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. |
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 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 | 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
Data Governance Operating Model | Roles and Responsibilities Overview
 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.
 Data 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.
 Data Subdomain Lead 
Responsible for a specific subdomain within a domain. Reports to the Data Domain Lead.
 Data 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.
 Data 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.
 Data 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.
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
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
|
|
|
