BI HAPPY
Business Intelligence Happiness
Skip to content
  • Home
  • Introduction
  • The Authors
← How I got flash to run on my iPad and iPhone
Data Visualization Lesson from 6 Years Old →

The dilemma of the expanding and shrinking universe

Posted on March 24, 2011 by Ron Keler

One of the most fundamental questions you will need to answer as you begin designing your meta data strategy can be borrowed from astrophysics. In much the same way that our cosmos adhere to great expending forces that stem from the big bang and keep stretching and expanding it, it also adheres to gravity and forces that ultimately will cause it to shrink back again. Just like your meta data layer. Your users are putting requirements that call for combining information across any conceivable domain, related or not, and on the other hand require concise, simple and intuitive reporting capabilities. How do you reconcile these conflicting trends?

When using BusinessObjects universes as your semantic or metadata layer, you have a variety of tools to help you cope with this fundamental difficulty, contexts, aliasing, derived universes and other techniques that can be employed to manage data complexities. However, these techniques require know-how and decrease your universes maintainability.

So, which way should you go in your universe design: reach for the mega universe that engulfs everything your users want to know about in a single universe, or carve out the information into smaller mini universes?

Consider the first the picture below. It represents a “mega universe” that spans many different subject areas, contains 15 contexts and over 1000 objects scattered in deeply nested subclasses.

Is this a universe you can put in front of users? Will there ever be more than selected few analysts with very good understanding of the underlying database who will be able to use this? If the principal guiding you toward a mega universe was trying to encapsulate database complexity from the users and handle it in the metadata layer, you might want to reconsider it.

On the other hand, the simple and small universe depicted in the following image represents challenges as well. While it will be much easier and simpler to build, test and maintain, it will surely leave your users wanting to create reports that combine the information in this universe with information in others like it. So now, you have effectively “passed the buck” to your users in terms of the burden and complexity for combing multiple subject areas. This also may not go down well.

The fact of the matter is that there is really no easy solution to this problem of the mega vs mini universes. The decisions around how much information to include in a single universe must be very pragmatic and tailored to address the particulars of the specific situation and requirement you are trying to address.

You must consider the comfort level of your technical resources with a highly complex universe, and your ability to truly test it, with all the possible combinations of data it entails at a satisfactory level, and weigh that in lieu of your users’ community requirements, and your users’ sophistication and technical savvy levels. You will likely find that in most cases, resisting the initial urge to expand, and focusing on relatively small universes that address one or very few subject areas, and do so extremely well, will become a lot more manageable and effective than a single universe designed to handle your entire data landscape.

 

This entry was posted in BI At Large, Universe Design and tagged mega universe, Metadata, mini universe, semantic layer, Universe. Bookmark the permalink.
← How I got flash to run on my iPad and iPhone
Data Visualization Lesson from 6 Years Old →
  • Archives

    • October 2021
    • June 2020
    • June 2017
    • March 2017
    • September 2016
    • June 2016
    • February 2016
    • November 2015
    • July 2015
    • May 2015
    • March 2015
    • January 2015
    • September 2014
    • August 2014
    • July 2014
    • June 2014
    • March 2014
    • February 2014
    • January 2014
    • December 2013
    • October 2013
    • August 2013
    • July 2013
    • June 2013
    • April 2013
    • March 2013
    • February 2013
    • December 2012
    • November 2012
    • October 2012
    • September 2012
    • August 2012
    • July 2012
    • June 2012
    • May 2012
    • April 2012
    • March 2012
    • February 2012
    • January 2012
    • December 2011
    • November 2011
    • October 2011
    • September 2011
    • August 2011
    • July 2011
    • June 2011
    • May 2011
    • April 2011
    • March 2011
    • February 2011
    • January 2011
  • Meta

    • Log in
BI HAPPY
Proudly powered by WordPress.