Our lexicon for a few technical terms

In the open consultations in which the Social Good Accelerator provides an answer, some specific expressions are often used. Here is a brief lexicon compiling definitions of those common concepts.

If you believe a term, which is difficult to understand, is present on our website but not on this page, feel free to ask our team!

Big Data (or large datasets, megadata, massive data):

It refers to a very large set of data that no conventional database or information management tool can really work with and brings together in large families the trillions of bytes of data we produce every day: messages we send to each other, videos we publish, weather information, GPS signals, transactional records of online purchases and many more.

The challenge around the Big Data is considered to be one of the biggest to come in the next decades.

A dataset

It is a collection of related data elements that are associated with each other and are accessible individually or in combination, or managed as an entity.

A dataset is organized in a data structure. In a database, for example, a dataset may contain business data (names, salaries, contact information, sales figures, etc.).

The database itself can be considered a dataset, as can the bodies of data it contains that are associated with a specific type of information, for example, sales data for a corporate department.

Open Data

The practice of “opening” access to one’s data to all, both for consultative purposes and for reuse. It allows the sharing person/structure to contribute to the community and to submit its data to criticism in order to improve it; but also to improve the service rendered to the client/user with better information.

It also allows third parties to incorporate this data into their projects to produce higher value-added services. They can be of public or private origin, produced in particular by a community, a public service, a citizen collective or a company.

Artificial intelligence (AI)

It consists of implementing a number of techniques to enable machines to imitate a form of real intelligence. It is implemented in a growing number of fields of application.

Algorithms are able to optimize their calculations as they perform processing. This is how spam filters become more and more effective as the user identifies an unwanted message or, on the contrary, processes false positives.

While in 2015 the market for artificial intelligence was worth $200 million, it is estimated that by 2025 it will be worth nearly $90 billion.