Practical Data Quality Management - DAMA - Remote Training
DMQUAL-EN-D
Who should attend?
Information is at the heart of all organizations, akin to blood flowing through its arteries and veins. However, all too often Information is not professionally managed with the rigor and discipline that it demands. Nonetheless the implications of poorly managed information can be catastrophic, from ICO and other regulatory sanctions ultimately to business collapse. Professor Joe Peppard summed it up when he said, “The very existence of an organization can be threatened by poor data”.
This course will provide the rationale why information management is critical and provide methods and practices for addressing key information management challenges.
Public :
This course is intended for personnel involved in Information management, data governance, master data management and/or data quality, initiatives including: information managers, information quality practitioners, executives, technology leaders, business technology partners, business analysts, enterprise architects, information architects, and data architects.
Level :Awareness
Prerequisite :
No pre-requisistes are necessary to follow this course.
Course Content
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 different facets (dimensions) of data quality?
What do each of these dimensions’ mean?
What are the pitfalls of looking at just one data quality dimension in isolation?
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 data quality dimensions, data quality measures and data quality metrics.
What is their applicability and how many should we include in our data quality 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 data quality reference model & how to apply it.
Starting and sustaining a data quality initiative: the key steps for achieving data quality success, and the activities and structures that are required together with the necessary steps for creating the foundation for data quality.
What are the typical organization 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 INFORMATION 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?
MINI-PROJECT
Learning Objectives
The difference between “data quality” and “data quality management” and why it matters,
The relationship between data quality management and other core information management disciplines particularly master data management, data modeling and data governance,
Who is involved in making data quality initiatives work,
The major concepts that are fundamental to data quality management, such as a framework for Information quality, information life cycle, data quality dimensions, business impact techniques, root cause analysis techniques, etc..,
More
Coordinator :IFP Training instructors, with expertise in the field and trained in modern teaching methods adapted to the specific needs of learners from the professional world.
To French entities : IFP Training is referenced to DataDock ; you may contact your OPCO about potential funding. Please contact our disabled persons referent to check the accessibility of this training program : referent.handicap@ifptraining.com
IFPT Account
