A Customer Journey

Enterprise Data Governance


Executive Summary

Babcock Canada is continually innovating their services to provide the latest technologies and capabilities to their customers. Their large initiative to modernize their applications to AWS and build a centralized data lake was their first step forward in being able to provide advanced analytics and artificial intelligence with their services. With this effort, Babcock Canada wanted to take advantage of the migration effort to build and execute a solid Data Governance Framework. Effectively ensuring that their data is consistent, trustworthy and secure as they leap forward into the cloud. A critical step for their organization as they face data privacy regulations with the Government of Canada and rely more and more on data analytics to help optimize operations and drive business decision-making.

The Challenge

Data Governance Framework: Babcock Canada didn’t have a prior Data Governance Framework to leverage and build from. A new Data Governance Framework needed to be developed with their organization and capacity to deliver in mind.

Data Governance Plan: Our next challenge was creating a Data Governance Plan that could be seamlessly executed as Babcock Canada modernizes their application and build a centralized data lake.

Program Effectiveness and Sustainability: Data Governance is an ongoing effort which needs to be monitored and measured. The Data Governance Framework required methods to measure program effectiveness and sustainability.

The Solution

Goals and Objectives

On onset, eight primary goals and objectives were established for Data Governance. These goals guide data governance activities and implementation of data governance within the organization.


Establish the necessary foundation and organization structure required to support enterprise-wide data governance.


Enhance security to protect enterprise-wide data assets.


Achieve compliance with external mandates.


Oversee efforts to provide acceptable quality data that is accurate.


Facilitate cross organizational collaboration, data sharing, and integration.


Prioritize efforts to address data gaps and needs.


Encourage creative and innovative solutions to data needs.


Improve data utilization and ease of access.

Success Measures

The data governance framework states that success measures will be defined as governance is implemented as Babcock Canada migrates to the cloud. Success measures will determine that progress is being made toward achieving goals and objectives. Performance measures are designated to each objective and will ensure the accountability of a team or individual to produce the desired outcome.

Data Governance Framework

Babcock Canada’s Data Governance Framework consists of four main components, people, policies, process and technology. All four components are necessary for Babcock Canada to achieve a holistic, pragmatic data governance approach.

People: Defined councils, groups and roles that make up data governance decision-making bodies.

Policies, Standards and Regulations: This includes the various policies, directives, guidelines and standards for data management. As both an input and output of business processes, it also includes definitions or a business glossary, and an enterprise data model.

Process and Procedures: Data is affected by business processes that control data definition, management and quality. This also includes procedures by which data is integrated in existing frameworks, and how data issues are prioritized and resolved.

Technology: As the enabler of business processes, managing technology, and utilizing its power to automate processes is an important part of data governance.

Data Governance with AWS

AWS Glue Catalog

A persistent metadata store and contains information about your data such as format, structure, size, data types, data fields, row count, and more. You can register databases and tables within the AWS Glue Data Catalog, which is integrated with many native AWS services.

AWS Glue DataBrew

Gives a visual interface to prepare, profile data, and track lineage in a repeatable, automated fashion. AWS Glue DataBrew can also be used for data quality automation and alerts.

Redshift Spectrum

With Amazon Redshift Spectrum, you can build a modern data architecture, to seamlessly extend your data warehouse to your data lake and read all data – data in your data warehouse, and data in your data lake – without creating multiple copies of data.

AWS CloudTrail

CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of the Babcock AWS account. With CloudTrail, Babcock can log, continuously monitor, and retain account activity related to actions across the AWS operational infrastructure.

AWS CloudWatch

Primary collector of log, event, metrics and alarm data, enabling detailed monitoring of most AWS services.

AWS Redshift Audit Logging

For auditing and monitoring all transactions within the data warehouse.


Data Lake Repository. Bucket access policies to manage access control the data lake.

AWS LakeFormation

Allows you to grant and revoke permissions on databases, tables, and column catalog objects created on S3 data lake.

Results and Benefits

Data Governance as part of an enterprise data management approach, has a positive effect on the quality of enterprise data. Babcock Canada are in better control of their data, structurally increasing the quality. Improved data quality will have a considerable effect on Babcock Canada’s efficiency of business processes and the business performance, but also positively increasing compliance with the regulator, the Government of Canada.



Cloud Consulting Services
Advisory Services
Project Management


AWS Glue
AWS LakeFormation
AWS Glue Catalog
AWS Glue DataBrew
AWS Redshift
AWS Redshift Spectrum
AWS Config
AWS CloudTrail


03/2023 - 04/2023