Equip your team with the fundamentals of Collibra DataIntelligence Cloud and drive its adoption within your organization. Ask our trainers how to tailor this course for your organization’s need.

Target Audience: Beginner / All

Key Takeaways:

• Create and characterize assets for critical data, business semantics, and governance practices
• Formulate asset definitions using best practices
• Build accessible asset views that stakeholders can understand and adopt
• Propose and approve assets

Prerequisite Knowledge Required:
None

Collibra's Guided Stewardship operating model is the framework for Collibra Catalog. Using this operating model, we show you how to confidently interpret your data by dissecting its data layers, tracing relationships between them and identifying the asset types used within each layer.

Target Audience: Beginner Data / Business Stewards / Data Governance Manager / Lead

Topics:

• Introduction
• Conceptual layer
• Logical layer
• Physical layer

Prerequisite Knowledge Required:

• None

How can you use Collibra to represent your critical data elements to data consumers? Developing a broad conceptual framework for how assets work will take you a long way towards this goal. Join us and we will take you through some of the fundamental concepts and processes necessary to design and curate critical data elements for your organization.

Prerequisite knowledge required:
Collibra basics, Collibra terminology

Target audience:

• Platform Admin
• Architect
• Data / Business Stewards
• Data Governance Manager / Lead

Do you want to import spreadsheets into Collibra? If so,let us show you some of the nuances you’ll need to know to be successful when importing assets. We cover strategies like how to make your imports feel effortless by using asset views.

Key takeaways: By the end of this class, you will be able to:

• Build asset views
• Prepare spreadsheets for import into Collibra
• Execute the export/import process to create assets with characteristics

Prerequisite knowledge required:
Asset governance

Target audience:
Intermediate Data / Business Stewards, Data Governance Manager / Lead

Are you planning on becoming a workflow developer? This course is for you! Join us and learn how create and deploy workflows. While creating workflows, you will practice using variables to capture user inputs with forms. Additionally, we will show you some tips for troubleshooting workflow errors.

Key takeaways: By the end of this class, you will be able to:

• Distinguish between global and resource-based workflows
• Set workflow metadata
• Assign workflow tasks to users
• Create process variables for workflows
• Deploy workflows to Collibra
• Debug workflows

Prerequisite knowledge required:
Workflow management, Designing workflows

Required software:

• None

Target audience:
Intermediate workflow developers

Getting a pulse on the health of your data should take minutes, not months. That’s why we designed Collibra Data Quality (DQ) to automatically profile your data – allowing you to quickly remediate data errors. Take this course and we will walk you through the entire DQ process from building a DQ job to interpreting the results via the data’s profile.

Objectives: By the end of this class, you will be able to:

• Create a DQ Job
• Interpret results from a DQ job
• Using Item Labeling, retrain a DQ model
• Recognize available statistics through Profile Insights

Prerequisite knowledge required:
None

Target audience:
Data steward / Data quality analyst / Data analyst / Data owner / Business analyst / Data scientist / Data quality lead / Data governance lead

Discover the most time-saving elements of Collibra Data Quality (DQ): automatic rule creation. We will walk through how to configure DQ to learn from your data, generate Adaptive Rules (AR), and add sensitive labels to data sets.

Objectives: By the end of this class, you will be able to:

• Recall the purpose and functions of Data Quality’s behavior model
• Configure a behavioral model
• Apply adaptive rules to a data set
• Distinguish the properties of adaptive rules
• Summarize how data classes and data categories support rule discovery
• Activate rule discovery through DQ catalog or DQ jobs
• Create sensitive labels

Prerequisite knowledge required:
None

Target audience:
Data steward / Data quality analyst / Data analyst / Data owner / Business analyst / Data scientist / Data quality lead / Data governance lead