A brief overview of some terms relating to data and health

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.