FB05 - Practical Data Quality

OVERVIEW

The course faces up to principles, processes and activities involved in the creation of a Data Quality function.

It also explores how to get started with this feature and outlines the basic steps for successful results.

LEARNING OBJECTIVES

The aim of the course is to help attendees to familiarize themselves with the concepts, practices and tools that can make a Data Quality initiative a case of success.

Contents include:

  • Introduction to Data Quality: what it means to have quality data in the context of business processes and how to define it.
  • Measure data quality.
  • Identify causes, effects, benefits and impacts of Data Quality.
  • Framework to improve data quality.
  • Data Profiling.
  • Technological supports (e.g. Machine Learning) and software.
  • How to formulate a “Business Case” to support Data Quality initiatives.
  • How can we establish a connection between Data Quality and business objectives and needs?
  • Inclusion of Data Quality within a Data Management program.

FORMAT

Training in Classroom

Physical classes taught by Trainer.

Duration: 15 hours divided in 2 days

The course includes:

  • access to the digital material of the lessons through the FIT Academy e-learning platform;
  • attendance certificate issued by FIT Academy and DAMA local chapter.

Remote Learning with Trainer

Live webinar classes taught online by Trainer.

Duration: 12 hours divided in 4 modules

The course includes:

  • access to the digital material of the lessons through the FIT Academy e-learning platform;
  • attendance certificate issued by FIT Academy and DAMA local chapter.

TRAINERS

Christopher Bradley

Information Strategist, Independent Advisor & Trainer

Michele Valentini

Management Consultant and Teacher

LANGUAGES

  • Trainers Spoken Language: English, Italian
  • All the course materials are provided in English

SOURCES

  • DAMA-DMBoK Data Management Body of Knowledge 2nd Edition. Technics Publications. 2017.
  • Loshin, D. Enterprise Knowledge Management: The Data Quality Approach. Morgan Kaufmann, 2001
  • Jugulum, R. Competing with High Quality Data. Wiley, 2014

CALENDAR

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