5 Popular Data Governance Frameworks in 2024

With the current wave of Big Data, organizations spanning various industries find it challenging to manage and govern their data assets effectively. 

In the said context, a Data Governance framework throws a lifeline to struggling organizations. Backed by essential structures, guidelines, and protocols, these frameworks make it easier to keep data managed, protected, and harnessed in a way that resonates with business goals and objectives. 

Best Data Governance frameworks to leverage this year

Among the plethora of frameworks available, this guide covers the best 5 Data Governance frameworks, throwing light on the best Data Governance best practices. Here’s the complete list of prominent frameworks that stick out for comprehensive approach and widespread adoption.

1. Data Management Body of Knowledge (DMBOK)

The Data Management Body of Knowledge (DMBOK) is designed and managed by DAMA, an acronym for Data Management Association. It covers numerous aspects within Data Governance, such as requirements, definition, business valuation, and guidelines. It actually acts as the backbone of any company that aspires to have Data Governance practices of higher standards.

Key Components:

DMBOK has several key components essential for effective Data Governance:

• Data Governance principles: These principles set out to define a fundamental structure that leads the implementation of Data Governance in the organization. These set of principles highlight the importance of accountability, transparency, and alignment towards a business’s primary objectives. 
• Roles and responsibilities: The DMBOK framework establishes certain roles and responsibilities for effective Data Governance, such as data stewards, data owners, and data custodians. The clear definition and delineation of roles facilitates efficient monitoring and compliance, robust decision-making, and usage of data.
• Policies and procedures: The framework shares guidance for enterprises on developing policies and procedures to govern data during its entire lifecycle. These policies encompass numerous aspects within Data Governance, including Data quality, security, and compliance, while ensuring data is effectively managed at every touchpoint in accordance with organizational standards.
• Data Governance maturity model: The DMBOK contains a maturity model that enables an organization to evaluate its current status of Data Governance and identify the areas within its protocol that require improvement. The ascension based on the maturity model helps companies improve their ability to govern data over time. 

2. Control Objectives for Information and Related Technology (COBIT)

COBIT, developed by ISACA (Information Systems Audit and Control Association), is a globally recognized framework for governing and managing information technology. While primarily focused on IT governance, COBIT includes principles and practices for effective Data Governance.

Key Components:

COBIT encompasses the following key components relevant to Data Governance:

• Alignment with business objectives: COBIT stresses the significance of aligning Data Governance initiatives with business goals and objectives to reap the maximum benefits of IT investments and elevate organizational success. 
• Risk management: The framework provides a strategic roadmap for risk analysis, assessment, and mitigation within Data Governance activities. It advocates the cause of proactive, agile risk management, suggesting that organizations can safeguard mission-critical data assets by preempting and preventing potential threats. 
• Performance measurement: COBIT consists of metrics and indicators that can be used for measuring the performance of the Data Governance processes. By adhering to such metrics, organizations can track their performance with respect to Data Governance and determine ways in which to be more successful.
• Compliance and legal requirements: Apart from implementing legal and regulatory laws, COBIT helps organizations in remaining compliant. Organizations that follow the practices and rules being agreed upon remain free from penalties, regulatory actions, and reputational damage.

3. Data Governance Institute (DGI) Framework

The DGI framework defines Data Governance as an expression of wielding control and authority over matters related to data. It proposes a systematic approach with a thrust on enhancing decision-making, mitigating operational inefficiencies, and minimizing costs - all while establishing fluid procedures and standards. 

Key Components:

The DGI Framework encompasses the following four components:

• Value statement, goals, and metrics: The DGI framework asserts that a Data Governance program should be underpinned by a succinctly pronounced mission and vision statement as well as a value proposition. Organizations that put the DGI framework in use should focus on establishing data targets, which are in sync with the value statement, and define relevant metrics to measure each of them periodically. Goals, in this context, act as a guiding strategy and offer a medium for feedback and growth. 
• Data rules, decision rights and accountability: The DGI framework entails creating Data Governance structures, consisting of policies, definitions, glossaries, and compliance regulations. These rules, as a whole, will represent the fundamentals of a unified Data Governance policy, ensuring precision, feasibility, and compliance. The framework further suggests that organizations must set up decision rights to define the ownership of data, facilitating faster response and resolution times. 
• Controls and standards: The DGI framework encourages organizations to build proper Data standards, covering the acquisition of Data, storage, reuse, and flow of data. Introducing stringent, well-curated controls promises the security and confidentiality of data as well as its stable availability at scale, diminishing the associated risks. 
• People and processes: As per the framework, organizations should prioritize assembling a team to lead Data Governance efforts. Key stakeholders, data stewards, along with a dedicated governance office, should take part in building the Data Governance program. This results in undertaking tasks in collaboration, obtaining a buy-in, and making sure that protocols are put in place for effective Data Management. 

4. The SAS Data Governance Framework

The SAS Data Governance Framework links Data Governance tightly to corporate goals, enabling executive support and buy-in for governance initiatives. The framework espouses a practical approach, catering to every facet of organizational goals while building a Data Governance framework that’s comprehensive and relevant. 

Key components:

• Adherence to business objectives: The SAS Data Governance Framework emphasizes creating Data Governance initiatives in alignment with corporate objectives and challenges. 
• Data Governance Strategy: Comprising Program Objectives, Guiding Principles, Decision-Making Bodies, and Decision Rights, this component forms the foundation of the governance structure.
• Data Stewardship and Management: Data Stewardship involves overseeing data quality, security, and lifecycle management, while Data Management focuses on operational activities like metadata and architecture.
• People, Processes, and Technology: This component emphasizes finding skilled personnel to enact policies efficiently through suitable technological solutions, covering data preparation, monitoring, integration, and visualization.

5. BCG Data Governance Framework

BCG in the BCG Data Governance Framework stands for the Boston Consulting Group. This widely-used framework divides the process of Data Governance into four key components. 

Key Components:

• Data structures: The BCG framework places emphasis on establishing clear, coherent data structures, including data catalogs, models, and flows, ensuring a standardized and consistent set of data definitions at scale. It suggests that organizations should focus on building an exhaustive data glossary with specific definition details to allow seamless Metadata Management and integration. 
• Data Policies: One of the vital attributes of the BCG Framework is data policies, encompassing standards to certify data reliability and security. These policies, as a guiding light, outline principles and regulations for audit trails, Data validation, confidentiality, and compliance requirements. The framework highlights that organizations must put together a clear, transparent batch of policies, lowering risks of data manipulation and breach.  
• Data Tools: BCG advocates the use of proper data tools and resources, eliminating governance challenges. Organizations may begin with basic tools like Excel Spreadsheets, but, as requirements turn complex, they may bring in smarter automation tools to govern efficiently. These advanced tools must be well-equipped to organize data, set parameters for analysis, and drive Data-driven decisions, consequently improving the success of Data Governance initiatives. 
• Data Organization Participants and Target Operating Model (TOM): Within the BCG framework, the grand spotlight falls on Data Organization Participants and the Target Operating Model (TOM) for structured Data Governance roles and models. It advocates an establishment of a DMO (Data Management Office) led by a CDO (Chief Data Officer), which spearheads data stewards, owners, and custodians. The TOM serves as the foundation for Data Governance structures, recommending a tiered approach with the CDO transitioning to the advisory role as governance matures.

Bonus additions to the list

Thought we were finished, but here's more! Let’s explore two standout frameworks that should be on every business’ radar in 2024.

6. PwC Enterprise Data Governance Framework

The PWC Data Governance Framework comprises five key components:

1. Data Governance strategy
2. Enterprise Data Management
3. Data lifecycle management
4. Data stewardship
5. Data Governance enablers

The framework underlines the importance of a Data Governance strategy built after defining business goals and metrics. It emphasizes on Enterprise Data Management and prioritizes building systems to manage metadata, Data Integration, and Data Analytics. 

As part of lifecycle management, the framework recommends that organizations must do the groundwork for streamlining and managing the flow of data until it’s archived or disposed of. Through data stewardship, it takes steps towards effective day-to-day management of data to ensure quality, integrity, and lineage.

The PWC framework encourages organizations to set up a tailored governance solution after identifying gaps in the ecosystem and accessing maturity levels. The approach goes a long way forward in fostering adaptability and enhancing Data Governance at grassroots’ level. 

7. Eckerson Data Governance Framework

The Eckerson Data Governance Framework constitutes six layers and 36 components, including people, processes, technology, objectives, and culture. 

The crucial steps in defining an effective structure start with defining the goals, metrics, and methods of Data Governance as well as the analysis of policies, providing guidance, and engaging sponsors like data owners and business executives. 

Processes are facilitated through automating the creation and management of data quality protocols, whereas technology and culture entail using tools and fostering people-centric attributes for effective governance to prevail respectively. 

Out of all, the Eckerson Data Governance Framework is the most flexible and adaptable, enabling organizations to hand-pick and configure components to fit their specific business environments. 

Top Data Governance frameworks: Important FAQs

As we finish up our 2024 guide on top data governance frameworks, explore these key FAQs tailored to provide our readers with more valuable insights into the subject area.

What is Data Governance?

Data Governance refers to implementing a consolidated set of practices and standards to ensure and enhance the quality, security, and availability of data at scale. The overarching aim of Data Governance is to ensure that data quality remains top-notch for deeper, more meaningful insights and efficient decision-making. 

What are the key benefits of implementing a Data Governance framework?

The benefits of implementing a Data Governance framework are aplenty. By using one, organizations can ensure that data quality is improved, regulatory compliance is enhanced, risks are reduced, accountability levels increase, and the value gained from data assets is maximized.

How can organizations successfully implement a Data Governance framework?

From leadership buy-in to stakeholder engagement, organizations must factor in every possible circumstance and contingency while ensuring the successful implementation of a Data Governance framework. Leaders must assemble the right resources, clearly define roles and responsibilities, and set up workflows for ongoing monitoring and evaluation in an effort to implement a Data Governance framework. 

Is it necessary for small businesses to have Data Governance frameworks?

Yes. Ensuring data quality and reliability is a non-negotiable business requirement. Organizations of all scales and sizes must figure out an ideal Data Governance framework for their specific needs and strive to implement it. As a starting point, they must start with scalable and tailored frameworks. 

What are the emerging trends in Data Governance 2024 and beyond?

Data Governance is an emerging field with a multitude of trends shaping its future. In 2024, we see the integration of AI and machine learning with Data Governance has picked a new momentum. The rise of decentralized Data Governance models is unlocking new opportunities. Moreover, we witness ethical Data usage practices and new regulatory developments gaining more prominence than ever in 2024.