Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas

October 31, 2020
Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas

What value does semantic data modeling offer? As an informationarchitect or data science professional, let's say you have anabundance of the right data and the technology to extract businessgold—but you still fail. The reason? Bad data semantics.In this practical and comprehensive field guide, author PanosAlexopoulos takes you on an eye-opening journey through semanticdata modeling as applied in the real world. You'll learn how tomaster this craft to increase the usability and value of your dataand applications. You'll also explore the pitfalls to avoid anddilemmas to overcome for building high-quality and valuablesemantic representations of data.Understand the fundamental concepts, phenomena, and processesrelated to semantic data modelingExamine the quirks and challenges of semantic data modeling andlearn how to effectively leverage the available frameworks andtoolsAvoid mistakes and bad practices that can undermine yourefforts to create good data modelsLearn about model development dilemmas, includingrepresentation, expressiveness and content, development, andgovernanceOrganize and execute semantic data initiatives in yourorganization, tackling technical, strategic, and organizationalchallenges