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CDO and Data Quality Accelerator:Strategy to Implementation
Rating: 4.1 out of 5(118 ratings)
580 students

CDO and Data Quality Accelerator:Strategy to Implementation

Masterclass:Data Quality,Data Governance,Chief Data Office,Digital Transformation,Data Strategy,Metadata,Data Profiling
Created byBing Yu
Last updated 3/2025
English

What you'll learn

  • Chief Data Officer
  • Chief Data Office
  • Centralised Chief Data Office Organisation Structure
  • Data Strategy
  • Data Monetisation
  • Data Governance
  • Data Stewardship
  • Data Quality
  • Data Architecture
  • Data Lifecycle Management
  • Operations Intelligence
  • Advanced Analytics and Data Science
  • Data Quality Objectives
  • Data Quality Dimensions and Examples
  • Roles and Responsibilities of Data Owners and Data Stewards
  • Data Quality Management Principles
  • Data Quality Management Process Cycle
  • Data Profiling
  • Data Profiling Technologies (Informatica, Oracle, SAP and IBM)
  • Metadata
  • Differences Between Technical and Business Metadata
  • Business Validation Rules
  • Data Quality Scorecard (with Informatica example)
  • Tolerance Level
  • Root Cause Analysis
  • Data Cleansing
  • Data Quality Issue Management
  • IOS 8000
  • Data Domain

Course content

7 sections • 18 lectures • 1h 30m total length

  • Greetings1:16

    Course structure and contents introduction

  • Why Is Digital Transformation Important and What Is The Definition?2:23

    Airservices Australia invested in a digital transformation strategy to improve data and information management, as well to empower decision making across the company. Augmenting its traditional analogue technologies, Airservices committed to using digital technologies and cloud services while meeting the requirements of national and global technology standards and protocols. Read more of this case study in the link.

  • Who Is Chief Data Officer and What Is Chief Data Office?3:38
  • Key Concepts of Chief Data Office Functions, Roles and Responsibilities7:39
  • Where Does Data Quality Fit In The Chief Data Office and Its Main Objectives3:09

Requirements

  • No prior Chief Data Office or Data Quality knowledge is required
  • A basic understanding about digital transformation will be beneficial but not required

Description

In light of the accelerating AI revolution across industries in the past years, it has never been more relevant than it is now that you should improve your digital literacy and upskill yourself with data analytics skillsets. [updated in 2024]

This course features the latest addition of an organisation structure - Chief Data Office which enables an organisation to become data and insights driven, no matter it's in a centralised, hybrid or de-centralised format. You'll be able to understand how each of the Chief Data Office function works and roles and responsibilities underpinned each pillar which covers the key digital concepts you need to know. There is a focus on the end-to-end data quality management lifecycle and best practices in this course which are critical to achieving the vision set out in the data strategy and laying the foundations for advanced analytics use cases such as Artificial Intelligence, Machine Learning, Blockchain, Robotic Automation etc. You will also be able to check your understanding about the key concepts in the exercises and there are rich reading materials for you to better assimilate these concepts.

At the end of the course, you'll be able to grasp an all-round understanding about below concepts:

  • Digital Transformation

  • Chief Data Officer

  • Chief Data Office

  • Centralised Chief Data Office Organisation Structure

  • Data Strategy

  • Data Monetisation

  • Data Governance

  • Data Stewardship

  • Data Quality

  • Data Architecture

  • Data Lifecycle Management

  • Operations Intelligence

  • Advanced Analytics and Data Science

  • Data Quality Objectives

  • 6 Data Quality Dimensions and Examples

  • Roles and Responsibilities of Data Owners and Data Stewards (Data Governance)

  • Data Quality Management Principles

  • Data Quality Management Process Cycle

  • Data Domain

  • ISO 8000

  • Data Profiling

  • Data Profiling Technologies (Informatica, Oracle, SAP and IBM)

  • Metadata

  • Differences Between Technical and Business Metadata

  • Business Validation Rules

  • Data Quality Scorecard (with Informatica example)

  • Tolerance Level

  • Root Cause Analysis

  • Data Cleansing

  • Data Quality Issue Management (with a downloadable issue management log template)

After you complete this course, you will receive a certificate of completion.

So how does this sound to you? I look forward to welcoming you in my course.

Cheers,

Bing

Who this course is for:

  • Students who are interested in learning about the end-to-end data quality management fundamentals and best practices
  • Students who have non-digital background and would like to explore career opportunities across data analytics disciplines
  • Students who have technical background and would like to understand from a big picture about how their work fits in a wider digital organisation
  • Students who would like to understand how Chief Data Office structure works in an organisation
  • Students who would like to learn about data ownership and data stewardship
  • Students who are considering applying data quality standards and implement data quality management processes within their organisations
  • Students who are taking their starting steps out of their studies in the field of data analytics

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