The Dawiso methodology is a structured framework for implementing data governance, data discovery, and knowledge management. It is designed as a starting point for organizations at any stage of their data governance journey, providing context for how to apply the principles covered in Importance of data governance in practice.
Gain a deeper understanding of the Dawiso methodology:
The role of data governance
Data governance enables organizations to extract knowledge and value from data through three interdependent elements: technology, people, and processes. Together, these ensure data quality, accessibility, and security by defining clear ownership and preserving organizational knowledge.
Why data governance matters
Effective data governance delivers measurable benefits:
- Consistent data ensures all teams work from the same numbers, reducing conflicting reports.
- Clear security policies protect sensitive information and help meet regulatory requirements.
- Reliable, well-documented data enables better decisions across the organization.
- Defined ownership eliminates data redundancies and fills knowledge gaps.
- Responsible data handling builds customer trust and supports transparency requirements.
- Clear governance reduces costly errors, regulatory fines, and rework.
- Shared data guidelines improve cross-team collaboration and new-hire onboarding.
- Accurate, discoverable data provides a competitive advantage in analysis and reporting.
Dawiso’s approach to data governance
Dawiso specializes in data governance, data management, and data knowledge management. The platform provides a single workspace for data cataloging, discovery, and documentation, designed for maximum automation. The user interface supports all users, from regular employees to IT professionals.
Three principles shape the Dawiso approach:
- Knowledge-centric approach — Dawiso helps users manage and extract insights from data, not just store it.
- Operational focus — the platform streamlines processes and improves business outcomes through better data management and collaboration.
- User-friendly design — the interface is accessible for users at all skill levels, from data professionals to those new to data governance.
Supporting all maturity stages
Many organizations begin their data governance journey scoring between 0 and 3 on common maturity scales. Dawiso supports every phase of this progression, with capabilities that scale as processes mature.
Core principles of the methodology
There are various data governance frameworks — DAMA, DGI, and COBIT among them. The Dawiso methodology draws on these frameworks and distills them into practical, actionable principles.
Effective data governance requires more than the right tools. The people managing the process and the guidelines they follow are equally critical. Without all three elements — technology, people, and clear processes — even sophisticated tooling produces suboptimal metadata quality.
Four guiding principles
These principles guide the Dawiso methodology regardless of an organization’s maturity level:
- Just do it: Prioritize action over perfection. Releasing metadata, definitions, and other elements early and iterating based on feedback produces faster results than waiting for a complete implementation.
- Keep it simple: Start with basic processes and introduce complexity only when necessary. Organizations at a low maturity level should avoid complex systems and workflows until they become essential.
- Deliver value incrementally: Focus on small, manageable steps that each deliver visible value. Early wins build trust and demonstrate progress to stakeholders.
- Think politically: Work with early adopters and advocates to build momentum. A phased approach produces smoother transitions and longer-term commitment than attempting large-scale change all at once.
Dawiso strategy
Dawiso encourages a methodical approach regardless of data governance maturity level. Use the following questions to identify the right starting point:
| Stage | Key question(s) | Proposed solution |
|---|---|---|
| 1. Set clear objectives | What are the primary data governance goals and desired improvements? | Define clear goals for data governance and use them as the foundation for your initiative. |
| 2. Identify key challenges | What is the most pressing data governance issue currently affecting the organization? | Review your data landscape to identify the main challenges. Make a short list of the most immediate bottlenecks and inefficiencies. |
| 3. Assess required effort | What resources, time, and effort are needed to resolve the identified challenges? | Assess the resources and risks involved to get a realistic view of the effort required and a timeframe for addressing the issues. |
| 4. Set priorities | Which challenges should be addressed first based on urgency and effort required? | Prioritize issues by weighing their significance against the effort needed to resolve them. |
| 5. Evaluate organizational fit | How do the prioritized solutions align with broader organizational goals and resources? | Ensure the selected solutions align with company goals, organizational readiness, internal support, and team culture. |
| 6. Target immediate opportunities | Where can meaningful improvements be achieved quickly to demonstrate progress? | Target issues that deliver fast, visible results to build momentum and create early confidence in the initiative. |
| 7. Build momentum | How can progress be maintained as more complex challenges are addressed? | Use early successes to drive ongoing improvement and establish long-term commitment to data governance. |
Key components of the methodology
Dawiso’s methodology relies on several interconnected components to ensure metadata is high-quality, consistent, usable, secure, and compliant:
- A defined structure
- Clear roles and responsibilities
- Policies and procedures
- Data quality management
- Privacy and security practices
Roles and responsibilities
Clear ownership and accountability are essential to data governance. Two roles form the foundation:
- Owner: responsible for the structure, access, and classification of each element (object, asset, process, model, area, domain, or department).
- Steward: manages the daily operations and upkeep of these elements. Owners typically approve changes; stewards handle day-to-day activities.
In Dawiso, these roles map to:
- Owner: responsible for the data or information asset described by metadata in Dawiso.
- Steward: manages metadata and information, such as maintaining metadata definitions.
- Space Admin: oversees a specified information space within Dawiso.
- Dawiso Admin: manages Dawiso’s configuration settings.
Depending on the organization’s structure, additional roles may be relevant — users, sponsors, custodians, and a data governance council. The right combination depends on factors such as legacy systems, culture, data literacy, and strategy.
Prioritize assigning contributor licenses to owners and stewards — they are the primary roles responsible for managing metadata quality.
Information lifecycle
In Dawiso, workflows support the information lifecycle by defining a validation process for each object. Metadata moves through the following stages:
- Information is introduced into Dawiso as a draft or concept. Metadata can be auto-assigned during data ingestion or manually added by stewards.
- The data owner validates the information.
- Once validated, the information is considered reliable and available for use by others.
- The workflow advances the information from draft to approved status.
- If updates are needed, the data steward reviews and modifies the information, then returns it to the data owner for re-validation.
- Align workflows with your organization’s best practices and policies. Consider limiting metadata editing to data stewards or designated data owners.
- Document these practices in your documentation for easy reference.
- Determine when to introduce metadata and when to involve stewards.
- Prioritize which datasets need immediate attention to avoid overwhelming staff.
Policies and behavioral guidelines
Dawiso is built on a foundation of best practices and behavioral guidelines. While Dawiso provides the framework, each organization is responsible for defining and enforcing its own policies. These can be embedded directly in Dawiso (within the enterprise pricing solution) or communicated through user training.
For guidance, see the Resources section.
Compliance and security
Dawiso protects data assets through:
- Encryption
- Secure data transit protocols
- Regular security audits
Dawiso complies with key regulations including GDPR and CCPA. For details on specific compliance standards, contact your Customer Success Account Manager or visit dawiso.com.
Recommended adoption journey
1. Create a core implementation team
Assemble a compact team that covers all key roles. In smaller organizations, team members may take on multiple roles.
| Role | Description | Role most suitable for |
|---|---|---|
| Admin/Driver/Product Owner | Responsible for Dawiso’s deployment, configuration management, and integration oversight. | Product owners or project managers |
| Dawiso Owner | Oversees platform governance, ensuring alignment with organizational goals and setting policies. | Data governance leads |
| Technical Lead | Manages integration between Dawiso and existing systems, with expertise in data and tool integration. | |
| Dawiso Advocate | Educates users on Dawiso’s features and provides training to help maximize the tool’s value. | |
| Pilot Owner and Steward | Responsible for inputting initial content, maintaining content quality, and providing feedback to improve the platform. |
2. Start with value
Identify areas that offer quick value and align with business objectives. Three common starting points:
| Starting point | Common challenges | Recommended approach |
|---|---|---|
| Business glossary | Inconsistent terminology causes miscommunication and produces unreliable analysis. Teams onboard more slowly without a shared reference. | Implement a shared glossary to standardize terms, align teams, and support clearer decision-making. |
| Reporting catalog | Reports are hard to find, undocumented, and users distrust data without clear ownership or source information. | Scan metadata, assign stewards, and add a business terminology layer to improve findability and trust. AI assistance can accelerate the process. |
| Analytics table catalog | Teams cannot find the right data, leading to misuse and duplicated work. | Create a structured catalog of analytic tables with clear ownership and documentation. |
3. Engage early adopters
Identify and engage early supporters who understand the value of metadata management. Work with them to determine additional metadata requirements, such as:
- Establishing data lineage to trace data from source to consumption.
- Expanding the business glossary to cover more terms and concepts.
- Integrating operational metadata to provide insights into data usage and flow.
- Specifying metadata to meet compliance requirements such as GDPR.
4. Integrate into data asset creation
Incorporate Dawiso into the data asset creation process so that metadata governance is built into how data products are designed from the start, rather than applied retrospectively.
Resources and support
See the full list of support channels.