Data Governance Framework Adoption
Data & AI
Motivation
Implementing a data governance framework ensures that your organization's data is accurate, consistent, secure, and used responsibly. It supports compliance with regulatory requirements, reduces risks associated with data breaches, and enhances decision-making by effective and efficient use of information. A robust data governance framework also fosters a culture of accountability and transparency, enabling better collaboration and trust across the organization.
What we bring
With over 20 years of experience in software and data architectures for many of the world’s largest enterprises, PRODYNA is well-equipped to help you design and establish a comprehensive data governance framework. Our expertise in the area of Data Governance includes:
- Data Governance Standards: Introduction of processes and tooling that helps you accomplish the goal quickly.
- Data Quality Management: Implementing processes to monitor and improve data quality.
- Metadata Management: Ensuring that data is well-documented and easily accessible.
- Data Security and Privacy: Protecting sensitive data and ensuring compliance with data protection regulations.
- Data Stewardship: Establishing roles and responsibilities for data management.
- Change Management: Facilitating organizational change to support data governance initiatives.
What you need
To successfully adopt a data governance framework, your organization will need to provide:
- Current Data Landscape: Information on existing data assets, processes, and systems.
- Actual organizational roles and structure: Details of current organizational structure and relevant roles.
- Stakeholder Engagement: Involvement of key stakeholders across the organization.
What you get
PRODYNA's Data Governance Framework adoption focuses on six essential areas, tailored specifically to your organization's unique needs and existing structures. We work closely with your team throughout the process, ensuring knowledge transfer, smooth implementation, and lasting internal capability. You will receive clearly defined, practical deliverables designed to minimize implementation time, reduce costs, and maximize organizational impact:
Conduct Organizational Analysis
Analysis of the current implementation of data governance:
- Review of the (data) organization and the establishment of associated responsibilities and roles.
- Documentation of existing processes, especially for data lifecycle and data lineage.
- Assessment of currently used systems.
- Understanding of governance awareness among relevant employees.
- Conducting a BSI security audit.
Define Key Results
Derivation of measurable KPIs:
- Definition of measurable governance KPIs based on the results of the analysis.
- Harmonization of KPIs with the previously defined governance objectives and the governance target vision.
Build Governance to Execution Model (GEM)
Adapting the Governance to Execution Model:
Governance Bodies & Roles:
- Defining central governance bodies and roles.
- Establishing the governance council, data owners, and data stewards.
Data Policies & Contracts:
- Creation of comprehensive policies for data quality, data sharing, data access, and data security.
- Preparation of data contracts for the relevant data types and associated metadata.
Ruleset & Processes:
- Definition of rules for data quality, metadata, data sharing, data access, and data security based on defined policies.
- Documentation of processes for data lifecycle, data lineage, and issue resolution.
Data Intelligence Platform Prototyping:
- Definition of essential data domains.
- Creation of a rudimentary data catalog.
- Consolidation and system integration of the catalog and associated pipelines and data platforms.
Communication Model:
- Developing a comprehensive communication plan to empower the organization and employees.
- Defining a training program for key content.
- Designing support channels for employees and data responsibilities.
Dashboard Monitoring:
- Definition of dashboards for monitoring governance KPIs.
- Design of alerting mechanisms for revealing deviations.
Quick facts
- Effort: 8–16 weeks, scope dependent.
- Data Catalog: Customized templates, metadata plans, and clear data diagrams.
- Roles & Structure: Defined roles, responsibilities, and gap-closing actions.
- Policies: Tailored data quality and security docs with KPIs and risk controls.
- Platform Prototype: POC with integrated systems, pipelines, and data catalog focus.
Benefits
- Business Efficiency & Value: Aligns governance with business goals, reducing redundancy and optimizing resources.
- Problem Solving & Alignment: Builds a strong foundation to fix data gaps and align structure with best practices.
- Transparency & Management: Boosts visibility for better data inventory, curation, and metadata control.
- Risk & Security: Identifies and mitigates risks with tailored, priority-based security measures.
Contact us
for more information.
David Wainwright
