Historical data refers to past data collected and stored over time, representing how a business, system, or process has behaved previously. In business intelligence and analytics, historical data is essential because it provides context, trends, and baselines that current data alone cannot offer.

Examples of historical data include:

• Past sales transactions

• Website traffic over months or years

• Customer behavior before and after product changes

• Financial statements from previous periods

• Support ticket history

Historical data enables organizations to answer questions like:

• How are we performing compared to last quarter or last year?

• Is this trend normal or unusual?

• Did a previous change improve or hurt performance?

From a BI perspective, historical data is the foundation for trend analysis, forecasting, benchmarking, and cohort analysis. Dashboards often rely on historical comparisons such as month-over-month (MoM) or year-over-year (YoY) growth.

Technically, historical data is stored in data warehouses or data lakes with timestamps and versioning. Good historical datasets preserve changes over time rather than overwriting old values. Techniques like slowly changing dimensions (SCDs) are used to track how attributes evolve.

Challenges with historical data include storage cost, data consistency, and schema evolution. As systems change, historical data must remain interpretable. Poor handling can lead to broken comparisons or misleading trends.

Historical data is also critical for machine learning. Models trained without sufficient historical depth often perform poorly because they lack exposure to variability and seasonality.

In short, historical data provides the long-term memory of an organization. Without it, analytics becomes short-sighted and reactive rather than strategic.