Data Quality

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Data quality measures how well business data aligns with standards. High-quality, reliable data enhances efficiency, supports decision-making, and drives profitability.

Importance of Data Quality

Poor data quality leads to wasted time, conflicting information, poor decisions, and a significant drop in efficiency.

Many organizations enforce strict data controls at the point of entry, but this is often insufficient. When data is loaded into a data warehouse or transferred between applications, additional validation and transformation rules are required to maintain data integrity.

Data Quality Strategy

The primary goal of any data integration solution is to collect data from multiple sources, validate it, and transform it into a standardized format.

Three Key Steps in Data Quality Management

Profiling – Ensures the source data file or table meets required formats, such as file encoding, field formats, and order.
Cleansing – Even after meeting profiling standards, data often needs cleaning, such as removing empty values or irrelevant information.
Auditing – A critical part of data integration, providing IT departments with a history of data transformations, profiling, and cleansing. This is essential for troubleshooting and failure investigations.