Dawiso methodology

The Dawiso methodology embodies our approach to data governance and philosophy. Its primary purpose is to serve as a starting point for any organization by offering a comprehensive framework for implementing data governance, data discovery, and knowledge management.  It provides context for understanding how structured data governance (discussed in the Importance of data governance article) can be practically applied within your organization.

In this article, we provide context for Dawiso and the underlying concepts, helping align your objectives and demonstrate the value of adopting data governance principles.

The role of data governance

The main aim of data governance is to enable the extraction of knowledge and wisdom from data, which is facilitated through the synergy of

1. Technology
2. People
3. Processes

Data governance allows you to ensure data quality, accessibility, security by defining clear ownership and preserving knowledge and wisdom for your organization.

Why is it vital?

At its core, data governance refers to the holistic management of data availability, usability, integrity, and security, which is achieved thanks to the following principles:

• Data consistency ensures that everyone is on the same page and is looking at the same numbers.
• Data security protects sensitive information and complies with regulations. Governance reduces legal risks and avoids costly fines.
• Reliable data allows companies can improve their decision making processes.
• Operational efficiency reduces inefficiencies by eliminating data redundancies and filling data gaps.
• Responsible data handling boosts customer loyalty.
• Data governance provides clear financial returns.
• Data guidelines enhance team cooperation.
• Quality data insights offer a market edge.

Dawiso’s approach to data governance

Dawiso specializes in data governance, data management, and data knowledge management.

Dawiso provides a single workspace for data cataloging, discovery, and documentation, designed for easy adoption and maximum automation. The user interface is built to support all users, from regular employees to IT professionals.

Dawiso is based on three key principles:

1. Knowledge-centric approach: Dawiso helps users manage and extract insights from data, not just store it.
2. Operational focus: Dawiso aims to streamline processes and improve business outcomes through better data management and collaboration.
3. User-friendly design: The platform is accessible for users of all skill levels, from IT professionals to those new to data management.

Supporting all stages of data governance

We understand that many organizations are in the early stages of data governance, typically scoring between 0 and 3 on the maturity scale. Dawiso provides support for every phase of this process.

Core principles of our methodology

There are various data governance frameworks, such as DAMA, DGI, and COBIT. While these have their strengths, our approach simplifies these models into clear principles and actionable steps.

We emphasize practical implementation, providing guidance through the complexities of data governance and metadata management. In data governance, having the right tools is just one part of the solution. Equally important are the people managing the process and establishing clear guidelines. Without the right tools and people, even advanced tools will result in suboptimal metadata quality.

Foundational principles

Our approach is shaped by our vision, commitments, and philosophy. Based on leading data governance frameworks, we aim to make data governance accessible and straightforward. We guide our clients by these principles:

1. Just Do It: Prioritize action over perfection. It is better to release metadata, definitions, and other elements early and improve them with feedback. This allows faster iteration and refinement.
2. Keep It Simple: Start with basic processes and introduce complexity only when necessary. If you are at a low maturity level, avoid complex systems and workflows until they are essential.
3. Deliver Value Incrementally: Focus on delivering value in small, manageable steps. Early wins build trust and demonstrate progress.
4. Think Politically: Work with early adopters and advocates to build momentum. Avoid attempting large-scale change all at once—take a phased approach for smoother transitions and long-term success.

Our strategy

Dawiso encourages a methodical approach, no matter your data governance maturity level. To find the best starting point for your initiative, consider the following key questions:

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 issues, such as primary bottlenecks and inefficiencies.
3. Assess required effort	What resources, time, and effort are needed to resolve the identified challenges?	Conduct an assessment of the resources (e.g., manpower, technical needs) and risks involved to get a clear view of the effort required. This will also provide a realistic 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. This will help you balance urgency with feasibility.
5. Evaluate organizational fit	How do the prioritized solutions align with broader organizational goals and resources?	Ensure the selected solutions align with overall company goals, organizational readiness, internal support, team culture, and other relevant factors.
6. Target immediate opportunities	Where can we achieve meaningful improvements quickly to demonstrate progress and efficiency?	Target issues that deliver fast, visible results to build momentum and demonstrate early success, boosting motivation and creating a positive perception of the data governance initiative.
7. Build momentum	How can we maintain progress and address more complex challenges in the future?	Use early successes to drive ongoing improvement and tackle more complex challenges over time. This will help your organization establish a long-term commitment and resilience in its data governance journey.

Key components of the Dawiso methodology

We ensure your metadata is high quality, consistent, usable, secure, compliant, and always available. This is supported by:

• A defined structure
• Clear roles and responsibilities
• Policies
• Procedures
• Data quality management
• A strong focus on privacy and security

As a result, we ensure efficient data discovery and a comprehensive data catalog for your organization.

Let’s explore some of these elements in more detail:

1. Roles and responsibilities

Clear ownership and accountability are essential in modern data governance. Usually, there are two two key roles:

• 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. Generally, owners approve changes, while stewards manage the day-to-day activities.

Roles specific to Dawiso:

• Owner: Responsible for the data or informational 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.

Additional roles to consider, like users, sponsors, custodians, and the council, may be essential depending on your organization’s structure. Data governance approaches vary based on factors such as legacy systems, culture, data literacy, and strategy.

2. Information lifecycle

In Dawiso, workflows refer to the validation process for an object, supporting the information lifecycle.

The metadata lifecycle, from creation to final validation, works as follows:

1. Information is introduced into Dawiso as a draft or concept. Metadata can be:
   • Auto-assigned during data ingestion
   • Manually added by stewards
2. The data owner typically validates the information.
3. Once validated, the information is considered reliable and can be used by others.
4. The workflow moves the information from draft to approved status.
5. If updates are needed, the data steward reviews and modifies the information, then sends it back to the data owner for re-validation.

3. Behavior guidelines

Dawiso is built on a foundation of best practices and essential behavioral guidelines. While Dawiso provides the framework, it is your responsibility to define and enforce these policies. You can either embed them into Dawiso (within the enterprise pricing solution) or train users to follow them.

For more guidance, refer to our Resources section, where we offer a comprehensive set of recommendations.

4. Compliance and Security

We ensure the safety of your data assets through

• Robust encryption
• Secure data transit protocols
• Regular security audits

Dawiso complies with key regulations, such as GDPR and CCPA. For more details on specific compliance standards, contact our Customer Success team directly or explore our website.

The recommended adoption journey

1. Create a core implementation team

Create a compact team for Dawiso, ensuring all key roles are covered. In smaller teams, 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. Here are a few common starting points:

Starting point	Challenges	Solution
Business Glossary	Teams use different terms and definitions, causing misunderstandings., Inconsistent terminology leads to flawed analysis or strategy., The absence of a common language hinders collaboration., Onboarding new team members takes longer without a clear reference., Ambiguity in business terms delays decision-making.	Implementing a business glossary helps standardize terms, fostering efficient communication, collaboration, and clear decision-making across the organization.
Reporting catalog	Business users struggle to find available reports on reporting platforms., Reports lack documentation, making them difficult to locate using familiar terms., Users distrust reports due to unclear data sources and timeliness., There is uncertainty about who is responsible for report quality., Navigating multiple reporting platforms is inefficient.	Creating a reporting catalog by scanning metadata and assigning stewards addresses these issues. A descriptive layer maps business terminology to reports, improving findability and trust. AI assistance can further streamline the process.
Analytics table catalog	Teams struggle to find the correct data without a clear list of analytic tables., The absence of a catalog leads to data misuse and duplicate efforts.	A well-organized catalog of analytic tables allows teams to find and use data efficiently and safely, minimizing duplication and errors.

3. Engage Early Adopters

Identify and engage early supporters who understand the value of metadata management. Work with them to determine additional metadata needs, such as:

• Expanding metadata sections by e.g.:
  • Establishing clear data lineage to trace data from source to consumption.
  • Broadening the business glossary to cover more terms and concepts.
  • Integrating operational metadata to provide insights into data usage and flow.
• Specifying metadata to meet requirements like GDPR compliance.

4. Integration into Creation Process

Incorporate Dawiso into the data asset creation process, making it an integral part of data product design.

Resources and support

See the list of all our support channels here.