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What Is Data Architecture?

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Data Architecture describes the models and artifacts that connect a Business Strategy and Data Strategy with its technical execution. Primarily, Data Architecture 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 Data Architecture by focusing on the following components:

  • Data Architecture Outcomes: Models, definitions, and data flows on depicted at various levels, usually referred to as Data Architecture artifacts
  • Data Architecture Activities: Forms, deploys, and fulfills Data Architecture intentions
  • Data Architecture Behaviors: Collaborations, mindsets, and skills impacting business division and enterprise Data Architecture

Other Data Architecture Definitions Include:

  • 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)

Data Architecture Use Cases:

  • 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 Data Architecture 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
  • Translate business needs into data and system requirements
  • Facilitate alignment of IT and business systems
  • Manage complex data and information delivery throughout the enterprise. Acts as agents for change transformation and agility
  • “Depict the flow of information between people (users) and Business Processes”

Image used under license from Shutterstock.com

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