Data quality initiatives struggle to become an integral part of everyday business processes. Encyclopaedia helps overcome this barrier by capturing your data quality requirements simply. Transforming outputs from its data quality engines into easy-to-read "traffic light" style indicators, Encyclopaedia allows users to easily understand the data quality status of business terms, reports, and tables.
The result is data quality embedded into a company's everyday routines:
- the company will be making decisions based on correct data (or at least, it knows when it's using unreliable data for its decision making).
- the company will become more cost-effective in the gathering and communication of data quality information to its consumers and in the handling of data quality issues.
- and finally, good data quality governance creates an environment for the continual improvement of important business information's quality.
Fixing the problem
A strong DQ knowledge-management system must provide enough information to satisfy all the queries and needs of all the different types of users who are concerned about data-quality, namely:
- business users who need information about the data-quality of reports and data-objects used for their decision making,
- and all the users responsible for the data-quality within the company (DQ manager, data-stewards, developers and others).
That's a large and diverse set of users and when we say "enough information” we mean that the system should contain:
- documentation about DQ checks,
- the operational status of DQ checks (the results of the DQ checks that have been run),
- and seamless access to the related data-objects' and reports' documentation.
This is how the DQ Catalogue will look to your business users.
This is how DQ checks for particular objects looks in the DQ status window.
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