1.1 Concepts

The concepts relevant to the Data Strategy, such as data and information, or artificial intelligence, machine learning and advanced analytics, are often used interchangeably. In this document, these concepts are used as follows (a more comprehensive list of concepts is included as an appendix)[1].

Data is ‘raw information’ stored in information systems that can be processed by machines. Data can be either structured data, which has semantic meaning defined by metadata, or unstructured data, which does not. Information is interpretable structured data that can be refined into knowledge. Knowledge, information and data can also refer collectively to the knowledge, understanding (causes of and connections between things) and wisdom (based on wide-ranging experience and learning from it) refined and internalised from information.

Artificial intelligence generally refers to autonomous, learning and predictive algorithms and systems (including robots). Under the concept of artificial intelligence, machine learning refers to solutions in which a machine learns from data either with the help of teaching data (supervised) or independently (unsupervised) and adapts its own behaviour toward a given goal based on received feedback. Advanced analytics refers more generally to a group of analysis methods broader than traditional statistics that utilise data, such as dynamic optimisation, simulations and algorithms, which fall under the concept of artificial intelligence described above.

[1] The concepts are largely aligned with the definitions of the Finnish Ontology and Thesaurus Service’s (Finto) Information Terms glossary (https://finto.fi/tt/fi/) (see the concept models for ‘knowledge, information and data’ and ‘data (machine-readable information)’)