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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/.

If you've started a Master Data project, or you are working with Metadata at the business levels, then you probably are familiar with the need for ontological classification of terms.  If you aren't familiar with Ontologies (at an entry level), then I would say that you will have a tough time putting together successful MDM or Master Metadata components for your EDW/BI solution.

Ontologies are forms of classifications for terminology. In other words, the idea is as follows:

  • Vehicle
    • Car
    • Bus
    • Train
    • Motorcycle
    • Auto
    • Bicycle

This is just one way (one ontology) of terms that are used to describe vehicle.  There are many other views of Vehicle (axis) on which to pivot this data set, depending on the classification strategy you want to use (enter: Taxonomy). 

Business users often fight over what a term means, why? Because they apply the term in a different context (along a different ontology or categorization).  The problem is: they are both right at the same time, even with different definitions of the Metadata.

What does this mean to my Master Data Projects?

Well if you're focused on creating a successful BI deployment, and creating value in the business, then you MUST allow these multiple ontologies to exist.  However, good governance practices will state that you SHOULD manage them through a taxonomy (a classification of groups of ontologies).  I guess you can think of it as a CUBE for metadata with the elements in the cells, and along the axis points. (Someone should visualize metadata this way, it would be really interesting). 

In other words, you MUST be capable of creating "master" metadata classifications IF you are to be successful at creating, understanding, and deploying Master Data Management solutions across your company or organization.

Unfortunately there are hundreds of "spread out" tool sets that manage "ontologies" and sets of meta-data, but don't come close to hooking it in to a BI/EDW or even Master Data tool.

How does this impact my project?

If you have embarked on MDM project, or something akin to that, and you have NOT looked at your metadata or ontological layout, you are already behind the 8-ball.  You probably are experiencing cost overrun, missed requirements, non-met time frames for delivery.  You probably have a disgruntled staff, and disallusioned business users.  If on the other hand you have a successful project, I'd like to hear how you did it without Master Metadata.

It's high time to put together the Metadata needed to apply, understand, and decipher Master Data at the business level.  Oh - yea, and one more shocker (probably not news to you)... but if you're ever going to get value out of Unstructured data, or you plan on mining unstructured data, then you will ABSOLUTELY need the ontological breakdown of the different languages that your documents are written in.  It is a MUST to tying any of that information into a structural world where the context can SHIFT at any given time.

I hope this is helpful, I'd like to hear your experiences on the subject.


Dan Linstedt


By the way, we've just launched a library of technical documents (free) to which we will be expanding these subjects at http://www.DataVaultInstitute.com


Posted July 24, 2009 4:27 AM
Permalink | 1 Comment |

1 Comment

This is a really good point. I watched a podcast recently that I think drives this home really well - "What does Data Quality have to do with IT? Moving from Techno-Speak to Business Value" - in which Navin Sharma of Pitney Bowes Business Insights frames this another way. Bottom line - Data Quality is rooted on whether or not data is "fit for use" - and, as it will be used by various folks different ways,it needs to be handled as such. Here's a link to the podcast...http://searchdatamanagement.bitpipe.com/detail/RES/1240854023_72.html
Thanks for the post!

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