Data are recorded facts, figures, observations or measurements about something—such as a person, event, place or process. They may be numbers, words, images, sounds or physical measurements. Data become useful when they are put in context, checked and interpreted to answer a question.
What is data?
The Office for National Statistics (ONS) defines data as “facts and figures that are collected by individuals and organisations.” Statistics Canada describes data as “facts or figures about an object or phenomenon.” In practice, data can be a temperature reading, a survey response, a photograph, a sound recording or a transaction entry.
A fact does not have to be a number to count as data. What makes it data is that it has been recorded in a form that can be examined or used. The meaning of a recorded value depends on context: a number such as 18, for example, is difficult to interpret without knowing what it measures, its units and when it was recorded.
What are the main types of data?
Data can be classified in several ways. The classifications below describe different properties, so one dataset can fit more than one category at a time.
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Quantitative and qualitative data
Quantitative data are numerical observations that can be measured or counted, such as age, weight or distance. Qualitative data are descriptive observations or responses that cannot be captured by a number alone, such as a person’s explanation of why they do unpaid work. A survey may collect both: a respondent’s age as a number and their explanation as text.
Structured and unstructured data
Structured data are arranged according to predefined fields or items. A questionnaire spreadsheet with a separate column for each question is one example. Unstructured data include material such as text, images and sound that does not follow the same predefined tabular arrangement.
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These labels describe organization, not whether information is numerical or descriptive. A text answer in a fixed questionnaire field is qualitative but structured; a collection of free-form audio recordings is unstructured.
Microdata and aggregate data
Microdata describe individual units or records, such as individual responses in a survey. Aggregate data combine records into summaries, such as a total or average for a group. Aggregation can make patterns easier to see, but it removes some record-level detail.
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What is the difference between data and information?
Data are the recorded inputs; information is data organized or interpreted so that it conveys meaning. Statistics Canada describes statistical information as data that have been recorded, classified, organized, related or interpreted. A spreadsheet of observations is not automatically an insight: you need to know what its fields mean, whether its entries are usable, and how the observations relate to the question being asked.
For example, a list of temperature readings becomes more informative when the location, units and dates are clear and the readings are organized to show a pattern over time. Analysis may then support a conclusion—but only if the data and method are suitable for that conclusion.
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How are data collected and used?
Data work is a connected process rather than a one-time act of gathering numbers. Statistics Canada describes a journey that moves from defining a question and finding and gathering data to exploring, cleaning and describing it, analysing and modelling it, and communicating the result. Stewardship, metadata, standards and quality support each stage.
- Define the question. Decide what you need to understand and what evidence could answer it.
- Find and gather data. Identify an appropriate source and collect or obtain records relevant to the question.
- Explore, clean and describe. Inspect the records, address errors or inconsistencies, and clarify what the fields and values represent.
- Analyse and model. Use suitable methods to look for patterns or relationships, taking account of the dataset’s limits.
- Communicate the result. Explain what the analysis shows, the context needed to interpret it and any limitations.
Metadata—information about a dataset, such as what its fields mean—help people interpret and use data correctly. Standards can make definitions and formats more consistent. If a quality problem appears during analysis, the work may need to return to an earlier stage for correction rather than proceeding as if the result were sound.
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How can you tell whether data are reliable and useful?
The ONS describes quality as the “fitness for purpose” of a statistical product. That means there is no single quality check that makes every dataset suitable for every use. The ONS identifies several dimensions to consider:
- Relevance: Does the dataset measure the concept you care about?
- Accuracy and reliability: Are the observations dependable enough for the intended use?
- Timeliness and punctuality: Are the data current enough, and were they produced on a suitable schedule?
- Accessibility and clarity: Can you access the data and understand their definitions, metadata and presentation?
- Coherence and comparability: Can you sensibly compare the results across sources, places or time?
Quality also depends on the full lifecycle. The UK Government Data Quality Framework covers design and collection through dissemination and archival or destruction, with checks throughout. A dataset may need correction at an earlier stage if a later check exposes an issue.
What privacy responsibilities come with data?
When records identify people or can be linked to them, collection and use carry responsibilities beyond analytical quality. Statistics Canada notes that custodians must protect the privacy, confidentiality and security of identifiable data. Responsible handling therefore includes considering who can access identifiable records and how those records are protected throughout their use and storage.
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