By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    football analytics
    The Role of Data Analytics in Football Performance
    9 Min Read
    data Analytics instagram stories
    Data Analytics Helps Marketers Make the Most of Instagram Stories
    15 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    What to Know Before Recruiting an Analyst to Handle Company Data
    6 Min Read
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: In Praise of Industry Models
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Warehousing > In Praise of Industry Models
Business IntelligenceData WarehousingModeling

In Praise of Industry Models

zamaes
Last updated: 2012/05/30 at 5:00 PM
zamaes
5 Min Read
SHARE

If you want to make an apple pie from scratch, you must first create the universe.—Carl Sagan

If you want to make an apple pie from scratch, you must first create the universe.—Carl Sagan

Making an enterprise data warehouse from scratch may not necessitate recreating the universe, but it’s also not easy as pie. It’s a major undertaking, which must be handled incrementally if a workable result is to be achieved. Along the way are a multitude of design decisions to be made, each of which will have ramifications downstream, such as ETL processing, business intelligence development and enterprise services.

More Read

ai low code frameworks

AI Can Help Accelerate Development with Low-Code Frameworks

Tackling Bias in AI Translation: A Data Perspective
How AI is Boosting the Customer Support Game
AI-Based Analytics Are Changing the Future of Credit Cards
Enterprises Are Leveraging the Benefits of AI-Driven ERPs

I have spent some years working with the set of Industry Models from IBM, and while I have avoided speaking directly about them on this blog, so as not to reveal privileged information, I would like to take this opportunity to point out some of the real benefits of incorporating them into an EDW initiative. The models are not without their frustrations and limitations. But having experienced the alternative – of having to develop models from scratch – I can attest to their value.

Here are some of the major benefits of having a template data model:

  1. Standards
    • Data Architecture – The industry models provide a cohesive infrastructure, with repeating patterns, following a strict, disciplined approach. The design is based on a classification model that presents business information according to “what it is” rather than “how it’s used”. This opens up information to be stored in many perspectives, rather than for a single line of business.
    • Lowers Risk of redundancy – Because of its structure and its evolution over numerous implementations, redundancy of objects and conflicts between “versions” of information is eliminated. Every piece of incoming information has a single placeholder within the data model. The EDW System of Record becomes the “Single Source of Truth”.
    • Definitions and Naming Standards – The models come with a full set of logical names and physical abbreviations, reducing the risk of the misidentification of terms and facilitating the adoption of a single set of enterprise names.
  2. Enterprise Perspective
    • Core Concepts – Template model incorporates all aspects of each industry through its structured hierarchies of core concepts.
    • Iterative – Facilitates incremental adoption while minimizing risk of being “painted into a corner” through limiting design decisions.
    • Multi-Industry Integration – There are some occasions where an enterprise may be involved in multiple lines of business, such as retail and banking, or retail and insurance. Because of the common design principles on which the models are based, they are well-positioned for such integration.
  3. Flexibility
    • History – Accommodates history at “attribute-level”, minimizing redundancy and providing an efficient structure for historical analysis.
    • Growth – Accommodates growth with minimal structural changes; particularly with regard to metrics and relationships between business entities.
    • Perspectives – Highly-normalized structures are well-positioned for creating different perspectives of dimensions and metrics for reporting.
  4. Process Acceleration
    • Data Design Patterns – While the models are not intended to be used “out-of-the-box” without customization, the template offers a complete picture of the enterprise and a significant head start on development.
    • ETL Patterns – Repeating patterns in Data Architecture translate to repeatable ETL patterns, with a modular approach to each set of recurring data structures (e.g., writing history of a classification value, handling single values in an attributive table, approach to normalized hierarchies).
    • Testing Patterns – Acceleration of effort extends to unit and system integration testing. With repeating data structures come the ability to craft SQL code that follows repeatable patterns, reducing the effort to prepare test scripts.

In the absence of a set of Industry Models, from IBM or another vendor, all of these points must be addressed afresh; or left unconsidered, with the risk that mistakes will be made. The structures will follow inconsistent patterns, the names will conform to no specific set of conventions, and the effort to create a flexible repository to service the whole business will end up a silo of information being used by a small corner of the organization. Until the next time the executive decides to try again to create an enterprise asset.

If you want to make an apple pie from scratch, follow the recipe.

If you have experiences with or without industry models, please feel free to share them…

zamaes May 30, 2012
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Shutterstock Licensed Photo - 1051059293 | Rawpixel.com
QR Codes Leverage the Benefits of Big Data in Education
Big Data
football analytics
The Role of Data Analytics in Football Performance
Analytics Big Data Exclusive
smart home data
7 Mind-Blowing Ways Smart Homes Use Data to Save Your Money
Big Data
ai low code frameworks
AI Can Help Accelerate Development with Low-Code Frameworks
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

ai low code frameworks
Artificial Intelligence

AI Can Help Accelerate Development with Low-Code Frameworks

12 Min Read
data perspective
Big Data

Tackling Bias in AI Translation: A Data Perspective

9 Min Read
How AI is Boosting the Customer Support Game
Artificial Intelligence

How AI is Boosting the Customer Support Game

6 Min Read
AI analytics
AnalyticsArtificial IntelligenceExclusive

AI-Based Analytics Are Changing the Future of Credit Cards

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?