Data management has become a critical challenge for organizations of all sizes. As the volume and complexity of data continue to grow exponentially, many businesses find themselves grappling with:
Inconsistent or poor-quality data
Data silos that prevent a unified view of information across departments
Security vulnerabilities exposing sensitive information
Compliance headaches around increasingly-complex regulations
These pain points pose significant risk to an organization's reputation and bottom line. But, you can counter some of these risks with a robust data governance framework. Your first step is clearly defining policies and procedures for managing data. Let’s explore.
What is a data governance framework?
A data governance framework is a structured method for data asset management that defines an organization's protocols, roles, responsibilities, and standards. Data governance frameworks help ensure that data is stored properly and remains in compliance.
Implementing a data governance framework can align your data management practices with your business goals. A framework also addresses data-related challenges like data silos and sensitive information protection.
A data governance is comprised of the following components:
Roles and responsibilities: Data owners, stewards, and users
Policies and procedures: Data collection, storage, access, and usage policies
Standards and definitions: Common data formats and terminologies
Data quality and security measures: Data discovery and management tools
Benefits of a data governance framework
A data governance framework gives teams a leg up in several ways, including:
Improved data quality and consistency: It sets up the standards for accurate and consistent data.
Enhanced data security and compliance: It protects sensitive data and adheres to regulatory requirements to reduce the risk of breaches and fines.
Increased data accessibility and discoverability: It streamlines data access to make it easier for users to find and use data as it relates to business goals.
Better data-driven decision-making: It provides trustworthy data that supports informed and strategic business decisions.
More efficient data management processes: It optimizes data handling with automation tools to reduce redundancies.
Key components of a data governance framework
In accordance with agile data governance principles, the key components of a data governance framework are:
Policies and standards
Policies and standards form the foundation of a data governance framework by establishing guidelines to keep data consistent and compliant. These policies must be followed as they provide clear directives on how data should be handled, which minimizes risks of data breaches.
Some examples of standard data governance policies include:
Roles and responsibilities
Defining roles and responsibilities is critical to effective data governance. Some of the most basic roles include:
Data stewards: Who manage data integrity and compliance within the organization
Data owners: Who define data governance objectives and approve data management plans
Data custodians: Who handle the technical aspects of data management, like data storage and maintenance
Data governance council: Including senior executives and key stakeholders who provide strategic direction and oversight
Data management processes
Agile principles underscore the importance of iterative processes that build in continuous improvements to the data management process. That iteration applies to processes like:
Data collection: Systematically gathering data from various sources for analysis
Data storage: Saving collected data in databases or other storage systems
Data usage: Application of stored data to inform business decisions
Data sharing: Distribution of data between stakeholders or external entities
Data disposal: Secure and compliant deletion or destruction of data that is no longer needed
Data quality management
Data quality is important for reliable data governance. Techniques like data profiling and validation are used to maintain high data standards for current data. Organizations use tools like data quality software and automated validation systems to perform these techniques.
Data security and privacy
Data security and privacy are major components of data governance. It's essential that a data governance framework builds in compliance with privacy regulations like GDPR and CCPA.
Data architecture
A well-structured data architecture should be flexible, allowing for easy integration of new data sources and emerging technologies. That way, data infrastructure can evolve alongside your organization's changing needs.
Compliance and risk management
Effective compliance and risk management strategies address regulatory requirements and mitigate data-related risks through regular audits and compliance checks.
Data governance metrics and reporting
Measuring the success of data governance initiatives requires clear metrics and reporting mechanisms, such as:
Data quality scores
Compliance rates
Incident response times
Agile principles encourage iterative reviews and adjustments to governance strategies based on these metrics. Regular reporting on these metrics provides insights into the effectiveness of data governance practices and highlights areas for improvement.
Education and Training
To sustain an effective data governance framework, ongoing education and training are needed. They guarantee that all stakeholders understand their roles and responsibilities and the policies and processes in place.
Similarly, continuous education keeps everyone updated on the latest data governance best practices and regulatory changes.
Challenges of Implementing a Data Governance Framework
An organization can face several challenges when implementing or updating the data governance framework. You must keep the following in mind before getting into the implementation phase:
Lack of executive buy-in and resources can hinder the development and implementation of governance initiatives.
Organizations that do not have the latest data governance strategies in place usually have silos where departments operate independently. This makes it difficult to implement standardized data governance practices.
Ambiguities in data ownership can lead to inconsistent data management practices and create problems for governance efforts.
Without an updated framework, organizations can face issues of data siloes and lakes where data is not organized correctly.
Examples of data governance frameworks
DAMA DMBOK (Data Management International Data Management Body of Knowledge)
The DMBOK framework is a complete guide to data management practices. It covers diversified aspects of data governance, such as architecture, modeling, quality, and metadata management.
The framework provides best practices and guidelines for data professionals. It implies that we need a strategic approach to data management and gives a detailed roadmap for implementing data governance across an organization.
Data Governance Institute (DGI) Data Governance Framework
The DGI Data Governance Framework is a structured approach to data governance developed by the Data Governance Institute. It focuses on defining roles, responsibilities, and processes to manage data as an essential enterprise asset. It also provides a detailed methodology for developing data governance policies and managing data quality.
The SAS Data Governance Framework
The SAS Data Governance Framework integrates data management practices with analytics to maintain data in its best features. It aligns data governance with business objectives and ensures regulatory compliance by promoting the importance of technology in automating data governance tasks.
McKinsey Data Governance Framework
The McKinsey Data Governance Framework focuses on creating a data-driven culture with clear data governance policies and technology to improve data management. It aligns data governance with business goals to improve data accessibility and maintain data quality.
Effective data governance relies on diverse tools and technologies to automate processes and eliminate human errors. For this purpose, these types of data governance tools are used:
Data cataloging tools like data.world help organize and manage data assets. They provide a data catalog with features like metadata management, search and discovery, data tagging, and lineage tracking.
All these features help automate repetitive data cataloging tasks that are prone to human errors if done manually.
Some other popular tools for data cataloging include:
Atlan
Alation
Collibra
Informatica
Data lineage solutions
Data lineage tools track data's journey through its lifecycle while ensuring transparency and traceability. These solutions help understand data transformations and dependencies.
Examples of such tools are:
Snowflake
Informatica
Talend
Access control software
Access control tools manage permissions according to agile data management policies. They ensure that data is available only to authorized people who can access sensitive data by enforcing security policies and maintaining compliance.
Some popular access control tools include:
Data quality tools maintain data consistency and reliability. They perform various functions to keep data in the correct format without any redundancies or errors. Some popular data quality tools include:
An agile data governance framework
Unlike traditional rigid frameworks, agile data governance uses iterative processes and responsive strategies to handle the dynamic nature of data and business requirements. To bake agile concepts into a data governance framework, organizations must follow these principles:
Iterative development
Collaboration
User-centric approach
Flexibility and adaptability
Continuous improvement
Transparency
data.world is a modern data catalog and collaborative data governance platform that supports building an agile data governance framework with features like automated data lineage, business glossaries, and collaborative workspaces.
To explore how data.world can set up your data governance framework, request a demo today.
