Historical Data

Historical data pertains to the information that provides records about past events and circumstances. By observing trends, patterns, and insights from the past, we can anticipate future outcomes.


Historical data refers to the past information that is data collected over a specific period, which provides insights into the behavior and patterns of a system or process in the past.


Historical data can be obtained from various sources, such as:

  • Databases, including SQL & NoSQL
  • Server logs
  • Data archives
  • Historical sensor readings
  • Transaction histories (e.g., banking, retail, sales logs)
  • Customer behavior data
  • Weather patterns and other environmental data

Aggregation and Preparation:

Historical data are usually large and need to be handled carefully. This involves:

  • Organizing the data: to ensure that it can be sorted and filtered in meaningful ways.
  • Handling missing or incomplete data: replacing, modifying, or deleting the subset of data as appropriate.
  • Data cleaning: to ensure the data is reliable and accurate, removing duplicates or inaccurate entries.

Use of Historical Data:

Historical data is fundamental primarily for giving context to real-time data, evaluating long term trends, forecasting future tendencies, training machine learning models, and more.

For example:

  • Strategic decision-making processes typically utilize historical data to furnish insights on market trends to attain competitive advantage.
  • Finance and investment firms critically depend on historical data for market analysis, financial modelling, prediction, risk management, and strategy development.
  • In machine learning and AI, historical data is used as a training dataset for creating predictive models.


Historical data is exceedingly valuable for understanding trends and behaviors over time. But the derived insights are only as good as the quality of data. Consequently, ensuring clean and accurate historical data is paramount to get beneficial insights. Proper management, storage, and handling of historical data significantly contribute to various industries, especially for digital twin technology where it serves as a foundational basis for model development, troubleshooting, and predictive maintenance.