Glossary: Do you speak Insights?

AI / KI

Artificial intelligence or AI refers to a subfield of computer science where computer programs simulate intelligent human behaviour and decision-making structures. AI particularly focuses on machine learning. The term was originally coined by John McCarthy, an American computer scientist, who used it as the heading of a conference project proposal in 1956. The conference discussed, among other things, new programs capable of playing chess and checkers.
In Customer Insights, AI is used to enrich and analyse the merged data, including through machine learning.

Big data

Big data refers to a constantly evolving concept: it denotes a large volume of data that cannot be handled with traditional analysis and processing methods. The term also encompasses the new technologies that enable the processing of such vast amounts of data. Additionally, it signifies the societal transformation brought about by digitisation and the increasing availability of data.
The term “big” in big data refers to the four dimensions of volume, velocity, variety and veracity – that is, the scale of the data, the speed of data generation, the diversity of data types and sources, and the authenticity of the data.

Big data analytics

Big data analytics encompasses the processing, examination and extraction of specific information from enormous volumes of data. Its aim is to gain new insights. Big data analytics is employed by businesses to make more informed decisions and gain a competitive advantage. It is also used by government entities, such as in criminalistics, as well as by health authorities and medical research to predict epidemics, for example.

Business Intelligence (BI)

Business intelligence, abbreviated as BI, is a term originating from business informatics that refers to the systematic analysis of companies’ data to support businesses’ decision-making. Data are collected and evaluated through processes such as customer data platforms. The deployment of BI aims to increase value creation and minimise costs, among other objectives.

Customer data platform (CPD) vs. customer relationship management (CRM)

A customer data platform (CDP) aids in the management of customer data, while customer relationship management (CRM) helps with managing customer relationships. These two platforms are not mutually exclusive but are often used together to ensure efficient operations. In a customer data platform, data related to customer behaviour are collected, whereas CRM focuses on organising and managing interactions between customers and teams. For instance, when using a customer service solution within a CRM, it is possible to access customer contact information and their interaction history with the sales or support team. On the other hand, the data in a customer data platform capture every step, from initial contact to transactions, social media responses and online store behaviour, enabling the creation of a unified profile. Customer Insights, for example, is a customer data platform that can be used as a stand-alone product within the Power Platform, independent of CRM or Dynamics 365.

Machine learning

Machine learning refers to an artificial system that generates knowledge from experience. This “experience” can consist of examples provided during a learning phase, from which a model is built using algorithms based on patterns and regularities.

Profiling

Profiling involves creating a profile or comprehensive image of an individual, which can be used, for example, in job placement or criminalistics. Profiling entails the integration and analysis of various data sources.