By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Data Modeling with Generalizations – The Tool Issue
Share
Notification Show More
Latest News
ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
Aa
SmartData Collective
Aa
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
Last updated: 2009/10/15 at 4:21 PM
KarenLopez
5 Min Read
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:

image 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 . …

More Read

understand the difference between fact tables and dimension tables

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

Top 10 Powerful Data Modeling Tools For 2021
6 Amazing Cloud Based Data Modeling Tools to Try in 2017
Data Design Is Not Optional
Recommended read: The Predictioneer’s Game



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:

image 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
KarenLopez October 15, 2009
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

understand the difference between fact tables and dimension tables
Big Data

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

5 Min Read
data modeling tools to analyze
Modeling

Top 10 Powerful Data Modeling Tools For 2021

8 Min Read
data-modeling-tools
Modeling

6 Amazing Cloud Based Data Modeling Tools to Try in 2017

4 Min Read
data modeling
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.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?