Description

Quality management is necessary to guarantee reliable data for operational and decision-making IS. You will address the essential points to initiate this approach: data governance issues, regulatory issues, compliance with business rules, quality measurement and qualification of the input chain.

Who is this training for ?

For whom ?

Data Quality Analysts, data quality project managers, IS urban planners, AMOA IS data quality consultants, business IS managers, quality method experts, IS managers.

Prerequisites

Training objectives

Training program

• Introduction
  • Strategic role of data for the company.
  • Difference between data and information.
  • The different sources of company data.
  • The different forms of data exploitation.
  • Architectures: relational, NoSQL or BigData.
• Definition of quality and methodological framework
  • Defining and measuring data quality.
  • Data quality methodologies.
  • Comparison between TDQM/DWQ/AIMQ/ORME Data Quality.
  • The principles for evaluating the quality of business data.
  • Summary of Quality methodologies.
  • Dimensions - definitions and measurements.
  • Case study: Red case: a group launches a Quality approach for compliance with Solvency 2 and wishes to improve the quality of its customer data.
  • General organization of the approach .
• Data quality management approach
  • The place of quality in the Governance approach.
  • The actors and the organization.
  • The Cobit example.
  • Implementation of the Project approach.
  • The cost of non-quality.
  • Scope of the Quality approach.
  • Level of approach and granularity.
  • Case study.
  • The 10 actions to be launched by the Governance committee.
• Quality control and best practices
  • Quality auditing.
  • Identifying low quality data.
  • Collecting and storing quality metrics.
  • An approach centralized data quality system.
  • Types of controls and statistical tools.
  • Exploitation and evaluation of quality measures.
• Operational monitoring of data quality
  • Quality dashboards.
  • Continuous improvement cycle.
  • What role for Governance?
  • Case study.
  • The group's Data Quality team sets up reporting: definition of indicators and acquisition method.
• Raise the level of quality
  • Intervene upstream of the chain.
  • Identification of atypical cases.
  • Targeted management of cases of low quality data.
  • The data reconciliation.
  • Case study: Example of data reconciliation in a group following the integration of a subsidiary.