Data Warehousing for Better Decision Systems
The data warehousing industry certainly has matured since Ralph Kimball published the first edition of The Data Warehouse Toolkit in 1996. Although the large corporate early adopters paved the way since then, data warehousing has been embraced by organizations of all sizes. Surprisingly, some have not adopted at all. They can lose ground to their competitors and ultimately lead to slow or bad or no decisions at all, and without good decisions the organization can be steered right off a cliff!
Vendors bring to market exciting new tools every year, data is everywhere with IoT, Machine Logs, Social Media rivers, Click Stats, and now tons of tons of data building up from LOB and OLTP systems, it’s amazing how Dimensional Modeling has withstood the test of time and is my Go-To solution for solving performance problems and much much more..
This blog series I will collapse time for you and point to key lessons in Kimball’s Dimensional Modeling Toolkit, and share from others I’ve read along my journey in data & IT. Having learned valuable lessons from projects that failed, and applying those lessons learned to convert to successful projects, and where to take things to the next level that I’m still navigating, I hope to share all these with you. Having been a DBA for almost 20 years now, I’ve seen a thing or two, so I know a thing or two, and I hope I can slingshot your career (or project), not just fill my blogs with fluff.
Chapter 1 – Dimensional Modeling Primer (key takeaways)
First and foremost, the data warehouse must consider the needs of the business. Operational systems are where we put the data in, but the data warehouse is where we get the data out.
Know the Goals of a Data Warehouse – just go around and listen to management and these themes occur:
“We have mountains of data in this company, but we can’t access it.”
“We need to slice and dice the data every which way.”
“You’ve got to make it easy for business people to get at the data directly.”
“Just show me what is important.”
“It drives me crazy to have two people present the same business metrics at a meeting, but with different numbers.”
“We want people to use information to support more fact-based decision making.”
Now these themes turned to Goals to Drive the Initiative:
The data warehouse must make an organization’s information easily accessible. Simplicity is key, the data must be easily understandable and make sense to the user at first glance, make it legible.. Source systems are complex, often warranting joins to 20 tables to answer one question, and duplicating complex calculations unnecessarily.
The data warehouse must present the organization’s information consistently. Data must be carefully assembled from a variety of sources, cleansed, QA’d, discrepancies solved, a single version of the truth as the end-result.
The data warehouse must be adaptive and resilient to change. Cannot avoid change, user needs, business needs, technology all change and evolve, the data warehouse must be built as a solid foundation, hence a perfect reason to follow industry best practices shared with us from those who’ve seen more than we ever will.
The data warehouse must be a secure bastion that protects our information. Security is walking a fine line between getting the data in front of the decision makers, and protected from those that intend harm. Microsoft’s philosophy with BI is data-democratization, make corporate data easily accessible to everyone. A great concept but there is confidential data you don’t want in the hands of the wrong people.
The data warehouse must server as the foundation for improved decision making. The decisions made after evaluating warehouse data can make an incredible impact.
The business community must accept the data warehouse if it is to be deemed successful. It doesn’t matter if you’ve followed the industry best practices, and built with the best tools, it is all gone to waste if you can’t get anyone to use it, or trust it.
- Kimball Group – The Data Warehouse Toolkit 2nd Edition
