Successfully managing data quality

Introduction and definition

• From data to information to competitiveness.
• Definition of data quality and data quality management (DQM).
• Why DQM? - Drivers for the introduction of a company-wide DQM.
• Added value of data quality management for organizations.
• DQM maturity model (Where would you classify your company?).

Poor data quality

• Data quality requirements (legal/economic).
• Causes and effects of poor data quality on the organization as a system.
• Impact of poor data quality on key figures.
• Data quality classes and objects of investigation for the collection of poor data quality.

Define and measure data quality criteria (data profiling)

• Overview of data quality dimensions for optimal measurement of data quality.
• Define data quality dimensions (practical exercise).
• Apply data quality dimensions (practical example).

Development of logic trees for determining and evaluating cause/effect

• Principles of logic trees and added value.
• Apply logic trees (practical exercise).
• Evaluation of findings from logic trees.

Derivation of improvement measures and analysis of costs/benefits

• Develop logic trees for decision making for an optimal cost/benefit assessment (practical exercise).
• Derive improvement measures (practical exercise).

Development of data quality reporting and data quality index

• DQM control loop.
• From prototyping to DQ standard reporting.
• Development and implementation of a process-oriented DQ index.

Data quality organization and processes

• Roles and responsibilities.
• Standard processes: Data profiling, data quality monitoring, error tracking and improvement.
• Classification of data quality in the data governance model.