Interpreting Data Quality in Collibra Platform

Instructors:

  • Joyce Snelders, Manager, Analytics and Cognitive, Deloitte Consulting LLP
  • Darshana Galande, Senior Consultant, Deloitte Consulting LLP

Objectives:

  • Appraise data quality for business purposes
  • Assess relationships between assets and metrics
  • Support accountability through data quality dashboards

Description:

This course is the first in a series based on a retail distribution company’s need to cleanse and monitor the quality of their data. This course will help business users trust their data by determining what data quality is and explaining data quality dimensions such as timeliness, completeness and accuracy. We will highlight the relationships between a business term, a business rule, a data quality rule and a data quality metric. Additionally, we will explain how data quality rules, metrics and dimensions are aggregated and assigned to an asset. To conclude, we will show you examples of a data quality dashboard for a single asset, giving you an idea of how it will look in your organization.

Using Dashboards and Data Lineage to Derive Insights from Data

Instructor: 

Joyce Snelders, Manager, Analytics and Cognitive, Deloitte Consulting LLP

Objectives:

  • Select relationships and add characteristics to data quality rules and dimensions
  • Determine accumulated quality dashboards for multiple assets
  • Assess data impact across assets

Description:

This is the final course based on the use case of the retail distribution company using Collibra to cleanse and monitor the quality of their data. In this course we’re going to create data quality dashboards that are accumulated on a table, or on a certain domain. Following that we will look at the full life cycle of the data quality score through end to end lineage on a traceability diagram. You have created an aggregation path, so now you can see the relationships between your scores, your role itself, and to which columns the rule is applicable. This will enable a business user or a stakeholder to look at the business terms they are responsible for, creating accountability and giving them insight on what the status of the data quality score is. This course will also discuss importing data quality rules and metrics.

Data Quality Configurations and Dashboards

Instructor: 

Joyce Snelders, Manager, Analytics and Cognitive, Deloitte Consulting LLP

Objectives:

  • Interpret data quality components within Collibra Platform
  • Determine an aggregation path in the data quality rule configuration
  • Explain a data quality dashboard for a single asset

Description:

This is the second course in a series based on the use case of a retail distribution company using Collibra to cleanse and monitor the quality of their data. In this course we will do a one-time configuration by asset type in order to integrate and ingest scores into Collibra to view how a data quality score has evolved over time. We will create a community and a rulebook domain, and then configure the data quality rules in Collibra Platform. Once all configurations are complete, we will link the data quality rule to the asset types we have created for it. Lastly, we will discuss how to create a data quality dashboard for a single asset.

 

Data Quality Configuration and Design

Instructor:

Steven Wood, Solution Engineer

Course Objectives:

  • Configure and display Data Quality Results
  • Configure aggregation path in Data Quality Rule
  • Define relationships between assets and metrics

This course provides an introduction to data quality metrics, data quality rules, and aggregation paths. This process will allow you to display the data quality results in the Collibra Platform, from outside tools, such as Informatica Data Quality. We will review how to get to the data quality metric values from a specific object. To do this, we need to configure the aggregation path in a data quality rule. You’re able to display data quality results from any given asset, but you need to define all the relationships between the assets and the metrics.

>