Governance 3 Generally, data governance is a long-term strategic initiative, but data governance can also deliver short-term, tactical benefits. The need for both strategic and tactical approaches to data governance contributes to an organization’s confusion on where to begin. Many organizations have struggled with understanding the need for understanding their data, although data is one of an organization’s critical assets.
There are many definitions of data governance . One that has resonated well with many organizations follows:
Data governance is the practice of organizing and implementing policies, procedures, and standards for the effective use of an organization’s structured / unstructured information assets. Data governance is accomplished through the acts of data stewards, who exercise the careful, responsible management of data, entrusted to them on behalf of others. The data governance framework includes the intersection of and relationship with data quality, meta data management, and master data management for a comprehensive information management strategy administered by data stewards.
It should be noted, owners and stewards are NOT interchangeable, owners have power and control, and stewards manage on behalf of the owner, perhaps without control over the resource/object). Establishing clear data ownership is crucial for improving data quality control and leveraging data as a strategic asset.
Challenges in Recognizing the Need for Data Governance
Many organizations are interested in the concepts of data governance and data stewardship, but when asked if they want to explore how to implement these concepts, they need help to see the need for such a strategic initiative. Implementing data governance programs, which include frameworks and tools designed to assess and improve organizational data management practices, can help establish effective roles and responsibilities within organizations. Management can be more concerned with the immediate problems of project delivery and application problem-solving, rather than thinking about how to prevent future problems with data management, data usage, data protection, and data consolidation. This focus on immediate actions also can obscure the need for understanding the value and meaning of the data, making this critical asset less useful than it should be.
The Strategic Advantage of Data Governance
This concentration on immediate issues presents a challenge to those who see the benefits of strategic planning and the need for using data governance as a foundation for information management. Well governed data would help deliver better information for improved decision-making and can offer a competitive advantage, as well as satisfying multiple user domains’ requirements for commonly defined and managed data. The problem of persuading an organization to adopt a data governance approach within data management lies in the fact that it is not easy to demonstrate how these benefits can be achieved quickly enough to satisfy those within the organization who need to see results promptly. Balancing the tactical needs with the desire for strategic planning can show an organization that paying attention to data governance can be rewarding in many dimensions and can offer results faster than may be expected.
One process improvement method that can show both immediate and strategic results for an approach is maturity modeling (CMM) , which can be tailored for almost any discipline (software process, software integration, project management, data management, governance, etc.).
The Capability Maturity Model (CMM) and its derivatives instantiated the concepts of examining progression toward “maturity” in some discipline. The CMM-oriented models measure how much an organization uses defined processes to manage some activity (once again: software process development, system integration, data management, etc.).
Maturity Model Common Points and Benefits
Maturity modeling is the general technique that the CMM and all related models employ. Some common points in maturity modeling are:
Different organizations tend to adopt the different parts of a method in a similar progression, leading to the development of a scale that assesses the organization’s current maturity.
Mapping an organization’s current position on the scale gives an indication of its maturity in the method.
The maturity level can show an organization the most appropriate next steps, and give an indication of the benefits of improving its maturity.
Maturity levels are incremental; an organization cannot progress from Level 1 to Level 3 without having satisfied the components of Level 2.
Maturity can be delineated into key areas, and each organization can be more mature in one or more areas than in others at the same level.
What is Data Governance Maturity?
Definition of Data Governance Maturity
Data governance maturity refers to the stage an organization has reached in implementing and adopting data governance initiatives. It measures the organization’s ability to manage, access, and innovate using data assets effectively. Data governance maturity is a critical aspect of an organization’s overall data management strategy, as it directly impacts the quality, security, and value of its data assets. By assessing their data governance maturity, organizations can identify areas for improvement and develop strategies to enhance their data governance capabilities.
Importance of Data Governance Maturity
Achieving data governance maturity is essential for organizations to unlock the full potential of their data assets. It enables them to make informed decisions, improve operational efficiency, and drive business growth. Data governance maturity also helps organizations to mitigate data-related risks, ensure regulatory compliance, and maintain data quality. As organizations progress through different levels of governance maturity, they can better manage their data, leading to more reliable and actionable insights that support strategic objectives.
Benefits of Achieving Data Governance Maturity
The benefits of achieving data governance maturity are numerous. Some of the key benefits include:
Improved Data Quality and Accuracy : Ensuring that data is accurate, complete, and reliable, which enhances decision-making processes.Enhanced Data Security and Compliance : Implementing robust data governance practices to protect sensitive information and comply with regulatory requirements.Increased Data Value and Insights : Leveraging high-quality data to generate valuable insights that drive business innovation and growth.Better Decision-Making and Business Outcomes : Using reliable data to make informed decisions that lead to better business outcomes.Improved Operational Efficiency and Reduced Costs : Streamlining data management processes to reduce operational costs and improve efficiency.Enhanced Data Governance and Management Capabilities : Building a strong data governance framework that supports continuous improvement and scalability.
Levels of a Data Governance Maturity Model
The levels of a basic maturity model for data governance might appear as:
Figure 1: CMM Levels A data governance maturity assessment is a critical evaluation tool that helps organizations understand their current state of data maturity compared to a defined maturity model.
Figure 1: CMM Levels
Non-existent. Organization has no data governance approach and no stewardship functions.
Ad hoc. Organization performs occasional, non-standardized stewardship activities but has no formal data governance approach.
Standardized. Organization has implemented some standard data governance practices and has standardized the stewardship role, but has not identified specific stewards in all business areas nor given them formal industry approved training.
Committed. Organization has instituted data governance in all information areas and has identified and trained stewards for all business units.
Consolidated. Data governance and data stewardship are coordinated across the enterprise with measurements reported regularly to all stakeholders.
Strategic. Data governance is used to set, communicate and enforce business and IT information management policy.
Mapping an organization’s current position on the scale helps in evaluating and enhancing organizational data governance practices by assessing data maturity levels, identifying gaps, and formulating improvement plans.
Data Governance Maturity Levels
Data governance maturity levels are a framework used to assess an organization’s data governance capabilities . The most common data governance maturity models include:
Initial : This level is characterized by a lack of formal data governance processes and policies. Organizations at this stage often face significant data quality issues and lack a structured approach to data management.Repeatable : At this level, organizations have established basic data governance processes and policies, but they are not consistently applied. There is some awareness of data governance, but practices are still ad-hoc and reactive.Defined : Organizations at this level have well-defined data governance processes and policies, and they are consistently applied across the organization. There is a clear understanding of data governance roles and responsibilities.Managed : At this level, organizations have established a robust data governance framework, and they are able to measure and manage data governance performance. Data governance practices are integrated into business processes, and there is a focus on continuous improvement.Optimizing : This is the highest level of data governance maturity. Organizations have achieved a high level of data governance excellence and are continuously improving their data governance capabilities. Data governance is fully integrated into the organization’s culture, and there is a proactive approach to managing data assets.
By understanding data governance maturity and its importance, organizations can take the first step towards achieving data governance excellence and unlocking the full potential of their data assets.
Key Data Governance Maturity Models
IBM Data Governance Maturity Model
Developed in 2007, this model assesses progress across 11 core domains, including data risk management, value creation, stewardship, and policy.
It consists of five levels:
Level 1 (Initial): Limited governance, ad-hoc processes
Level 2 (Managed): Basic awareness and some repeatable processes
Level 3 (Defined): Clearer policies and initial stewardship implementation
Level 4 (Quantitatively Managed): Enterprise-wide governance with measurable goals
Level 5 (Optimizing): Data governance fully integrated into business processes
Gartner Enterprise Information Management Model
Introduced in 2008, this model focuses on five major goals:
Data integration across IT portfolio
Unified content
Integrated master data domains
Seamless information flows
Metadata management
The model progresses through six levels from Unaware (Level 0) to Effective (Level 5).
Oracle Data Governance Maturity Model
Features six milestone levels:
Milestone One (None): No formal governance processes
Milestone Two (Initial): Limited IT authority over data
Milestone Three (Managed): Basic stewardship and reactive problem-solving
Milestone Four (Standardized): Cross-functional teams and centralized policies
Milestone Five (Advanced): Enterprise-wide governance structure
Milestone Six (Optimized): Fully integrated governance
Stanford Data Governance Maturity Model
Adapted from IBM’s model, it evaluates six key components across three dimensions.
Components:
Awareness
Formalization
Metadata
Stewardship
Data Quality
Master Data
Dimensions:
People
Policies
Capabilities
Evolution and Integration of Models
Data governance maturity models have evolved significantly since IBM’s pioneering framework. While Oracle and TDWI share a six-level structure emphasizing iterative improvement, the Stanford model builds upon IBM’s foundation by incorporating an enhanced focus on data literacy and culture. The DAMA-DMBOK framework complements these models by providing comprehensive data management best practices.
Implementation Approach
Success in data governance requires:
Initial maturity assessment
Clear milestone definition
Continuous improvement focus
Quantifiable effectiveness metrics
Alignment with organizational needs
Organizations should select and adapt these frameworks based on their specific requirements and industry context, recognizing that each model offers unique strengths in guiding data governance implementation.
Choosing the Right Data Governance Maturity Model
Selecting a suitable data governance maturity model should be based on an organization’s specific needs, industry standards, and data complexity. Consider the following factors:
Define Governance Objectives: Start by clearly outlining data governance goals, as these will guide your choice of maturity model. Objectives may include improving data quality, ensuring compliance, or optimizing data management costs.Assess Cost and Flexibility: Not all models suit every budget. Organizations should balance cost constraints with model flexibility, ensuring it can scale with their data governance program as it evolves.Research Industry Standards: Some models, such as the Gartner data governance maturity model, are widely recognized and may offer best practices tailored to specific industries, helping you benchmark your progress effectively.Account for Unique Challenges: Tailor the maturity model to your organization’s unique challenges. For instance, data integration and quality issues may require special emphasis in the model.Evaluate Data Complexity: Assessing data volume, diversity, and sensitivity is essential in choosing a model. Complex data architecture demands a model capable of handling multi-layered governance needs.Plan for Continuous Improvement: Data governance is an evolving practice. Choose a model that allows for iterative assessments, enabling you to track improvements and adjust your strategy based on lessons learned.
Using a maturity model can assist an organization in understanding data governance, and allows any organization to measure its relative maturity in this important area. Having the process, key areas, process indicators, and methods of a complete data governance model can give an organization focus in developing and continually refining their approach to data governance and data stewardship.
Benefits of Systematic Data Governance Implementation
Assessment using the data governance maturity model can demonstrate the following effects:
For organizations that currently do not engage in data governance, systematically developing and implementing data governance based on a maturity model format reduces risk in projects and delivers higher quality information to users. Data governance can be an effective project enhancement effort for improved data and information quality.
After having reached Level 2 of the data governance maturity scale, data governance can become the basis for proactive management of information in the organization.
As the organization progresses along the data governance maturity continuum, it can be used to define, communicate, and enforce business-driven information management policy.
Benefits of Systematic Data Governance Implementation
Although many organizations struggle with the need for strategic management of information, data governance can be used as a way to address their immediate needs for tactical improvement in data quality and cost reduction. These benefits come as a result of improved data management in an organization, as has discovered through many successful engagements. Data protection, a part of IT governance concerned with the security and privacy of the data stored in information systems is another area of governance that should not be overlooked in developing an enterprise approach to managing data.
Stressing the tactical benefits of data governance does not weaken the strategic view needed for full information management. However, businesses may want to see the benefits of a tactical approach to data governance before they can justify the implementation of a strategic view of data and its governance. Using the data governance maturity model as the benchmark for assessing their current data governance status can give organizations information on what areas should constitute their initial focus for improving data governance practices, whether that focus is tactical or strategic.
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