We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    Flat editorial illustration: The article connects AI and marketing analytics with managerial judgment grounded in consumer behavi
    How an Online MBA in Marketing Connects AI and Data Analytics
    11 Min Read
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Data Modeling with Generalizations – The Tool Issue
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > Data Modeling with Generalizations – The Tool Issue
Uncategorized

Data Modeling with Generalizations – The Tool Issue

KarenLopez
KarenLopez
5 Min Read
Data Modeling with Generalizations – The Tool Issue
Illustrative image generated with OpenAI gpt-image-1.
SHARE

A bunch of factors have converged lately on the topic of generalized versus specific data modeling approaches. I’m working through the topic with two clients and yesterday I attended a webinar by Len Silverston and Paul Agnew about Universal Patterns for Data Modeling. Then Paul posted to dm-discuss about performance issues with generalizations. I posted a couple of responses:

As I talk about in one of my presentations on Managing Codes and Reference Data* Mistakes, the biggest hurdle to working with generalized structures is that our tools (data modeling, database, enterprise architecture, etc.) have not caught up with this more modern method of modeling. They are all designed to manage requirements that are specifically modeled. Once we move a concept from an entity-attribute to an instance of an entity, we have no place to create specifications about that instance.

So often what typically happens is that this is left to developers to figure out. And their tools aren’t any better at handling these generalizations. What used to be drag-and-drop query creation is now hand coding. DBAs can’t tune the structures as easily because they don’t have any insight as to what the data is going to be until real world test data is created or real world data is populated in the tables.

As data architects we can do up some sample/worked data examples in a spreadsheet, but there is no mechanism to manage those worked examples in our data models or to link those specifications together. Yes, some tools allow for enumerations to be managed, but these features don’t support the real world complexity need to show how this sample data is related to other data.

So we have two things that make it more difficult for DBAs and developers to work with generalized structures: Tools that don’t support it well (if at all) and data architects who fail to architect the data that has been generalized out of tables and columns and into row instances. On my projects, architects are required to prepare and manage (read that as “architect”) data instances as well as structures. On projects where this doesn’t happen, the generalized structures are often implemented incorrectly.

None of these problems are insurmountable. They are just challenges that we need to rise above. 

* in my original post to dm-discuss, I referenced a different presentation, but it is the Managing Reference Data and Codes presentation where I covered this content.

Technorati Tags: Data Model,reference data,generalizations,specific models,data architect

TAGGED:data modeling
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article’s core relationship is that restoring a refrigeration system does not establish trustwor
After the Cyberattack, Can You Trust the Machine?
Artificial Intelligence News
Flat editorial illustration: A technology-editorial comparison of disconnected origination, servicing, and collections records dr
The Data Reconciliation Problem Inside Disconnected Lending Systems
Big Data Exclusive Fintech
Flat editorial illustration: The article connects AI and marketing analytics with managerial judgment grounded in consumer behavi
How an Online MBA in Marketing Connects AI and Data Analytics
Analytics Artificial Intelligence Big Data Exclusive
Flat editorial illustration: The article connects commercial drone capture with integrated software workflows that organize and r
Drone Technology as a Business Investment: Calculating ROI, Costs, and Long-Term Value
Business Intelligence Marketing Software

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Resolving Many-to-Many Relationships
Data Mining

Resolving Many-to-Many Relationships

7 Min Read
Recommended read:  The Predictioneer’s Game
Predictive Analytics

Recommended read: The Predictioneer’s Game

6 Min Read
Data Driven Companies Must Understand Differences Between Fact Tables & Dimension Tables
Big Data

Data Driven Companies Must Understand Differences Between Fact Tables & Dimension Tables

5 Min Read
Data Design Is Not Optional
Best PracticesBig DataCRMData ManagementITPolicy and Governance

Data Design Is Not Optional

4 Min Read

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

Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?