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One of the challenges for a digital organization is building and maintaining a clear overview of all its integrations.
Traditionally, this was the responsibility of the integration team. However, with the rise of cyber threats and resilience demands like DORA and NIS2, Enterprise Architects are playing a bigger role. Organizing the digital landscape is now crucial - and it’s not limited to organizations classified as critical infrastructure.
A data-driven approach to Enterprise Architecture (EA) can make a big difference. When your EA repository acts as a digital twin of your organization, it provides an accurate, documented representation of key elements, such as the business structure, applications, data objects, and integrations. This clarity makes it much easier to generate and maintain an overview of integrations. For example, here’s a generated integration map for one application, using sample data.
Measuring Enterprise Architecture is a good idea, as I’ve explained in my post on LinkedIn.
By adopting Gartner’s concept of the Digital Twin of an Organization (DTO) and ensuring that your EA repository accurately reflects the key concepts, generating visualizations of these elements and their relationships becomes straightforward.
These visualizations can be made of entire EA repository, or focus on specific slices, such as all concepts that together implement a particular business capability. Such visual insights promote shared understanding and facilitate management and governance.
Understanding enterprise architecture (EA) and how it can best support a large organization is not easy. The discipline is inherently complex and there are many business and technical concepts to put in perspective.
How do we choose the right concepts to accurately reflect business realities, user needs and IT solutions for the different business lines?
What methods should we use to effectively scope, describe and interconnect these concepts? How do these methods assist in managing the portfolio or driving digital transformation?
In relational database design, data modeling is typically done using Entity-Relationship (ER) diagrams. This approach may lead to suboptimal models that are difficult to understand, due to a lack of semantic clarity and management issues for larger diagrams. This article explores a more elegant solution: Semantic Data Modeling with Xplain.
Data modeling for relational databases is typically done with ER diagrams, which represent the data structure that is then implemented in a database system using the structured query language (SQL) or one of its product-specific dialects.