In my February column, we discussed the need for and benefits of adhering to standards in the data integration framework (DIF). DIF standards are really about best practices for deploying your entire project. In this column, we discuss several essential data standards.
Data standards ensure the consistency, accuracy, integrity and validity that are critical to your success. Data is not usually clean and consistent, especially when gathered from many source systems. Reference data (referred to as "dimensions" in data warehousing or "master data" in enterprise resource planning systems) such as customers, suppliers, organizations and products generally varies between divisions and also changes over time. Standards on how to handle integrating these dimensions and ensuring consistency are crucial to creating the "single version of the truth." Often, it is not mistakes in the data entered in the transaction systems that cause poor data quality (although errors do occur), but rather a gap in understanding how to merge data from different systems and maintain dimensional integrity.
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