An industry data model acts as a blueprint based on best practices, government regulations and the complex data and analytic needs of an industry. Industry data models from IBM provide a predesigned framework that can help you manage data warehouses and data lakes better, enabling you to gather deeper insights and accelerate your analytics journey.
Helps analyze and design functional requirements faster for your enterprise data using industry-specific information infrastructures
Enables you to create and rationalize data warehouses using a consistent architecture to model changing requirements
Helps reduce risk and deliver better data to apps across your organization to accelerate transformation
Helps create enterprise-wide KPIs to address compliance, reporting and analysis requirements
Predefined vocabularies, KPIs and data structures for enterprise governance and analytics projects
Financial services-specific vocabularies, workflows, services, interfaces and components, which can help with the analysis and design of business process management and service-oriented architectures
Insurance vocabularies, KPIs and data structures can accelerate governance and analytics projects
Energy and utilities vocabularies, KPIs and data structures that can accelerate governance and analytics projects
Healthcare vocabularies, KPIs and data structures that can accelerate governance and analytics projects
Align concepts from industry regulations and standards with your business data to support compliance management.
Manage master data for single or multiple domains — including customers, suppliers, products, accounts and more.
Understand the guidelines and practices for using industry models in a data lake initiative.
Learn the guidelines for industry models to help build effective and accurate vocabulary assets.
Explore using industry models from IBM in the Hive and HBASE areas of the Hadoop landscape.
Explore the role of context, governance, integration and industry models.
Learn how leading financial institutions use industry models to succeed with use cases.
Find out how better data integration, data quality and availability in your data lake can help you avoid data swamps.
Read how support for GDPR and CCPA data privacy helps ensure the right data architecture.