Enterprise catalog management and data protection with Watson Knowledge Catalog on IBM Cloud Pak for Data 4.0.x

In this course, students will learn to:

• Prepare the governance environment
• Create a governance workflow and framework
• Describe data
• Discover the data
• Apply data quality rules
• Analyze the data 

Enterprise catalog management and data protection with Watson Knowledge Catalog is the third course in the learning path that IBM designed for professional Solution Architects of Cloud Pak for Data, and is intended to give you an overview of the Watson Knowledge Catalog service on the Cloud Pak for Data platform.

Before taking this course, students should be able to:

• Explain the purpose of Cloud Pak for Data and the value it brings to the business
• Describe its basic architecture 
• Differentiate between Cloud Pak for Data and Red Hat OpenShift Container Platform
• Define the AI Ladder and its associated roles and services
• Log in to Cloud Pak for Data and complete an analytics project

Introduction

• Recall prerequisite knowledge and skills
• Outline the course flow
• Define the lab environment
• Outline the narrative

Preparing the governance environment

• Summarize the purpose of Watson Knowledge Catalog
• State the benefits of using catalogs
• Describe how to control access to data
• List the characteristics of the Default Catalog
• Create user accounts and define roles
• Load data into an integrated database

Creating a governance workflow and framework

• Summarize enterprise governance
• Describe a governance workflow
• Establish a governance workflow
• Select a data class matching method
• Create governance artifacts

Describing data

• Differentiate among data descriptors
• Create a business term
• Import and debug a glossary of terms
• Publish business terms
• Add a classification

Discovering the data

• Explain the purpose of data curation
• List the data curation process steps
• Differentiate between the automated data discovery methods
• Evaluate the results of a Quick Scan and Automated Discovery
• Publish discovery results to a data quality project
• Determine relationships between data and governance artifacts

Applying data quality rules

• List the types of data quality rules
• Define and run a quality rule
• Build an automation rule
• Publish a refined data set to the catalog

Analyzing the data

• Summarize the data governance journey
• Create an analytics project
• Add data to the project
• Refine the data
• Review the data history

Review and evaluation

• Summarize governance artifacts
• Summarize data governance
• Complete the final quiz
• Discover what's next in the learning path