A data governance framework defines the processes, standards, and guidelines that promote better data quality and democratization. It helps maintain data consistency and completeness while promoting a more collaborative culture centered on data and objective metrics. This results in enhanced decision-making capabilities for all data leaders in the organization. Data governance frameworks also lay down guidelines for ensuring data privacy and compliance with regulations like GDPR, helping promote greater trust with customers and stakeholders.
In this article, you'll learn more about data governance frameworks so you can compare your current approach with best practices. By the end, you'll have a clear understanding of your framework's maturity and will be equipped to ensure success in your organization's data governance.
Examples of data governance frameworks
Choosing the right data governance framework requires careful analysis and evaluation of the pros and cons of each framework with your organization's specific data governance mission, goals, and timelines. Multiple data governance frameworks have been adopted across enterprises, such as:
The DGI framework takes a pragmatic, bottom-up approach that focuses separately on rules, people, and processes. It provides actionable steps and best practices to effectively adopt the data governance framework.
The DAMA DMBOK framework focuses on a more holistic, top-down view of data management and identifies nine different knowledge areas that focus on separate aspects of data governance.
Best practices for data governance frameworks
Once you've chosen your data governance framework, implementation of the framework requires detailed planning and execution. You need to make sure you adopt standard best practices so that the data governance initiative leads to successful business outcomes. The following are some best practices you should consider:
- Go for quick wins: Start with a small project that can be executed quickly to demonstrate the business value of data governance and gain the trust of the stakeholders across various data teams.
- Build a business case: Build a clear business case that demonstrates to leadership the organizational benefits and return on investment in data governance, such as higher revenue and better cross-functional collaboration. The business use cases should align with high-level organizational goals such as increasing revenue, reducing costs, improving customer satisfaction or enhancing operational efficiency. A clearly defined use case and an accompanying cost-benefit analysis are essential to quantify the potential ROI of data governance initiatives. Once cost-benefit analysis is performed, it's also important to identify the resources, stakeholders and capabilities required to implement effective data governance programs.
- Define success criteria: Agree on metrics that can be used to measure the progress and impact of the data governance initiatives over time.
- Build a long-term vision statement: To get initial buy-in from leadership and secure their continued support, you should build a strong vision statement to convince them of the long-term benefits.
- Identify clear roles and responsibilities: With specific data-focused roles and responsibilities like data administrators, data stewards, and data custodians, it becomes easier to execute the implementation of the data governance framework.
- Use frequent communication: It's imperative to regularly communicate the progress of the governance efforts within the relevant team of stakeholders as well as across the company to create organization-wide awareness and alignment.
Data governance maturity and how it relates to your framework
Once you've started rebuilding a data governance framework, you should be aware of the various progressive stages involved in accomplishing a full-fledged and mature data governance framework.
The standard maturity levels in a data governance framework include:
- Level 0: Absent - No awareness of data governance in the organization.
- Level 1: Aware - Initial awareness of data governance and its relevance for fostering a data-driven culture.
- Level 2: Developing - Foundations of a data governance framework are laid with a clear vision and stakeholder alignment.
- Level 3: Defined - A formalized, documented, and structured data governance framework is established across departments.
- Level 4: Managed - A functioning program is in place with data catalog tools, improving data quality and reducing downtime.
- Level 5: Optimized - Mastery of data governance is achieved, leading to trust in data assets, widespread buy-in, and better business outcomes.
The various maturity levels describe the recommended milestones that an organization should strive to achieve to fully embed data governance across all departments. It's important to note that there is no one-size-fits-all maturity model. Depending on your organization's current state of growth, technology stack, regulatory requirements, and existing processes, you should modify the data governance maturity model to suit your needs.
Once you've created a data governance maturity model, you should conduct an audit to evaluate the current governance efforts with respect to the prescribed milestones at your organization's current maturity level. The assessment should review existing data processes, data infrastructure and tools, data owners, and roles and responsibilities. You can obtain qualitative feedback from the relevant stakeholders to identify gaps and areas of improvement via surveys and questionnaires.
The survey can include questions such as:
- Do you have a clearly defined data governance strategy?
- Are the current governance processes meeting expectations and delivering the desired outcomes?
- What is your process to troubleshoot issues related to data quality, data access, and data downtime?
- Are there clear guidelines that define who can access data assets and the level of access that they have?
- Are there any security or compliance issues that are not yet addressed?
A statistical and detailed review of the survey can help you obtain a baseline understanding of the current state of awareness and adoption of data governance guidelines. Armed with this knowledge, you can progress to the next maturity level as prescribed by your chosen governance maturity model.
How to achieve better maturity for your data governance framework
As you're probably aware, implementation of any data governance framework as it progresses through various data governance maturity levels isn't straightforward. However, adopting tools like data catalogs can help streamline the governance initiatives from the very nascent stages to the final stages of maturity of the data governance program.
If you're looking for an AI-powered data catalog, observability, and governance platform that can help you enforce data governance at scale, consider Secoda. With strong adoption in the market, Secoda can help you quickly attain full maturity of your governance initiatives with automated workflows, advanced search and data discovery, and a seamless user interface.
As your organization grows and matures, you need a mature data governance framework to have a comprehensive understanding of your data assets and their quality and effectiveness in making data-driven business decisions. With Secoda, you can leverage automated tools to simplify and enhance your data governance processes as you scale. Secoda can easily integrate with your existing data tools, and you can seamlessly use its powerful data management, data lineage, data sharing, data observability, and data governance functionalities to implement a highly mature data governance framework at scale.
Take action
A recent study highlighted that [60 percent of corporate leaders prioritize data governance](https://www.dataversity.net/data-governance-trends-in-2024/) because of the tangible return on investment. Through clear policies, guidelines, and protocols, data governance frameworks lay down an objective benchmark for organizations to start extracting the full value of their data assets. However, the maturity levels of data governance frameworks vary widely across organizations.
Secoda is an all-in-one, AI-powered governance platform that provides data quality checks, data health insights, and superior search functionality, and it can be effortlessly integrated with modern data tools. If you want to implement a comprehensive data governance framework, book a demo with Secoda today and start extracting the full value of your data.
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