Improving Data Reliability in Practice: Following the Data Quality Process

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About Course

Data Quality work becomes valuable when every activity connects to a real business use. This course provides a practical ten-step method for moving from a defined use case and critical data elements to measurable Data Quality requirements, profiling and validation, issue remediation, Data Quality checks, and monitored controls.
Throughout the course, you work with one XYZ Company case and intentionally flawed data sets. Six practical exercises and eight reusable templates help you apply the method, document evidence, and connect every requirement to its CDE, lineage point, owner, check, and control.
The course focuses particularly on the contributions of Data Governance professionals, Data Stewards, and Business SMEs, while also clarifying the accountabilities of Data Owners and Technical Owners throughout the process.

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What Will You Learn?

  • Scope a Data Quality use case around a business need.
  • Identify Critical Data Elements and define their business meaning.
  • Translate business expectations into measurable Data Quality requirements.
  • Trace CDEs through the data chain and select control points.
  • Profile and validate data against approved requirements.
  • Design Data Quality checks and turn them into managed controls.

Course Content

Course Foundation
Data Quality work needs a clear business purpose, defined responsibilities, and a repeatable process. This topic establishes the XYZ Company case, the main professional contributions, and the ten-step Data Quality process used throughout the course. It also shows how each step produces the evidence and deliverables required by the next.

  • Framing the Data Quality Problem
    03:15
  • Conceptual Understanding
  • Analytical Thinking
  • Understanding the Ten-Step Process
    03:07
  • Conceptual Understanding
  • Analytical Thinking

Scope and Meaning
Data Quality work becomes manageable when the business use and relevant data are clearly scoped. This topic shows how to define a measurable business use case, identify the data sets and Critical Data Elements that matter, and establish agreed definitions and business rules before Data Quality requirements are written.

Data Quality Requirements
Data Quality requirements turn business expectations into measurable conditions for data. This topic introduces Data Quality dimensions, shows how requirements differ by data object and abstraction level, and explains how business expectations are translated into governed requirements with defined rules, thresholds, measurements, and ownership.

Lineage and Accountability
Data Quality requirements become actionable when they are connected to the data chain and assigned to accountable roles. This topic shows how lineage supports root-cause and impact analysis, how effective control points are selected, and how approved requirements move through preparation, review, approval, and publication before downstream DQ work begins.

Profiling and Validation
Data Quality analysis becomes actionable when observations are tested against approved requirements and supported by evidence. This topic distinguishes profiling from validation, shows how confirmed deviations become Data Quality issues, and explains how issues are classified, prioritized, and connected to appropriate remediation actions.

Checks and Controls
Data Quality requirements become operational when they are translated into executable checks and managed controls. This topic shows how to design checks at effective points in the data lifecycle, align them with approved requirements, and turn check results into owned responses, escalation, evidence, and ongoing effectiveness monitoring.

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Data Crossroads Academy