One of the most effective tools for managing data quality and data governance, as well as giving business users confidence in the data warehouse results, is the audit dimension. We often attach an audit dimension to every fact table so that business users can choose to illuminate the provenance and confidence in their queries and reports. Simply put, the audit dimension elevates metadata to the status of ordinary data and makes this metadata available at the top level of any BI tool user interface.
Ralph Kimball introduced the data warehouse/business intelligence industry to dimensional modeling in 1996 with his seminal book, The Data Warehouse Toolkit. Since then, the Kimball Group has extended the portfolio of best practices.Drawn from The Data Warehouse Toolkit, Third Edition, the “official” Kimball dimensional modeling techniques are described on the following links and attached .pdf:
Customer interactions create a wealth of timely data that marketing departments are eager to exploit. The customer status fact table provides a central switchboard for using this fast-moving data.
What does it take to develop a robust dimensional model? Here's how to get from requirements-gathering to final approval in a process that will ferret out the good, bad and ugly realities of your source data and help you avoid surprises, delays and cost overruns.
How do you deal with changing dimensions? Hybrid approaches fill gaps left by the three fundamental techniques
Follow the rules to ensure granular data, flexibility and a future-proofed information resource. Break the rules and youll confuse users and run into data warehousing brick walls.