CalDriCon

CalDriCon is an acronym, often used in the context of database management and data warehousing, referring to a specific set of practices related to data quality and consistency. It stands for Calculated, Drived, and Consistent. The term emphasizes the importance of ensuring that calculated and derived data within a database or data warehouse environment is consistent and accurate.

• Calculated: This refers to data values that are obtained through calculations performed on other existing data. These calculations might involve simple arithmetic operations, complex statistical analyses, or the application of specific business rules. The key aspect is that the value is not directly stored but rather generated on demand or as part of a data processing pipeline.

• Derived: Similar to calculated data, derived data represents information extracted or transformed from raw or existing data sources. Derivation often involves aggregation, summarization, or transformation of data to create higher-level insights or more user-friendly representations. The transformation processes are crucial for creating valuable information from operational data.

• Consistent: This element is the most critical aspect of CalDriCon. It emphasizes that all calculated and derived data must be consistent across the entire database or data warehouse. Consistency implies that the same calculation or derivation performed on the same underlying data should always yield the same result, regardless of when or where it is performed. This requires careful design and implementation of data processing logic, as well as robust data quality controls.

Ensuring CalDriCon is achieved often involves implementing rigorous data governance policies, standardizing calculation and derivation processes, and regularly monitoring data quality. Data profiling and validation rules play a significant role in identifying and addressing inconsistencies in calculated and derived data. Failure to maintain CalDriCon can lead to inaccurate reporting, flawed business decisions, and erosion of trust in the data.