Data is information recorded in a form that a computer system can store, process and transmit: an order amount, a GPS position, a click, a sensor reading, a text, an image.
Every digital action produces some: a payment, a search, a message, a visit to a web page. In industry, machines and sensors generate it continuously. A distinction is made between structured data, organized in tables (a customer database), and unstructured data, such as emails, documents or videos, which are harder to use.
Raw data is worth little. Its value comes from the chain that collects, cleans, stores and makes it usable for decisions or automation: dashboards, predictive models, AI training. This chain involves several jobs (Data Engineer, Data Analyst, Data Scientist) and a strict legal framework as soon as personal data is involved (GDPR).
Why it matters when hiring
"Data profile" means nothing until the stage of the chain is specified: building the flows (Data Engineer), analyzing and reporting (Data Analyst), modeling and predicting (Data Scientist). A common mistake is hiring a Data Scientist when the data is neither centralized nor reliable: the first useful hire is often a Data Engineer.