What Is a Data Architect?

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A data architect provides clear specifications, models, and definitions, translating a business’ Data Strategy into a Data Architecture and implementing this structure to align with an organization’s Data Governance. An architect is one who designs and advises on the construction of something. Data architects take an organization’s raw data and data assets and builds a solution leveraging this material. They are the “conduit between business and technology,” says Lynn White of Newswire. They must be able to:

  • Understand and translate the organization’s overall Data Strategy into a plan.
  • Understand and oversee technical development and processes, including hardware and software, to implement the Data Strategy, usually with the help of IT.
  • Use Data Modeling and Data Analytics skills to understand and leverage relationships among data elements.
  • Work with departments across the organization to establish and reinforce good Data Architecture–related practices and policies.
  • Put data in business context.
  • Communicate clearly and consistently about the Data Architecture across a business, so that all workers know how to use it effectively.

Other Definitions of Data Architects Include:

  • People “responsible for the detailed design and development of a data resource.” (Michael Brackett)
  • People “responsible for shaping the way an organization captures, organizes, integrates, analyzes, and stores data.” (Newswire)
  • People that “determine what to do with data and how best to organize it so that it adds value instead of weighing down the processes.” (Forbes)
  • People that “create blueprints for data management systems” and “design a plan to integrate, centralize, protect, and maintain a company’s data sources.” (Master’s in Data Science)
  • People that “translate requirements into systems.” (CIO)

Businesses Use Data Architects to:

  • Create a proper foundation for which to base data initiatives.
  • Detect any root issues before major time and money is wasted.
  • Organize data for effective machine learning and artificial intelligence.
  • Resolve disparate data resources so that the business can leverage its data assets.

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