CDO and Data Quality Accelerator:Strategy to Implementation
CDO and Data Quality Accelerator:Strategy to Implementation, available at $34.99, has an average rating of 4.75, with 18 lectures, based on 110 reviews, and has 550 subscribers.
You will learn about 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 This course is ideal for individuals who are Students who are interested in learning about the end-to-end data quality management fundamentals and best practices or Students who have non-digital background and would like to explore career opportunities across data analytics disciplines or Students who have technical background and would like to understand from a big picture about how their work fits in a wider digital organisation or Students who would like to understand how Chief Data Office structure works in an organisation or Students who would like to learn about data ownership and data stewardship or Students who are considering applying data quality standards and implement data quality management processes within their organisations or Students who are taking their starting steps out of their studies in the field of data analytics It is particularly useful for Students who are interested in learning about the end-to-end data quality management fundamentals and best practices or Students who have non-digital background and would like to explore career opportunities across data analytics disciplines or Students who have technical background and would like to understand from a big picture about how their work fits in a wider digital organisation or Students who would like to understand how Chief Data Office structure works in an organisation or Students who would like to learn about data ownership and data stewardship or Students who are considering applying data quality standards and implement data quality management processes within their organisations or Students who are taking their starting steps out of their studies in the field of data analytics.
Enroll now: CDO and Data Quality Accelerator:Strategy to Implementation
Summary
Title: CDO and Data Quality Accelerator:Strategy to Implementation
Price: $34.99
Average Rating: 4.75
Number of Lectures: 18
Number of Published Lectures: 18
Number of Curriculum Items: 19
Number of Published Curriculum Objects: 19
Original Price: £69.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
In light of the accelerating AI revolutionacross industries in the past years, it has never been more relevant than it is now that you should improve your digital literacyand upskill yourself with data analytics skillsets. [updated in 2024]
This course features the latest addition of an organisation structure – Chief Data Officewhich 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 responsibilitiesunderpinned 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
Course Curriculum
Chapter 1: Digital Transformation and Chief Data Office
Lecture 1: Greetings
Lecture 2: Why Is Digital Transformation Important and What Is The Definition?
Lecture 3: Who Is Chief Data Officer and What Is Chief Data Office?
Lecture 4: Key Concepts of Chief Data Office Functions, Roles and Responsibilities
Lecture 5: Where Does Data Quality Fit In The Chief Data Office and Its Main Objectives
Chapter 2: Data Quality Concepts
Lecture 1: Why Is Data Quality Important and What Is The Data Quality Definition?
Lecture 2: 6 Data Quality Dimensions and Examples
Lecture 3: Data Owner and Data Steward Roles and Responsibilities
Chapter 3: Data Quality Management
Lecture 1: What Is Data Quality Management and What Is DQM Definition?
Lecture 2: Data Quality Management Principles
Lecture 3: 5 Steps of Data Quality Management Process Cycle
Chapter 4: Data Profiling
Lecture 1: Data Profiling Definition and Technology Tools(Informatica, IBM, Oracle and SAP)
Lecture 2: Metadata, Technical Metadata and Business Metadata Definitions
Lecture 3: Business Validation Rules Based On Data Profiling and Metadata
Chapter 5: Data Quality Scorecard
Lecture 1: Data Quality Scorecard Definition. Tolerance Level and Demo
Lecture 2: Root Cause Analysis Steps for Data Quality Issues
Lecture 3: Data Cleansing and Data Quality Issue Management
Chapter 6: Project Exercises
Chapter 7: Course Summary
Lecture 1: Course Lectures Recap
Instructors
-
Bing Yu
Data&AI Strategy|Digital Transformation|Consulting|M&A
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 7 votes
- 3 stars: 20 votes
- 4 stars: 27 votes
- 5 stars: 53 votes
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