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Claudia Imhoff

Welcome to my blog.

This is another means for me to communicate, educate and participate within the Business Intelligence industry. It is a perfect forum for airing opinions, thoughts, vendor and client updates, problems and questions. To maximize the blog's value, it must be a participative venue. This means I will look forward to hearing from you often, since your input is vital to the blog's success. All I ask is that you treat me, the blog, and everyone who uses it with respect.

So...check it out every week to see what is new and exciting in our ever changing BI world.

About the author >

A thought leader, visionary, and practitioner, Claudia Imhoff, Ph.D., is an internationally recognized expert on analytics, business intelligence, and the architectures to support these initiatives. Dr. Imhoff has co-authored five books on these subjects and writes articles (totaling more than 150) for technical and business magazines.

She is also the Founder of the Boulder BI Brain Trust, a consortium of independent analysts and consultants (www.BBBT.us). You can follow them on Twitter at #BBBT

Editor's Note:
More articles and resources are available in Claudia's BeyeNETWORK Expert Channel. Be sure to visit today!

Recently in IT Architectures Category

Data integration has come a long way since the early days of data warehousing. We can now identify three major data integration initiatives in our IT environments. And – guess what? They require a clear and concise architecture. Read on to understand the three initiatives making up an enterprise data integration architecture.

Data seems to fall into three categories: master data, operational transaction data, and data used for decision making. Each of these categories requires specialized technologies and architectural components. I would like to briefly go over these in this blog.

1, Master Data – Master or reference data has been in the news a lot lately. This data consists of information about your customers, products, locations, and other major subject areas of interest to the enterprise. Today we have master data management (MDM) technologies that assure this form of data – both the current version as well as all of its history – is the best that it can be. The integrated and consolidated reference data is stored in a master data hub store.

2. Operational Transaction Data – This data consists of all the information about the activities occurring throughout the enterprise – basically it is what tracks the enterprise as it conducts its business. Purchases, call detail records, campaign activities, supplier orders, call center contacts, claims, and so on, are all examples of operational transaction data. This data must be managed much the same way as the master data and then stored in an operational data store (ODS).

3. Decision Support Data – This data consists of the historical snapshots of data used in strategic and tactical analyses. Trends, patterns, mining, multi-dimensional analytics -- all depend on this vast store of decision-making data. The snapshots are loaded into a data warehouse for ultimate delivery into the various BI applications and data marts.

You can see why an architecture is needed. All three forms of data require similar processes – the data must be collected, cleaned up, integrated, and populated into its appropriate store. All this data must also be accessible to the other environments and, in many cases, back to the operational sources themselves. The architecture becomes your road map, demonstrating data flows into and out of each data integration capability.

In addition, the three forms of data integration share many of the same technologies – EII, ETL, hardware, software, and even application software may be reused for each initiatives. Whether you create a physically distinct set of components for each initiative or create some form of mixed workload situation (combining two or more of these initiatives into the same component) is up to you. Study what each vendor has to offer, determine how the technology will fit into your architecture, ultimately supporting your overall data integration environment.

Yours in BI success,


Posted August 30, 2006 3:13 PM
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