem to be solved, in order to find a big data type associable to the problem itself);
• The second step consists in analysing the big data type (classifying the characteristics of the big data scenario and obtaining important indications for the development and implementation of the application).