A brief overview of some terms relating to data and health

Data can also be described as measurements or values that, when processed, yield information. The term digital data, in turn, refers to those data that are stored or processed by digital means. The collection and analysis of data enable personalized services, predictive analytics and improved outcomes. As the volume of data generated continues to grow, its importance in shaping policies, enhancing business operations and fostering technological advancements becomes ever more critical.

Data aggregation is when you take a lot of small pieces of information and combine them into a bigger summary. It’s like collecting puzzle pieces and putting them together to see the bigger picture. For example: If you collect data about daily temperatures from different cities, you can aggregate it to find the average temperature for the whole country. Instead of looking at every single detail, you can focus on the overall trends or patterns. it alse saves time: It’s easier to analyze summarized data than thousands of individual points. In short, data aggregation turns lots of small pieces of data into something more useful and easy to understand.

Data altruism is defined as ‘individual people and companies can voluntarily make data available for the common good'. It is focusing on the safe reuse of public-sector data and establishing a level playing field in the data economy by promoting data sharing and reducing barriers to data accessibility.

Data collection is the process of gathering information so you can use it to learn something, make decisions, or solve problems. The goal of data collection is to gather accurate and relevant information that can be used for analysis, decision-making, or improvement. It can be done in many ways: surveys or interviews; observing and recording behaviors; sensors, like weather monitors or traffic cameras. In short, data collection is just about gathering the facts you need to answer a question or achieve a goal.

A data controller is a person, organization, or entity that determines the purpose and means of processing personal data. The data controller has the responsibility for ensuring that personal data is handled in compliance with data protection laws. It is the person who decides why and how personal data will be processed.

Data culture is about how people in an organization or community value and use data in their everyday work and decision-making. Building a data culture isn’t just about having the right tools or technology—it’s about encouraging people to trust and use data as a natural part of how they work. In healthcare, it might mean using patient data to improve treatments and outcomes.

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.

A data processor is an individual, organization, or entity that processes personal data on behalf of a data controller. The data processor operates under the instructions of the controller and does not determine the purposes or means of processing the data. Processing includes actions such as collecting, storing, organizing, transferring, or deleting data.

A data provider is an entity (a person, company, or system) that supplies or shares data with others. Think of it as someone handing out information to people who need it. The data can be shared for free or as part of a paid service, depending on the situation. The data can be raw (like numbers or text) or processed (like reports or graphs). Examples include weather services providing forecasts, businesses sharing market data, or apps offering user statistics. For example, a company like Spotify could be a data provider if it gives music streaming data to artists.

Data quality refers to how good or reliable the data is for its intended purpose. High-quality data is accurate, complete, consistent, and up-to-date, making it useful for making decisions or solving problems. If the data is of poor quality, it might lead to mistakes or wrong conclusions. For example, if a customer’s address is wrong in a shipping database, the package might go to the wrong place (low data quality).

A data set is simply a collection of related data, usually organized in a way that makes it easy to look at or analyze. A data set is a group of data points about a topic. It’s usually structured, meaning it’s arranged in a table or similar format. You can think of it like a spreadsheet where rows and columns hold information about something specific. Each row might represent an individual thing (like a person, product, or event), and each column represents a specific type of information about those things (like names, prices, or dates). A weather report showing daily temperatures, humidity, and rainfall for a month is another example of a data set.

Data produced by people should be available to the people. Good healthcare, scientific research for better health, development of medication, health products and medical technologies, good practices, are based on the usage of shared data and knowledge. Health insurance is historically based on the fact that people put money in a box, a cash register, and people can take money from that cash register when they are ill. We have generalised this to our current health insurance, which is based on solidarity, people pay contributions and taxes, which can then be used by everyone when and where necessary. Actually, the same principles apply for data solidarity, meaning data produced by people should be available to the people. Just as all citizens contribute to the healthcare system through taxes, so too should data be shared for the common good. Data solidarity foregrounds the public value when it benefits people and communities without the risk of invading citizens’ direct privacy.

Data sovereignty is about the rules and systems that ensure data is stored, controlled, stored safely and used securely, and how it can be made easy to share and move between systems, while respecting key principles of digital independence. Data sovereignty is closely connected to the idea of digital self-determination, which means individuals have the right and ability to exercise autonomy over their digital presence, data and online activities. It also includes the idea of groups or communities having control over shared data.

A data space is like a shared environment or ecosystem where different organizations or people can safely share and use data. It’s built on rules and technologies that make sure the data is secure, easy to access, and used responsibly. The goal is to share data efficiently while keeping it safe and respecting privacy. In healthcare, a data space might let hospitals, researchers, and companies share patient data securely to improve treatments, without violating privacy rules.

Data standardization is the process of organizing data into a consistent format so it’s easier to understand, use, and share. It ensures that everyone who uses the data is on the same page, even if the data comes from different places or systems. Standardization makes data more reliable, compatible, and easier to analyze. If one system records "New York" as "NYC" and another as "New York City," standardizing them ensures all records are consistent, like always using "New York City." This helps avoid confusion, improves accuracy, and makes data integration smoother.

Data storage refers to how information is saved and kept for future use. Data storage is about finding a safe place to keep information, whether on your device, in the cloud, or on external hardware like a USB drive, or information stored in structured systems (databases) used by businesses for managing large amounts of data. It ensures the data is accessible, secure, and retrievable when required.

Data subjects are the people that share their data. A data subject is a person whose personal information (data) is being collected, stored, or processed. A data subject is the individual the data is about. They have rights over their data, such as knowing how it’s used, correcting it if it’s wrong, or asking for it to be deleted (depending on the law/regulation, like GDPR). When you shop online, you are the data subject for your order history, payment details, and shipping information.

Data transfer is the process of moving data from one place to another. This could mean sending data between devices, systems, or locations. Think of it like delivering a package—it’s about getting information from point A to point B. It’s how data travels over networks, like when you send an email, upload a file, or access a website. A cross-border transfer is also possible. It is transferring data between countries, often subject to laws and regulations to protect privacy and security.

A data user is a person, organization, or system that accesses and works with data. A data user is anyone who interacts with data for a specific purpose. They use the data to analyze, make decisions, or perform tasks. They could be reading, editing, analyzing, or sharing the data. A student using online research data for a project is a data user. Data users have responsibilities, like handling data responsibly and respecting privacy laws or guidelines.

DCAT-AP stands for Data Catalog Vocabulary Application Profile. It is developed and maintained by the European Commission for an Interoperable Europe. It is a standardized approach for describing public sector data sets, making it possible for data from diverse sources to be easily located, accessed and reused by various applications and stakeholders. It provides a common basis for standardized description of metadata and dataset within Europe to improve interoperability and make it easier to exchange data across borders and domains.

De-identification is the process of removing or masking personal information from a dataset so that individuals can no longer be easily identified. It’s a way to protect privacy while still allowing the data to be useful for analysis or sharing. It’s like blurring someone’s face in a photo—you can see the picture, but you can’t tell who the person is. A hospital might de-identify patient data by removing names and medical record numbers before sharing it with researchers.