What is information architecture?
Information architecture is the art and science of structuring and organizing information to make it accessible, understandable, and usable by humans and AI in the enterprise.
As data and information proliferate at unprecedented rates, the discipline of information architecture enables the evolution of raw and unstructured pieces of data into structured, cohesive, and actionable knowledge.
When information can be found and applied quickly and accurately, organizations can achieve differentiation through the strength of its knowledge, which is accomplished through thoughtful planning and design of the underlying structures and flows of data across workflows, domains, and business processes.
What are applications of information architecture?
What information architecture expertise do I bring to the table?
Information Classification
Information classification involves the thoughtful and systematic categorization of information based on its type, sensitivity, value, or other attributes.
The classification of information is core to efficient information management, implementation and monitoring of access controls, optimizing information retrieval, and protecting data at every step of a workflow or process.
The process of classifying information is an important exercise that enables security, visibility, and compliance with standards and controls across domains and applications.
Metadata Management
Metadata management focuses on the creation, maintenance, and usability metadata to contextualize and organize data and information.
Designing and implementing thoughtful metadata models and structures creates a robust foundation for discovering, accessing, interpreting, and applying data to support effective decision making.
Disciplined metadata management makes data discoverable and usable, amplifying its value and reach across the organization by both humans and AI processes such as Machine Learning (ML) and training Large Language Models (LLM).
Content Management
Content management is a common discipline within most enterprises and encompasses the technologies, tools, and processes that enable and support the lifecycle of content from creation through retirement and archiving.
Managing content involves structuring, tagging, versioning, and organizing content and documents in a manner that enables discoverability, searchability, efficient retrieval, and the enforcement of effective governance and access control policies.
In addition to underpinning effective and compliant human knowledge sharing, the effective management of both structured and unstructured content supports emerging AI-related technologies such as vector based databases and Retrieval Augmented Generation.
Canonical Data Modeling
Canonical data modeling is process of designing and implementing a standardized and consistent way of representing entities and relationships across an organization and between differing data formats.
The objective of an overarching enterprise-wide data model is to establish a common, ubiquitous language for data and metadata to facilitate integration and interoperability between disparate internal and external systems and data sources.
Designing and implementing a canonical data model and the underlying structures to support it will naturally reduce the complexities of managing data, eliminate ambiguity through the mapping and use of a common vocabulary, and facilitate the consistency and understanding of data across and even beyond the enterprise.
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