A brief overview of some terms relating to data and health

Data ethics focuses on the moral obligations that all societal actors have (or should have) when collecting, generating, analysing and disseminating both structured and unstructured data, human-provided data as well as the leverage of existing databases, including decisions driven by automated/artificial intelligence (AI) in relation to data in general and personal data in particular. It relates to general principles on which our societies are built and is highly relevant to building trust and ensuring fairness. It is not only about protecting data privacy or security. It is also about protecting citizens, customers and users from data practices by both the public and the private sector that adversely impact people and society.

Data extraction in research involves collecting and retrieving relevant data from various sources for the purpose of analysis, interpretation and deriving conclusions. In healthcare, data extraction plays an increasingly important role in patient care and predictive medicine as well as in medical research.

Data governance refers to the overall management of the availability, usability, integrity and security of the data that is collected, used and reused. It involves the establishment of policies, procedures and standards to ensure that data are managed effectively throughout their lifecycle within organizations as well as within and between countries.

A data holder is an entity (like a person, organization, or system) that stores or manages data. A data holder keeps data safe and organized. It decides how to manage, share, or protect that data based on rules or laws. Examples include banks (holding financial data), schools (holding student records), or even your smartphone (holding your photos and contacts).

Data literacy refers to the ability to comprehend, interact with, analyze, and reason through data. It involves interpreting data in various forms—whether it’s charts, database fields, dashboards, or other formats. Additionally, it encompasses the skill of effectively working with data on a daily basis, using appropriate analytical methods to extract meaningful insights while approaching the information with critical thinking. This includes not only the ability to ask insightful questions and challenge the data but also the crucial ability to communicate findings and interpretations clearly and efficiently to others.

Data maturity refers to an organization’s ability to effectively manage, utilize, and derive value from its data. It measures how advanced an organization is in handling data as a strategic asset, including how it collects, stores, analyzes, governs, and uses data for decision-making.

Extracting patterns from large quantities of unstructured data is referred to as data mining or data analytics. Increasingly this is now done through methods such as artificial intelligence and machine learning. In healthcare, data extraction plays an increasingly important role in patient care and predictive medicine as well as in medical research. For example, the demand for reliable health information increased significantly during the COVID-19 pandemic. Many health systems could not, however, ensure the flow of necessary data and information between providers and public health agencies, making it difficult to detect patterns and interpret them to obtain actionable insights.

Data ownership means having control over a piece of data and the right to decide how it's used. If you "own" the data, you get to make choices about who can access it, how it can be shared, or whether it can be deleted. Data ownership gives you the power to make decisions about the data. It can involve legal rights, responsibilities, and sometimes accountability for how the data is used. We rather speak about rights and obligations for both data subjects and data controllers, rather than using the word data ownership.

A data permit is an official approval that allows a person or organization to use specific electronic health data for certain purposes. This permit is issued by a health data access body and outlines what data can be accessed, who can use it, and under what conditions.

Data processing is what happens when raw data is taken and turned into something useful or meaningful. It starts with raw data, like numbers, text, or images. Tools or systems organize, analyze, or change the data to make it easier to understand or use. The result is something useful, like a report, a graph, or a decision. It’s like cooking: you take raw ingredients (data), follow a recipe (a set of steps), and end up with a delicious dish (useful information). For example, when you deposit a check using a banking app, the app processes the image of the check to extract information like the amount and your account number.