Data governance steers a data-driven business

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Data-driven businesses rely on data governanceHow data-driven a business really is is often revealed by how data-driven its decision making is.

I've blogged before about the difference between data-justified decision making and data-driven decision making. The former is when data is not used to drive decisions but is instead used to justify decisions after the fact. The latter is when the following things happen:

  • Data drives fact-based planning.
  • Analytics guides corporate strategy.
  • Business leaders are willing to consider new data (and new data sources).
  • Decision-making processes are designed with a feedback loop to collect data that's used to track and evaluate a decision’s outcome and help drive a better decision next time.

Where data governance fits

How does data governance play into being a data-driven business?

In general, while it has many definitions, for me data governance is driven by three aspects: Principles, policies (business rules) and procedures (data rules). A principle communicates governance intent using crisp, plain-talk statements to specify the goal of a policy. A policy consists of business rules framing the context of the step-by-step data rules of a data management procedure that must be followed in order to comply with the policy. Together, these aspects of governance align everyone at all levels of the enterprise with the desired end-state of a policy’s implementation, in addition to providing standards and metrics for measuring compliance with a policy.

A data-driven business uses data governance principles, policies and procedures to govern its decision-making processes. Principles will include statements such as “Verified information will always be used to make true data-driven business decisions. Therefore, decision-making processes will always document the data used to make a decision, including its source and data quality assessment.” Policies will include business rules such as those that indicate preferred data sources, data quality requirements and standards for determining policy compliance. Procedures specify the data rules (i.e., technical processes) executed to carry out the business rules and generate the metrics to measure policy compliance.

Good business decisions are driven by good data. Operationalizing data governance leads to better data, which leads to better business. Data governance provides the guiding principles and context-specific policies that frame the procedures of data management. In this way, data governance steers a data-driven business.

Download The SAS Data Governance Framework: A Blueprint for Success
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About Author

Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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