A brief overview of some terms relating to data and health

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.