There were a few interesting posts this past week as it relates to the Data Vault Methodology and emerging trends in Business Intelligence (BI). These are must reads for BI practitioners who wish to stay current on where the industry is heading as well as how and why things should be done in a certain [...]
There were a few interesting posts this past week as it relates to the Data Vault Methodology and emerging trends in Business Intelligence (BI). These are must reads for BI practitioners who wish to stay current on where the industry is heading as well as how and why things should be done in a certain way or method. Again, as Dan Linstedt, the inventor of the methodology is known to say, the data vault was created to solve specifically the Enterprise Data Warehouse (EDW) problem.
The first entry is the slide deck from the recent Advanced Architecture Conference in Denver: http://danlinstedt.com/datavaultcat/datavault-advanced-architecture-conference-slides/
There is a wealth of information contained herein that outlines at a high level the benefits and reasons why the data vault method is enabling pervasive BI. It goes into details about comparing 3rd normal form and the star schema form to the value the data vault brings to the table. It outlines the components of a data vault, including the hubs, links, satellites, PIT tables, and bridge tables.
It was nice to see the connection with the methodology to emerging tools. There are now more than a handful of tools that are undertaking the task of automating the ETL and loading processes of an EDW. The method of the data vault allows for a lot of the technical innovation. Dan presents the case well that businesses today want answers faster and cheaper today as well as having the system flexible enough to evolve with the business over time. Most EDW systems today fall short of being pervasive, and often that is a methodology problem that appears to have an answer.
In addition to these topics, Dan pushed forth the proposition of an “Operational Data Vault” and began to lay out how this connects to Business Intelligence. This section was very interesting and helped to turn on a few light bulbs and progressive thoughts. I love the idea of pushing the intelligence as far as possible while still keeping the style and benefits. I agree with Dan that this is still so new that there are not yet any vendor applications or tools that can help with this…yet.
The presentation does leave a little bit of gap with the more advanced concepts, such as change management and how to leverage a data vault using agile principles. It is also a bit light on how to drive a project plan as well as what to watch out for while attempting to build a data vault style EDW. All in all, he presents a very compelling case as to why the data vault should be strongly considered when delivering Business Intelligence.
The second entry is a public rebuttal to a challenge about the validity of the data vault methodology:
As Dan states, he felt the need to lay out the facts and correct misconceptions from the original author. After you get past the first few sections of disclaimers and background, the article does a great job of comparing and contrasting methodologies and technical styles for building the EDW.
I often see HDSA (persistent historical data storage area) implemented at client sites and they sometimes go by different names (ODS, staging, etc.). I have yet to see a better discussion of the merits of a data vault compared to the HDSA as he presents here. If you are in an environment that has an ODS or persistent history area, please take a moment to read this section, because the warnings and issues are real and one should not make decisions here lightly because of the long term ramifications.
Dan also goes into great detail to itemize the benefits and value proposition of why you take the steps to have a data vault at the core of the EDW.
I would say that the overall tone of the article still makes it a bit hard to read, but if you can overlook the defensive stance, you will find many gems that will help solidify and justify the purpose and value of the data vault compared to any other technique.