The world of data marketing is constantly evolving, with new words and expressions appearing and being used, from the simplest to the most abstract.

  • Algorithm

An algorithm is the description of a sequence of steps used to obtain a result from input elements. It is the set of operating rules specific to a calculation; a sequence of formal rules.

  • Artificial Intelligence (AI)

Artificial intelligence (AI) is in fact a discipline that has been in existence for some sixty years, bringing together sciences, theories and techniques (notably mathematical logic, statistics, probability, computational neurobiology and computer science), and whose aim is to have a machine imitate the cognitive abilities of a human being.

  • Business Intelligence (BI)

Business Intelligence (BI) defines the technologies, applications and practices for collecting, integrating, analyzing and presenting data, with the aim of supporting better decision-making in business verticals such as sales, marketing and finance. BI systems are essentially data-driven decision aids.

  • Big Data

The term Big Data was coined in 1997 in an article on the technological challenges of visualizing "large data sets". It refers to information resources whose characteristics in terms of volume, velocity and variety dictate the use of particular technologies and analytical methods.

  • Customer Data Platform (CDP)

A CDP or Customer Data Platform in marketing centralizes and organizes its customers' online and offline data, enabling them to be used more effectively. In this way, the company can segment, target and personalize its marketing campaigns to improve customer relations. This tool can also be used to distribute information (customer data) to other tools.

  • Customer knowledge through segmentation

The principle is to create a typical customer profile based on exogenous criteria (qualification not based on the relationship with the customer). This segmentation can also be used for prospecting.

  • Data-driven marketing

It's about enabling marketers to make decisions based on accurate data and information rather than opinions or hunches.

  • IRIS code

Zones defined by INSEE for census purposes for all municipalities with more than 10,000 inhabitants and most municipalities with between 5,000 and 10,000 inhabitants.

  • Customer Relationship Management - CRM

CRM (Customer Relationship Management) is a strategy that advocates the centralization, storage, analysis and exploitation of an individual's interactions with a company in order to optimize the management and quality of the customer relationship. It also designates a computer solution intended to exploit data collected during exchanges between a prospect/customer and the company for commercial purposes.

  • Data-Driven Marketing - DDM

It's about enabling marketers to make decisions based on accurate data and information rather than opinions or hunches.

  • Data Quality - DQ

Data Quality refers to a company's ability to implement actions to ensure that the data in its information system(s) is correct and sustainable over time. Mastering data quality is an important issue for companies. The aim is to provide all data users with correct, complete, up-to-date and consistent data.

The notion of data quality is a generic term describing both the various characteristics of data, and the set of processes used to guarantee these characteristics. Data is said to be of high quality when it meets the requirements for its use.

  • Data Mining

Customer data management process that operates from different perspectives, establishing relationships and transforming data into useful information for specific customer actions.

  • Data Lake

Collects and stores large volumes of structured and unstructured data. The data is raw. A data lake is used to feed a data mart.

  • Data Mart

A database whose content is related to a business activity and which is created to meet the specific needs of a group of users. This is often (but not always) a partitioned segment in the company's data warehouse.

  • Data Science

Data Science refers to a set of disciplines involving data inference, algorithm development and technology. These aim to solve complex analytical problems. Data science is about using data creatively to generate business value through insights, with the aim of helping companies make smarter decisions.

  • Data Visualization

Also known as Dataviz, this term encompasses a set of tools designed to translate raw data into simplified visual representations to facilitate analysis and understanding. The aim is to enable companies to analyze very large volumes of data, so as to communicate quickly and effectively about them and make decisions.

  • Data Quality Management - DQM

Data quality management is the set of actions and procedures aimed at :

- Ensure and maintain the quality of data meeting the business and technical needs of users within a company,
- Secure it and make it available for operational use.

This is an ongoing process. This process meets two objectives:

- Examine the reliability and relevance of data, essential and structuring for the company, on the basis of quality criteria on the one hand,
- Develop strategies and tools to eliminate data that does not meet these requirements.

Companies embarking on a Data Quality project follow a single guiding principle throughout the project: to transform quality data into useful information.

  • Deduplication

Data deduplication, often referred to simply as deduplication, is a feature that reduces the impact of redundant data on storage costs. When activated, data deduplication optimizes free space on a volume by examining the data it contains and searching for duplicated parts on the volume. The duplicated parts of the volume's dataset are stored only once, and (if necessary) compressed for even greater savings. Data deduplication optimizes redundancy without compromising data fidelity or integrity.

  • Contextual data

Less frequently used because it is more difficult to collect, contextual data provides information about the company's or contact's environment. The typical case of contextual data is that obtained during market research or a survey. Contextual data can also come from highly specialized data from private databases (drug databases, vehicle registrations, etc.) or public databases (census, high-risk areas, etc.).

  • External data

External data is data that is not generated by one of the components of the company's internal information system, or that is not captured internally.

There is no major difference between internal and external data. The only difference is that the company has less control over the external data, when it is entered or generated by an external application. The data quality verification phase will be all the more important in the case of external data, which may have been corrupted during its entry or generation.

External data can be management data, such as sales feedback from a network of partners; or peripheral data, such as meteorology, stock market prices or competitor price surveys. In the context of marketing and advertising, the notion of external data generally refers to third-party data.