Data is key to AI.

Data is facts and statistics, numbers and values, collected together for reference or analysis, it is the information stored on or used by a computer.

AI is largely reliant on data, the more data the better.

Data Science pursues the goal to gain knowledge from data. We use the term Data Science to cover the entire process or “data value chain” from collection of the data and processing of the data to modelling the data.

Data modelling is exploring, documenting, mapping out and presenting the data in an easy to understand diagram, or visualisation.

As we said, where AI is concerned, the more data the better. We call very very large data sets (collections of data) “Big Data”. Of course it’s not really the size that matters, it’s what you do with it that counts!

Big Data is characterized by the following five features:

1. the magnitude of data to be processed is very large (volume) 
2. the diversity of data types and sources (variety)
3. the speed at which they occur (velocity) 
4. the valuable data must be filtered out (value)
5. the uncertainty regarding the quality of the data (veracity).

Data Mining is the use of statistical or machine learning methods to detect new relationships and patterns in a data set. The aim is, for example, to make recommendations for decisions or make predictions. So for example when you search for your favourite song on Spotify, the Spotify algorithm can use the data of every other person who has searched for that song. Patterns in the data such as what song is searched for next, what other songs users listen to can then be used to suggest songs for you to listen to next.