📌 Temporal Data Modeling Summary
Temporal data modelling is the process of designing databases or data systems to capture, track and manage changes to information over time. It ensures that historical states of data are preserved, making it possible to see how values or relationships have changed. This approach is essential for systems where it is important to know not just the current state but also the past states of data for auditing, reporting or compliance purposes.
🙋🏻♂️ Explain Temporal Data Modeling Simply
Imagine keeping a diary where you write what happened each day, so you can look back and see what you did in the past. Temporal data modelling helps computers do something similar by keeping records of how things change, instead of just showing the latest version.
📅 How Can it be used?
Temporal data modelling can be used to track employee position changes over time in a human resources management system.
🗺️ Real World Examples
A hospital uses temporal data modelling to keep track of patient medical records, including previous treatments, medication changes and doctor visits. This allows doctors to see a patient’s full medical history, not just their current status, which is crucial for effective care.
A retail company uses temporal data modelling in its pricing system to record when product prices change. This enables them to analyse how sales were affected by price adjustments at different points in time.
✅ FAQ
Why is it important to keep track of how data changes over time?
Knowing how data has changed over time is useful for many reasons. It helps organisations see trends, understand past decisions, and answer questions about what happened and when. For example, banks need to know a customers balance at a specific date, and healthcare providers may need to review a patients previous medical records. Tracking these changes can also help with audits and meeting legal requirements.
How does temporal data modelling help with auditing and compliance?
Temporal data modelling makes it possible to store and retrieve past versions of information. This is especially important for audits, where you might need to prove what data looked like at a certain point. It also helps organisations stick to rules about keeping records for a set period, which is a common requirement in many industries.
Can regular databases handle changes to data, or is something special needed?
While regular databases can store current information, they often overwrite old values when something changes. Temporal data modelling adds extra design to make sure those old values are kept as well, so you always have a full picture of how things have changed. This means you can look back at any moment in the past, not just see what is true right now.
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