General Data Governance Awareness

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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.

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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

In Data-Driven organizations reliable strategic decisions are based on complete, accurate, secure and timely data.

Data Governance is the backbone that will drive Solvayto have complete, accurate, secure and timely data, while enabling operational efficiency and becoming more insight-driven.

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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 | Federated Model

Data Governance at Solvay operates under a Federated Model. This model applies to Master Data, as well as Transactional and Calculated Data, utilizing existing resources within the organization.

Data Management Team

Set the governance (standards & policies) and monitor the implementation
Define and establishes the Committees, ensuring the operationalization of Data Governance
Run of the governance team, ensure the adherence to the data governance standards and policies, and provide support when needed

Domains

Operationalize data governance & management within their data domain
Responsible to manage their own data
Follow & collaborate on Guidelines and Procedures for Data Management & Governance
Collaborate with the Data Management team to share best practices

Image RemovedGeneral Awareness Training Video This training provides an overview of Data Management Foundations, explaining what isData Management, Data Governance and Data Quality.

Data Governance Operating Model | Roles and Responsibilities Overview
Image Added Data Governance Lead
Responsible for ensuring the effective Data Governance across the organisation and engaging with Management to continuously promote the topic and its value.
Image AddedData Domain Lead
Responsible for orchestrating my data domain, ensuring that data is accurate, accessible, and aligned with the data governance model. Additionally, he/she defines the priorities within the domain, guiding data initiatives to meet organisational objectives.
Image AddedData Subdomain Lead Image Added
Responsible for a specific subdomain within a domain. Reports to the Data Domain Lead.
Image AddedData Owner
Responsible for designing, defining, and improving data requirements, data quality rules, and business rules, ensuring data integrity and consistency across the dataset under her/his oversight.
Image AddedData Steward
Supports the Data Owner by ensuring the implementation of data requirements, enforcing data rules, and resolving identified data quality issues. Additionally, Responsible for proposing and maintaining definitions for business terms and data elements, as well as documenting and maintaining data lineage.
Image AddedData Custodian
Responsible a specific data source within an organisation. He/she applies his/her system knowledge and technical skills to support the technical infrastructure to meet Data Governance requirements.

Additional Resources
Data Governance Handbook 2025
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This document was created to define key activities within the Data Governance Lifecycle. Its purpose is to:
Define the Data Governance Foundations: Outlines what is Data Governance and how it is run across Solvay
Define Roles and Responsibilities: Establishes well-defined roles responsible for effectively managing, maintaining, and safeguarding data. This model promotes accountability by assigning specific individuals to oversee data governance, quality and management initiatives initiatives.
Promote Accountability and Oversight: Ensures designated roles, such as data stewards, are in place to monitor and uphold data governance activities.
Facilitate Cross-Functional Alignment: Creates a unified language and shared understanding of data governance across business areas, fostering collaboration and ensuring that data is used consistently and effectively throughout the organization
General Awareness Training Video
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The purpose of this training video is to:
Explain Data Management Foundations: Explain what is Data Management, Data Governance and Data quality.
Show Data Management Structure at Solvay: Explain how Data Management is structured across Solvay and how our Deployment approach works.
Explain main Roles and Responsibilities: Explain the roles that are responsible for effectively managing, maintaining, and safeguarding data.
Explore available resources: Explore the different readily available resources regarding the Data Management Programme.
Find the document presentation here: Training Pack

Data Roles:
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
Owners and Data
Steward)
IT (Data Custodian)
Stewards
Image AddedThis Wiki page contains the current list of data roles involved in Data Management initiatives. It includes the designated individuals for the following roles:
Data Domain Lead
Data Subdomain Lead
Data Owners
Data Stewards
Data Governance Model
Data Management Steering Committee
Data Governance Committee
Data Domain Community