Database Management

Data collection is how information gets INTO databases: web forms, tags, cookies, transaction tracking, and location-based data (static vs non-static, via GPS). Real-life contexts: buying airtime, toll gates, loyalty…

A data warehouse holds structured, historical data from many sources for analysis (BI). It uses ETL (Extract, Transform, Load) and is often denormalised for fast reporting, unlike a normalised operational database…

Data mining finds trends and patterns in large data sets to support decisions (e.g., shoppers who buy nappies also buy wipes). Always weigh ethics, consent, privacy and security.

Maintaining integrity (accurate + consistent), security (against crashes/hacks), maintenance, data independence (data separate from applications), and reducing redundancy.

Every information system begins with collecting data — and the method chosen shapes its accuracy, cost and usefulness.

A data warehouse is a central store of an organisation's historical data, built for analysis rather than day-to-day transactions.

Data mining searches large data sets for patterns people would never spot manually — powering recommendations, fraud alerts and forecasts.

Data is an asset with a life cycle. Managing it means protecting quality, availability and confidentiality from capture to deletion.