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Dan Linstedt

Bill Inmon has given me this wonderful opportunity to blog on his behalf. I like to cover everything from DW2.0 to integration to data modeling, including ETL/ELT, SOA, Master Data Management, Unstructured Data, DW and BI. Currently I am working on ways to create dynamic data warehouses, push-button architectures, and automated generation of common data models. You can find me at Denver University where I participate on an academic advisory board for Masters Students in I.T. I can't wait to hear from you in the comments of my blog entries. Thank-you, and all the best; Dan Linstedt http://www.COBICC.com, danL@danLinstedt.com

About the author >

Cofounder of Genesee Academy, RapidACE, and BetterDataModel.com, Daniel Linstedt is an internationally known expert in data warehousing, business intelligence, analytics, very large data warehousing (VLDW), OLTP and performance and tuning. He has been the lead technical architect on enterprise-wide data warehouse projects and refinements for many Fortune 500 companies. Linstedt is an instructor of The Data Warehousing Institute and a featured speaker at industry events. He is a Certified DW2.0 Architect. He has worked with companies including: IBM, Informatica, Ipedo, X-Aware, Netezza, Microsoft, Oracle, Silver Creek Systems, and Teradata.  He is trained in SEI / CMMi Level 5, and is the inventor of The Matrix Methodology, and the Data Vault Data modeling architecture. He has built expert training courses, and trained hundreds of industry professionals, and is the voice of Bill Inmons' Blog on http://www.b-eye-network.com/blogs/linstedt/.

Want to break down the barriers? Tired of "taking sides" when you don't have to? In this blog I explore a modeling technique called the Data Vault (no it doesn't have to do with security or locking your data away). This technique sits squarely between Inmon 3rd normal form warehouse and Kimball Star Schema design as a warehouse.

This modeling technique is comprised of the best-of-breed from both designs and is built to overcome limitations of the adaptations made to each data modeling architecture; specifically with regards to data warehousing.

What is a Data Vault?
Definition: The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star schema. The design is flexible, scalable, and consistent. It is a data model that is architected specifically to meet the needs of today’s enterprise data warehouses.

What is it's real name?
Common Foundational Warehouse Architecture

What does it do for me?
* Saves time and money in build out of your data warehouse or enterprise integration initiative.
* Provides a consistent, repeatable architecture that can scale to your enterprise needs.
* Defines easy to use standards for all to follow
* Reduces complexity of the integration effort
* Saves storage space
* Increases visibility into both: well-oiled and broken business processes
* Demonstrates a strong basis for Data Visualization and Data Mining

What are some of the benefits?
* Rapid prototyping and build out of data marts and reporting solutions.
* Genuine audit trail pictures produced of the enterprise vision of data (even if none exist on the source systems)
* Data is modeled by type, and rate of change - allowing both batch and real-time to be "added" to the warehouse at the same time.
* Contains a high degree of data attribution
* Scales to petabyte levels if necessary
* Can handle near real time data arrival at a fraction of a second.

Yes, but are there any customers using it?
Sure - just check the web site for more information.

What are some of the success stories?
* Large manufacturing company saved millions when finding and fixing a billing error that had been occurring for the past 15 years.
* Large financial company integrated M&A 3 companies in 3 months flat
* Large banking company adds "branches" to their warehouse quickly at a low cost.
* Government operation decreases "time to build data marts" to 1 hour (from requirements to inception).

What makes the Data Vault so successful?
It's ability to be modeled AT THE BUSINESS LEVEL. The Data Vault is designed to mimic the business keys - the most important data element in business. Once the keys are established it models the relationships across those keys; which flush out both process relationships and undocumented business operational relationships. Finally the attribute data or descriptive data is added to the mix and split by Type of Data and Rate Of Change.

From a business perspective, the logical model is tied tightly with the physical model and architecture - therefore it is easy to change the model as the business changes. It is also based on a statement of fact. The business keys are in use and were in use at a specific point in time. This model is built to capture those ideas.

Come hear more about this technique at TDWI, Sunday October 30th in Orlando. Or contact me directly: daniel.Linstedt@myersHolum.com

Thanks,
Dan L


Posted September 27, 2005 5:26 AM
Permalink | 3 Comments |

3 Comments

Is there a matrix for when do you which data warehouse modeling technique? I know there is a lot around Inmon vs Kimball but nothing which includes data vault. What business questions need to be answered to determine the technique?

Are there healthcare and/or insurance companies that have successfully implemented an enterprise or patient care data vault?

What are some of the Best Practices around a hybrid hub-and-spoke approach (i.e. data vault with supporting data marts)?

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