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Align data governance with business-driven data architecture

The role of the data analyst has gained prominence in relation to the recognized criticality of processed and analyzed information as an integral component of the business processes of an organization. Yet, data analysis is just one factor contributing to business success. A wide variety of transaction processing and operational systems to run and manage the business employ the same data that flows through to the analysis to improve the business. This continuous need for reuse underscores how import are maintaining consistency and coherence among the varied data assets used across the enterprise.

Some organizations try to address this need by defining an enterprise data architecture. However, situating this practice within the information technology (IT) department may stress the need for tools, technology, and database environments over assurances of satisfying requirements of meeting business expectations and cross-functional coherence and synchronization. How we define an effective and meaningful data architecture must engage both the business and information management teams so that:

  • Business objectives and business processes inform the design of the enterprise data architecture.
  • We operationalized data governance to ensure standards in application data use and consistency of data utilization.

Conventional siloed data design has impeded the enterprise perspective on information use, but a paradigm is emerging that recognizes the critical advantage of information and seeks to establish a consistent enterprise data architecture with harmonized semantics. By using an integrated set of repository-based business process models and business-driven data architecture, data governance can not only ensure the quality of enterprise data, it can help guarantee that the business processes will achieve the stated business objectives.

Read the 8-page whitepaper “Aligning Data Governance with Business-Driven Data Architecture by David Loshin to learn how:

  • Siloed data modeling impedes the enterprise data strategy.
  • Business objectives and business processes drive data architecture.
  • Data governance enforces data policies.
  • Business-driven data architecture actualizes data governance.

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