Course Code: bespokedataquality
Duration: 21 hours
Prerequisites:

A passion to see information managed as a corporate asset.

Overview:

This 3-days Data Quality Management course address the key aspects of Data Quality management & provides practical take away actions that will enable you to start a Data Quality initiative in your organisation. The course draws up the Data Quality discipline as defined in the DAMA body of knowledge (DMBoK).
DAMA (The Data Management Association) is the World’s leading independent body for Information management professionals, offering certification, mentoring, and guidance.

To give participants a firm grounding in the basics of Data Quality Management and to deep dive into the principles, processes and activities involved in creating a working Data Quality function. This 3-days class explores a framework for Data Quality management and how to get started with a Data Quality initiative, including the key steps for achieving and sustaining Data Quality success

Course Outline:

Making the case for Data Quality

  • How can we make the connection between Data Quality and business needs?
  • What does “Data Quality” mean in the context of business processes and can we define it?
  • What is Data Quality Vs Data Quality Management and why does it matter?
  • What happens when it goes wrong? We will examine many examples of Data Quality issues from real world cases and assess their implications and see how these could have been avoided

Measuring Data Quality

  • What are the 6 Dimensions of Data Quality as defined by DAMA?
  • What do each of these dimensions’ mean?
  • What are the pitfalls of looking at just one Data Quality dimension in isolation?
  • Alternative (non DAMA) views of Data Quality Dimensions.
  • How can we evaluate data quality for the data quality dimensions and are these applicable to the problems being faced? This is an essential step to provide the input for root cause analysis and remediation approaches.
  • 4 different styles and approaches to reporting Data Quality will be discussed highlighting the benefit and applicability of each.

Assessing the causes & impact of poor Data Quality

  • Continuing the Data Quality measurement framework, what is the relationship between DQ Dimensions, DQ Measures & DQ Metrics.
  • What is their applicability and how many should we include in our DQ assessments?
  • What are the techniques to determine the impact of poor-quality data on the business?
  • What are the benefits of increasing Data Quality and the business impacts of poor Data Quality?
  • Root Cause Analysis: What really caused the problem? An approach for identifying and prioritizing the real causes of the data quality problems?
  • Techniques for Root Cause Analysis including “5-whys” & “Fishbone”
  • Developing targeted strategies and approaches for addressing the causes.

A framework for improving Data Quality

  • A DQ reference model & how to apply it.
  • Starting and sustaining a Data Quality initiative: The key steps for achieving Data Quality success, and the activities & structures that are required together with the necessary steps for creating the foundation for DQ.
  • What are the typical organisation roles, responsibilities, organization structures and principles that should be in place to ensure successful Data Quality?
  • How can we put all of this together into a workable framework for establishing and sustaining Data Quality in your organization?
  • Now that you’ve made a start, how do you sustain Data Quality. How can we bake Data Quality (and other Data considerations) into our “Business as Usual” activities to make it stick?

Automated support for improving Data Quality

  • What tooling & automated support exists for Data Quality initiatives?
  • What are the types and the applicability of software tools to support a Data Quality initiative?
  • What is a reference architecture model for Data Quality tools, and the common functions, capabilities, and the differences between them?
  • What items should we examine when selecting Data Quality tooling? An evaluation checklist will be discussed covering what to look out for.

Fitting Data Quality into an overall Infromation Management Framework

  • What is the relationship between Data Quality, Master Data Management, Data Governance & the other Information disciplines?
  • What is the crucially important role of data models in a Data Quality initiative?
  • How is this governed? The essential part that Data Governance undertakes.
  • How do we measure the success of a Data Quality initiative & the pitfalls of tactical Data cleaning where the data is corrected in situ?