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Data Quality is Essentially Simple!

At first glance the subject and practice of Data Quality can seem hugely complex and about as difficult to unravel as the Gordian Knot. The main reason for this is that most of the current efforts in this area go into trying to clean up the data quagmires that are created daily because the Five Rules for Data Quality have been ignored.

Fundamental Rules of Data Quality

Data Quality Rule 1
Data ONLY exists to support the Business Functions of the enterprise. We might need to add Rule 1a, which is that a Business Function is NOT a Department! It is a fundamental business activity. Oh! and Rule 1b: A Function is not a Process!!

Data Quality Rule 2:
There are no exceptions to Rule 1. Data that is not used by any Function should be removed. If the data might be of value to someone else then the enterprise should put it up for sale, otherwise simply delete it.

Data Quality Rule 3:
Data quality is driven by Function Quality. Get the Function right and the data will be right. Rule 3a: Whoever is responsible for the quality of the Function is responsible for the quality of the data it creates.

Data Quality Rule 4:
If the enterprise does not have a Logical Data Model based on Business Function it has not got Data Quality. Rule 4a. A database schema showing tables and foreign keys is not a data model – it is a database schema!

Data Quality Rule 5:
Codes and Keys are not UIDs! This is a fundamental error made by a large majority of data analysts and data designers. Data Paradox: The use of unique keys as UIDs is the one means by which you can guarantee duplication in a database! See my posts on QUACKs and UIDs

So, instead of expending vast resources on trying to solve the insoluble, let enterprises take Alexander’s approach, cut through the cr*p knot and implement Data Quality Assurance by applying these Five Fundamental Rules.

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