Data governance can often seem complex and overwhelming, but with the right tools and guidance, it doesn't have to be. A structured data governance framework provides the essential roadmap for your journey, highlighting key objectives that enhance your organization's capabilities. In this blog, we'll outline the framework's components, its functions, and how to align it with your business objectives.

In this blog, we cover:

    1. What is a Data Governance Framework? ↵
    2. Why is it Important to Have a Data Governance Framework? ↵
    3. What Type of Data Governance Framework is Right for Your Organization? ↵
    4. Data Governance Framework Examples ↵
    5. What are the Components of a Modern Data Governance Framework? ↵
    6. Best Practices for Utilizing Your Data Governance Framework ↵

What is a Data Governance Framework?

Data governance is vital across the data lifecycle — from creation to retention — encompassing data collection, storage, processing, sharing, and disposal. A data governance framework provides an organized set of policies, practices, and procedures to actively manage your organization’s data assets — bringing accountability to people, processes, and technology.

Why is it Important to Have a Data Governance Framework?

A data governance framework:

  • Prioritizes areas like data quality, security, and compliance.
  • Clarifies roles, responsibilities, policies, and standards for effective data management.
  • Enhances collaboration among stakeholders from various departments to achieve data governance goals.
Visual representation of a data governance framework highlighting data quality, security, compliance, clear roles and responsibilities, and collaboration among stakeholders for effective data management.

A data governance framework enables benefits such as improved data quality, risk mitigation, operational efficiency, and enhanced transparency.

A framework’s benefits include:

  • Improved data quality: By emphasizing data accuracy, organizations bolster decision-making and trust.
  • Risk mitigation: The framework identifies and neutralizes risks, safeguarding sensitive data.
  • Operational efficiency boost: Clear roles and processes streamline data activities, removing silos and standardizing data practices.
  • Enhanced transparency: Defined roles promote a transparent, accountable culture, uplifting data integrity and trust.

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What Type of Data Governance Framework is Right for Your Organization?

Choosing the right data governance framework is critical; it needs to align with your organization’s size, needs, risk profile, urgency, and capabilities.

Data Governance Framework Examples

Comparison chart of traditional and modern data governance framework examples, highlighting their key characteristics.

A side-by-side look at traditional vs. modern data governance frameworks.

There are several options, including but not limited to, the Data Governance Institute (DGI) framework and the Data Management Body of Knowledge (DAMA DMBOK) framework — an industry standard for data management. Additionally, some consulting firms and software companies create proprietary frameworks based on their expertise.

Data governance frameworks can be broadly classified into traditional and modern types:

1.) Traditional data governance frameworks such as the DAMA DMBOK provide a structured approach to data governance. They are designed to offer:

  • Well-defined processes and clear roles.
  • Well-documented policies.
  • A focus on consistency, reliability, and compliance.
  • An all-encompassing view of data governance.

2.) Modern data governance frameworks are often developed by consulting firms or software companies and cater to the evolving data landscape, including the integration of AI and emerging technologies. They feature:

  • Agility and adaptability.
  • Flexibility in data management.
  • Agile and iterative approaches for quick adjustments and integration of new data sources.
  • Emphasis on collaboration and cross-functional teamwork to align with strategic objectives.

While traditional frameworks like DAMA DMBOK offer a solid foundation, modern frameworks cater to the demands of the rapidly evolving data landscape, leveraging advanced technologies, and fostering agility and collaboration.

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What are the Components of a Modern Data Governance Framework?

Diagram illustrating the five components of a modern data governance framework: program management, quality standards, analytics management, metadata management, and data security and privacy.

Breaking down the essentials: Key components of a modern data governance framework at a glance.

If you choose to use a modern data governance framework — one that is designed to iterate and scale your data governance program — it should include:

1.) Data Governance Program Management: Robust program management is pivotal in spearheading data governance projects and guaranteeing their fruitful execution. This step lays out the path to establish a team; set out processes, policies, and guidelines; and offer training for data governance endeavors.

Data governance program management should include:

  • Educating and training: Learning activities that enhance the skills and capabilities of everyone involved in data management related activities.
  • Policies and procedures: High-level rules that guide and constrain business behavior for data governance and management.
  • Change management: Activities that foster cultural change, re-engineer organizational structure, and establish new roles and responsibilities for the successful implementation of a data governance program.
  • Organizational readiness assessment: Evaluation of the organization’s capacity to adopt a data governance program.

2.) Data Quality Standards: A vital part of data governance, this step emphasizes the accuracy, consistency, and reliability of data. It requires setting quality guidelines, engaging the business in crafting these guidelines, launching initiatives to address data quality problems, and consistently overseeing data quality.

Data quality standards should include:

  • Data quality rules: Deterministic rules to identify data quality issues provide the organization with a quality standard. These rules should be created by business SMEs and have a lifecycle with specific ownership responsibilities.
  • Current state assessments: An assessment that reports the current state of data quality typically guides the overall data quality program. Assessments are done through various domains, such as by DQ dimension, subject area, database, and department.
  • Data profiling: Modern data quality tools automatically detect data quality issues, and human intervention helps to process false positives and negatives that occur.
  • Issue and resolutions logging: A central log is crucial for tracking and communicating data quality issues through resolution and makes it easy to reference previous resolutions.
  • Performing monitoring: Data quality monitoring tools, which are business rules engines, are used for problem detection, while data profiling tools typically perform assessments.

3.) Data and Analytics Management: This step seeks to curb unchecked analytics methods and uphold governance across data and analytics tasks. It encompasses setting rules and checks for self-serve analytics and data architecture, safeguarding appropriate data storage, utilization, and distribution, thereby preventing a chaotic “wild west” situation.

Data and analytics management includes:

  • Designing and managing data storage: Creating and handling places where data assets can be stored and maintaining inventories to track these storage locations and the assets within them. It also includes ensuring that security principles are in place so that stored data is protected in a way that is legally compliant and proportional to its data security sensitivity level.
  • Organizing architectural and data standards: Arranging data stores within an overall context. Data stores can be dissimilar (e.g., databases, flat files, XML) and organized at different levels. Services and components directly related to data management, such as data movement, data quality checking, and virtualized views, are also part of this process.
  • Agreeing on information sharing and usage: Reaching an agreement between a data producer (or owner) and a data consumer about data sharing. It determines each party’s warranties, responsibilities, communication channels, and outlines consent. This area may overlap with data privacy, contractual data obligations, confidentiality classification, and data lineage, and such overlaps need resolution.
  • Implementing ethical AI: Using AI software in the best interests of our stakeholders, adhering to emerging laws and regulations.
  • Managing master data: Managing the types of data that participate in the enterprise’s transactions.
  • Enabling end-user computing: De facto use of non-corporate systems to run or manage the business, with Excel workbooks on local or departmental drives being a major example.
  • Overseeing third-party data management: Identifying external datasets, determining their usefulness, dealing with contractual matters, setting up ingestion processes to bring the dataset in, and respecting contractual obligations thereafter.

4.) Metadata Management: This aspect of the framework focuses on comprehending and overseeing data assets, their application, and the related metadata. It enables organizations to monitor and regulate data assets, pinpoint data lineage, and handle data access and utilization throughout the organization.

Metadata management includes:

  • Building and maintaining a data dictionary: Keeping database structures in an easily accessible repository.
  • Creating and sustaining a data asset catalog: Preserving database structures in a repository that is easily accessible.
  • Tracking terms in a business glossary: Keeping track of business terms and their definitions, whether these relate to data elements. For example, some business terms, like “hedge fund,” are never individual data elements.
  • Maintaining data lineage: Keeping a list of the tables and columns in a database or equivalent, including definitions and other vital information. This also includes tracking the flow of data from its source to its target.

5.) Data Security and Privacy: This component covers adhering to regulations, shielding against data breaches, and controlling data access permissions. It tackles rules like GDPR, CCPA, HIPAA, and safeguards Personally Identifiable Information (PII), while setting proper data access and usage guidelines.

Data security and privacy practices include:

  • Classifying data: This involves grouping data taxonomically into classes that require specific management and security needs.
  • Reporting regulatory matters: This includes reporting all forms of data management activities as required by law, contractual obligations, or because of legal engagements.
  • Ensuring compliance: This involves making certain that all actions comply with laws, regulations, and policies governing how data is collected, stored, processed, and shared.
  • Maintaining retention and disposition: This refers to preserving data for as long as needed for processing or as required by law or contract, and then permanently disposing of it.
  • Managing access controls and auditing: This includes handling the granting of access to data rather than systems and ensuring responsible use of this access.
  • Securing continuity and recovery: This involves making sure that data governance and processes can function adequately after an adverse event, and that data is always available for business needs.

Best Practices for Utilizing Your Data Governance Framework  

1.) Have an Organizational Data Governance Program in Place: Having a well-defined data governance framework is not enough; you need to put your framework into action and establish a data governance program. Every organization has distinct needs, making it vital to craft a program that’s just right for you.

Download: Enterprise Data Governance Program Guide

Designed for organizations that already have a solid grip on data governance, this guide assists in launching a comprehensive enterprise data governance program. If you have the nod from your executives, a budget spanning multiple years, and a strategy for change management, dive into this guide.

Download Step-by-Step Guide to Iterative Data Governance

If your organization aims to evolve its data governance step by step, this guide is your go-to. Leverage this manual to shape a data governance strategy that’s not only functional and sustainable but also aligns seamlessly with your current business landscape.

2.) Align Your Framework with Business Objectives: The framework isn’t just an IT endeavor. It should be closely aligned with your organization’s broader business goals and data strategy. This means engaging stakeholders across all functions, ensuring they understand the value of data governance and are committed to its success. Use your framework as a guide to prioritize based on strategic objectives, pain points, risk factors, regulatory requirements, and readiness for change.

Why a data strategy without data governance is a recipe for failure

3.) Know that Your Framework Needs to be Flexible: Flexibility is paramount. Your framework should be agile enough to adapt to emerging technologies, regulations, and business needs. This ensures that the framework remains relevant and continues to provide value over time.

4.) Be Committed to Continuous Monitoring and Improvement: Implementation is not a one-time event. Continuously monitoring the effectiveness of your framework and seeking areas of improvement will ensure that it remains effective, efficient, and valuable.

5.) Get Your Stakeholders Engaged: Ensure that all relevant parties — from top management to the operational teams — are on board. Their buy-in, understanding, and commitment are critical for the successful implementation and sustainability of your data governance framework.

By adhering to these best practices, you can adapt your chosen framework to meet specific needs and achieve the benefits of effective data governance.

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Julia Liceaga Julia is an analytics consultant based out of Chicago but is currently enjoying the perks of remote work in Arizona. She guides our clients' Tableau usage into a catalyst for data-driven action and helps design strategic data roadmaps. She is also a co-lead of the Tableau practice at Analytics8. Outside of work, Julia enjoys spending time hiking with friends, exercising, and doing DIY home renovation projects.
Jenna O'Jea Jenna is an analytics consultant based out of our Raleigh office. She delivers impactful Tableau solutions and works closely with clients in a way that enables them to become savvy developers and end users. She also helps lead the Tableau practice at Analytics8. Outside of work, Jenna enjoys spending time with her son, traveling, and live music.
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