Welcome to the General Data Governance Awareness section!
Data Governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets . It establishes clear policies, roles, and processes to maintain data quality, consistency, and compliance. Strong data governance enables better decision-making, improved collaboration, and greater trust in data.
To learn more about our approach and best practices, refer to the Data Governance Handbook 2025 , which provides a comprehensive guide to our Data Governance Model.
Key Concepts
Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycle.
Data Governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.
Data Quality refers to the planning, implementation, and control activities that apply quality management techniques to measure, assess, improve, and ensure the fitness of data for use.
Why is Data Governance Important to Solvay?
Data Governance is the core enabler to become a data-driven organization
Data Governance is the backbone that will drive Solvay to have complete, accurate, secure and timely data, while enabling operational efficiency and becoming more insight-driven.
How are we going to achieve it?
Gaining impactful insights into Solvay’s decision-making it will be only possible by integrating the right skilled people, well-defined processes, precise tools, and strong organizational support to meet business needs.
Data Governance Framework
The use of a DG Framework, composed of five elements, helps to organise and structure methods and concepts around DG and tailor them to the specific engagement.
Guiding Policies & Principles
- Describes the guiding policies and principles for Data Governance within Solvay.
Roles & Responsibilities
- Clear defined roles, responsibilities, organisation structure and committees to enable data governance model to run. Outlines the individuals at Solvay who will direct the application of Data Governance, including the Data Governance Team and Committees.
Processes
- Outlines structured workflows and procedures ensuring the Data Governance initiatives are being implemented and deliver value to Solvay.
Tools & Technology
- Outlines the available tools required for data governance, such as business catalog and the data quality monitoring tool. These enable efficient implementation and management of governance activities.
Governance Metrics
- Outline key performance indicators (KPIs) and success metrics to measure the effectiveness of data governance initiatives.
Data Governance Operating Model
This training provides an overview of Data Management Foundations, explaining what is Data Management, Data Governance and Data Quality.
Key takeaways include:
What is Data Management ?
Development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycle.
What is Data Governance?
- Exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.
What is Data Quality?
- Refers to the planning, implementation, and control activities that apply quality management techniques to measure, assess, improve, and ensure the fitness of data for use.
Data Governance Collaborating Groups
- Data Management Team (Data Governance Lead and Data Governance Team)
- Business (Data Domain Lead, Data Subdomain Lead, Data Owner and Data Steward)
- IT (Data Custodian)
Data Governance Model
- Data Management Steering Committee
- Data Governance Committee
- Data Domain Community