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Complete Introduction to Data Quality and Chief Data Office

 

Complete Introduction to Data Quality and Chief Data Office

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Complete Introduction to Data Quality and Chief Data Office Master key Digital Transformation concepts and learn Data Quality Management, Data Governance, Metadata, Data Profiling
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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


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 Digital Transformation across industries in the past years, it has never been more relevant than it is now during the global pandemic that you should improve your digital literacy and upskill yourself with data analytics skillsets. [Course updated in Nov 2020]
  • 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:

  • 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

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