Enterprise Architecture Modelling:
Define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritise technology investments.
Business Process Modelling:
Define, map and analyse workflows and build models to drive process improvements, including determining where controls are needed for greater security, compliance and overall risk management.
Design and deploy new and hybrid data structures and resources with any data from anywhere for new applications, data aggregation and integration, master data management, Big Data analytics and business intelligence.
Give your data professionals the ability to automate enterprise metadata management, data mapping, code generation and data lineage for greater accuracy and faster time to value.
Let stakeholders define and discover data assets relevant to their roles within a business context, facilitating enterprise collaboration and accelerating the transformation of data insights into the desired outcomes.
Benefits of Integrated,
Persona-Based Data Intelligence
Whether you’re a data scientist, data steward, ETL developer, enterprise architect, business analyst, risk manager, chief data officer or chief executive – you use data to do your job, but you need it to be current and accurate. Thanks to the broadest set of metadata connectors and automated code generation, sensitive data discovery, data mapping and cataloging tools for data preparation, modelling and governance, every stakeholder can discover, understand, govern and socialise data assets.
The persona-based erwin EDGE Platform provides the most agile, efficient and cost-effective means of launching and sustaining a comprehensive data governance initiative.
Its components and the roles that use them are then integrated and enabled by cross-platform capabilities:
Impact analysis: See how changes to the data design will impact enterprise operations.
Lineage: Understand where data came from and what happened to it along the way.
AI and Machine Learning: Use artificial intelligence and automation to maintain an efficient and cost-effective data governance framework.
Data Transformation: Change instance data for data integration, aggregation and harmonisation.
Data Quality: Integrate data quality metrics and artefacts with metadata quality and standardisation capabilities for a consistent data landscape.
Metadata Ingestion and Translation: Automatically collect and organise key metadata for an accurate depiction of data at rest and data in motion.
Workflow and Collaboration: Manage the workflows and interactions of different stakeholders through pre-defined, configurable roles and processes.
Socialisation and Discovery: Enable stakeholders to form communities based on areas of common interest to share insights and collaborate.