Organizations are increasingly recognizing their data as a valuable asset that can significantly shape and enhance business outcomes. As organizations continue to navigate digital transformation and the inevitable growth of big data, effective data governance frameworks have become essential to maximizing data’s value throughout its entire lifecycle.
In this guide, we’re taking a close look at the importance of data governance in modern organizations, the key components of an effective data governance framework, and the benefits and challenges associated with data governance.
What is Data Governance?
Data Governance is a set of policies, procedures, processes, and best practices for securing, managing, and deriving value from organizational data.
A data governance framework establishes who may access what data, for what purpose, and through what means. It also establishes roles and responsibilities to help ensure the security and quality of the organization’s data assets.
Effective data governance policies help keep sensitive data secure, protecting the privacy of individuals whose personal or sensitive information may be collected and stored. Data integrity, availability, and usability are also supported by a comprehensive data governance framework.
Why Does Data Governance Matter?
Effective data governance is a competitive advantage that helps organizations ensure the proactive and efficient management of data and informational assets. This competitive advantage is manifested in three ways:
- Generating Valuable Insights – Data governance helps organizations ensure that data is collected, stored, transformed, analyzed, and accessed by the right person and at the right time to generate valuable insights that drive business success.
- Maintaining Data Security & Privacy – Effective data governance helps maintain the security and privacy of sensitive data by implementing policies and controls that restrict access to organizational data.
- Regulatory Compliance – Organizations that collect sensitive or personally identifying information from their customers may be subject to data privacy regulations like the GDPR or industry-specific regulations like HIPAA. An effective data governance framework ensures that organizations have the policies and procedures in place to remain compliant with data security and privacy regulations while still deriving value from their organizational data.
What are the Goals and Benefits of Data Governance?
Implementing a data governance framework usually has several goals, each of which is associated with one or more benefits. Below, we highlight some of the major goals of data governance frameworks, the mechanisms used to achieve those goals, and the target benefits for the organization.
Data Governance Goals:
- Break down data silos within organizational departments
- Control data access and usage
- Improve the quality of data
- Comply with data regulations
- Enterprise data architecture that centralizes transaction processing and combines data from multiple departments
- User-based data access controls, data usage monitoring and auditing, dynamic data masking, and other policies that restrict access and usage of sensitive data
- Policies that ensure the accuracy, completeness, consistency, and integrity of organizational data, and that transform unstructured data into usable data
- Mechanisms may vary depending on the specific regulatory requirements, and may include policies for storing, accessing, and securing data
Data Governance Benefits:
- Increase data visibility and availability across the organization
- Protect data security and privacy
- Develop more accurate insights that drive high-quality business decision-making
- Avoid fines and penalties associated with regulatory noncompliance
Who is Responsible for Data Governance?
A corporate data governance framework assigns roles and responsibilities to various members of the organization, including C-level business executives, IT managers, and data management professionals.
Ownership of the data governance framework is often assigned to a Chief Data Officer (CDO) who bears responsibility for the successes or failures of the organization’s data governance efforts. As part of their role, a CDO might lead the implementation of a data governance framework, establish performance KPIs and a monitoring system for data governance, and report on the overall success and impacts of data governance within the organization.
In some organizations, a data governance manager leads a dedicated team of data professionals who coordinate data governance initiatives, train other business units on data governance policies and procedures, and track KPIs to report on program success. Many organizations choose to form a data governance committee of business leaders and data owners to approve data governance initiatives, assess proposed policy changes, refine procedures, and resolve disputes.
Finally, a data governance framework assigns data stewardship responsibilities to individual employees or managers. Data stewards are tasked with the oversight of specific data sets and the responsibility of ensuring end-user compliance with governance policies associated with that data.
What are the Components of a Data Governance Framework?
While the concept of a data governance framework can be universally defined and consistent, the specific components of such a framework can vary substantially based on the needs and unique circumstances of the implementing organization.
At a high level, data governance frameworks often include the following five main components:
- Business/Corporate Drivers – Business drivers are the underlying reasons why an organization chooses to embrace a data governance framework. They generally imply potential value, such as revenue and/or efficiency, that the organization wishes to unlock.
- Data Governance Strategies – Data governance strategies are the high-level objectives and principles for the governance framework. These objectives frequently emerge from a developed understanding of the business drivers that prompt organizations to organize and monitor data more effectively.
- Data Governance Roles & Policies – Data governance policies establish a governance structure, assign data stewardship roles and responsibilities, assign decision-making rights to specific parties or user groups, and establish systems for escalating issues.
- Data Governance Standards & Processes – Data governance standards and processes are the specific working methods the organization uses to define, standardize, and clean data in a way that supports organization-specific use cases and requirements. These processes include people, partners, processes, and technologies that will be used to execute on the organization’s data governance policies.
- Data Governance Structures and Technologies – Data governance structures and technologies are the hardware and software tools and systems used to support data governance initiatives.
What are the Major Challenges of Effective Data Governance?
Organizations may face significant challenges when implementing a data governance framework for the first time. Many of these challenges reflect the nature of data governance as an enterprise-wide initiative that requires collaboration between stakeholders in multiple departments and levels of leadership.
As you move towards implementing a framework for data governance within your organization, be prepared to address the following challenges on your way to success.
Building a Business Case for Data Governance
In addition to making a strong initial business case, data governance leadership must endeavor to continuously monitor and demonstrate the value of good data governance to the business. Establishing a business case could mean highlighting the negative consequences of noncompliance with data security and privacy laws or highlighting gaps between organizational data usage and potential value-adding use cases.
Balancing Data Accessibility with Privacy & Security
An effective data governance policy must strike a balance between ensuring the security and privacy status of organizational data, and making that same data accessible where and when it is needed to support business decisions and other intended use cases.
Governing Big Data Systems
Organizations today are producing massive volumes of data at faster rates than ever before. While some of this data arrives structured in relational databases, organizations are also generating large volumes of unstructured and semi-structured data, which is often stored in data lakes before being transformed and cleaned for analysis. Governing these big data systems requires specific processes and procedures for many different types of data generated from myriad sources.
Synchronizing IT and Data Governance
In most organizations, IT governance policies exist to manage and regulate access to organizational IT resources, which may include data. Organizations implementing a data governance framework should seek to align their IT and data governance policies.
Selecting Tools and Technologies
A piece of technology or software does not constitute a data governance framework on its own – rather, technology should be used to support the framework with features that streamline or automate compliance at scale.
Data Governance Standards and Best Practices
Despite the challenges associated with implementation, a successful data governance initiative promises significant organizational benefits, such as easier access to data, more timely insights, and the flexibility to more easily adapt data to new use cases within your organization.
Below are 10 best practices that organizations should follow to enable a smooth data governance implementation.
- Start small and set realistic goals.
- Focus on how your data governance initiative supports key business objectives.
- Analyze the current state of your data governance program and create a roadmap for success.
- Clearly establish accountability by defining ownership and stewardship of every data set.
- Strive for data transparency – create an organizational data catalog that allows team members to easily track down the data they need.
- Use metrics and KPIs to evaluate the impact of data governance initiatives and report successes to key stakeholders.
- Encourage communication and collaboration between all stakeholders in data governance.
- Establish a formalized process for dispute resolution between all stakeholders in data governance.
- Use access controls and data masking to hide sensitive data, limiting access to specific roles, situations, and usages.
- Support data governance initiatives with effective use of software and technology.
Leveraging Data Governance Tools & Technology
Immuta integrates directly with existing data platforms like Databricks, Snowflake, Amazon Redshift, Azure Synapse, and more. With Immuta, you’ll be able to centralize and automate key aspects of your data governance framework, such as sensitive data discovery, enterprise data definition and cataloging, data request workflows, and data usage monitoring for auditing or reports.
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