Whitepaper : ER/Studio Data Architect
Master Data Modeling for Master Data Domains
Increasing dependence on enterprise-class applications has created a demand for centralizing organizational data using techniques such as Master Data Management (MDM). The development of a useful MDM environment is often complicated by a lack of shared organizational information and data modeling. In this paper, David Loshin explores some of the root causes that have influenced an organization’s development of a variety of data models, how that organic development has introduced potential inconsistency in structure and semantics, and how those inconsistencies complicate master data integration.
See Also:
- Whitepaper: Agile Data Modeling: Not an Option, but Essential
- Whitepaper: Enterprise Data Modeling for Business Intelligence Applications
- Whitepaper: Improve Your Data Modeling Skills
- Whitepaper: Is your Data Modeling Workflow Agile or Fragile?
- Whitepaper: Model Behavior: An Introduction to Data Models
- Whitepaper: The ROI of Data Modeling
- Webcast: A Perfect Ten: The Data Model
- Webcast: A Photographer and a Data Modeler Walk Into a Bar…
- Webcast: Agile Data Management vs. Agile Data Modeling
- Webcast: Becoming a Better Data Modeler: Part 1 (Data Modeling Certification)
- Webcast: Becoming a Better Data Modeler: Part 2 (Faulty Design Patterns)
- Webcast: Data Modeling and Blockchain
- Webcast: The Importance of Data Model Change Management
- Webcast: You’ve Just Inherited a Data Model – Now What?
- Webcast: Principles of Data Modeling
- Webcast: Put Together the Pieces of your Data Model Puzzle
- Webcast: SQL Server Data Modeling Best Practices
- Infographic: Improve Your Data Modeling Skills
- Infographic: Data Modeling and Collaboration with ER/Studio
Topics :
Products :
Increasing dependence on enterprise-class applications has created a demand for centralizing organizational data using techniques such as Master Data Management (MDM). The development of a useful MDM environment is often complicated by a lack of shared organizational information and data modeling. In this paper, David Loshin explores some of the root causes that have influenced an organization’s development of a variety of data models, how that organic development has introduced potential inconsistency in structure and semantics, and how those inconsistencies complicate master data integration.
See Also:
- Whitepaper: Agile Data Modeling: Not an Option, but Essential
- Whitepaper: Enterprise Data Modeling for Business Intelligence Applications
- Whitepaper: Improve Your Data Modeling Skills
- Whitepaper: Is your Data Modeling Workflow Agile or Fragile?
- Whitepaper: Model Behavior: An Introduction to Data Models
- Whitepaper: The ROI of Data Modeling
- Webcast: A Perfect Ten: The Data Model
- Webcast: A Photographer and a Data Modeler Walk Into a Bar…
- Webcast: Agile Data Management vs. Agile Data Modeling
- Webcast: Becoming a Better Data Modeler: Part 1 (Data Modeling Certification)
- Webcast: Becoming a Better Data Modeler: Part 2 (Faulty Design Patterns)
- Webcast: Data Modeling and Blockchain
- Webcast: The Importance of Data Model Change Management
- Webcast: You’ve Just Inherited a Data Model – Now What?
- Webcast: Principles of Data Modeling
- Webcast: Put Together the Pieces of your Data Model Puzzle
- Webcast: SQL Server Data Modeling Best Practices
- Infographic: Improve Your Data Modeling Skills
- Infographic: Data Modeling and Collaboration with ER/Studio
Topics :
Products :
David Loshin
David Loshin is the President of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. David is a recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 20 years, David is also a prolific author regarding business intelligence best practices, with numerous books and papers on data management.
