Data Architecture describes the models and artifacts that connect a Business Strategy and Data Strategy with its technical execution. Primarily, it provides a foundation for people and systems to work with data most efficiently.
Data Architecture is as much a business decision as it is a technical one, as new business models and entirely new ways of working are driven by data and information.
Since Data Architecture bridges product and service delivery with data compute and storage capabilities, Data Quality drives architecture by focusing on the following components:
Outcomes: Models, definitions, and data flows on depicted at various levels, usually referred to as architecture artifacts
Activities: Forms, deploys, and fulfills architecture intentions
Behaviors: Collaborations, mindsets, and skills impacting business division and enterprise architecture
Specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy (DAMA-DMBoK)
“Common vocabulary expressing integrated requirements ensuring that data assets are stored, arranged, managed, and used in systems in support of an organizational strategy.” (Dr. Peter Aiken)
“A set of rules, policies, and models that determine what kind of data gets collected, and how it gets used, processed and stored within a database system.” (Keith D.Foote)
“Using data effectively and built on a foundation of business requirements.” (McKinsey)
“Describes how data is collected, stored, transformed, distributed, and consumed. IT includes rules governing structured formats, such as databases and file systems, and the systems for connecting data with the business process that consumes it.” (Harvard Business Review)
“Models, policies, rules, or standards that govern which data is collected, and how it is stored, arranged and put to use in a database system and or in an organization.” (Business Dictionary)
Use Cases Include:
DataOps: Monitoring and improving data flow to create predictable data delivery and make existing data structures adaptable.
Machine Learning (ML): Building algorithms that adjust outputs from analyzing and synthesizing new input patterns.
Data Architecture Elements: Applying architectural data elements, such as those depicted below, to communicate where data exists and how this data moves from one process to another.
Visual Example of Architectural Data Elements:
Image Credit: Shutterstock.com
Businesses Use Data Architecture To:
Strategically prepare organizations to evolve quickly and to take advantage of business opportunities inherent in emerging technologies
