What is data governance?

Data governance explained

Data governance is a collection of processes, roles, policies, standards, and metrics that help ensure the effective and efficient use of information, empowering an organization to achieve its goals.

Why does it matter?

As organizations are transforming digitally, IT leaders are recognizing data as their most critical asset and adopting data-driven strategies to fuel digital transformation. In other words, organizations are using data for informed decision-making to deliver better business outcomes, boost revenue an,d gain a competitive advantage.

However, boosting revenues and getting an advantage takes time, data quality often requires improvement, and many companies struggle to make the transformation into truly data-driven enterprises. Data duplication, inaccuracies, inconsistency, incompleteness, and obsolescence can all affect data quality, with poor data quality costing the U.S. economy trillions yearly. In addition to dealing with poor quality data, many companies lack the optimal data infrastructure, such as a data governance framework that provides a fundamental imperative for the modern data-driven business, and they struggle with using data silos.

Benefits of data governance

Data governance provides a framework that helps enterprises manage their data and offers the following key benefits:

Single source of truth 

Leveraging data governance to create a single source of truth (SSOT) means that the individuals making decisions in your enterprise get their information from the same data sets. Having a centralized source of data also helps improve efficiency and flexibility, and reduce confusion, often by promoting a single data catalog with centralized data definitions, terminology, and metadata.  

Data quality 

Leveraging a data governance framework helps lay the foundation for standards of data accuracy, consistency, and completeness for your enterprise, and helps with producing trustworthy, current, high-quality data that is easy to understand. This data is topically relevant and safe and helps produce accurate and strategic insights.  

Operational efficiency

A data governance framework helps with determining the optimal operating model for your enterprise, including the required level of centralization and decentralization. An optimized operating model can help with creating consistent data management practices and streamlining processes and procedures. Similar to establishing an SSOT, it also helps with defining data ownership and supports everyone in your enterprise accessing the same up-to-date, bona fide, and reliable data.

Compliance

Enterprises can strive to prevent unauthorized access by creating centrally defined policies for accessing and modifying data. Data governance frameworks offer a pragmatic approach for compliance and risk management and help your enterprise’s data practices align with legal rules and industry regulations.  Data governance tools help protect sensitive data with privacy regulations and help support procedures to correctly protect, handle, and generate your data while maintaining compliance. These tools help enterprises to lay guardrails that can prevent data breaches, cyber attacks, and similar disruptions.

Data governance challenges

While implementing a resilient data governance framework offers organizations a medley of benefits, data governance also has a myriad of challenges enterprises may need to overcome on their journey to implementation. The following are examples of data governance challenges: 

Balancing access, visibility, and control

While establishing a single source of truth for your enterprise has numerous benefits, deciding who should and should not have access to certain segments of data is a challenge. The solution for this challenge is often creating a system to determine who can access specific data, when they can access it, and what they can do with the data once they have accessed it.

Another similar data governance challenge is that data is frequently stored on different formats across different hybrid environments or multicloud environments and different formats, providers, and locations. Accessing data housed in different data store types, such as data warehouses and data lakehouses, can also present challenges. Having data distributed and housed in all these areas makes it challenging to monitor and track the data usage and data flows.

Effective data governance can overcome this challenge by understanding where the data is, where it is going, who is accessing the data, and how it is being leveraged. This process often requires coordination with data owners, data stewards, and similar stakeholders. A balance must be found that limits access to sensitive data but maintains availability for those who need it.

Standardization and data architecture

When implementing a data governance program, enterprises must find an appropriate balance between the flexibility they want and the tools and architecture that they have. Developing appropriate data models and architectures for merging and integrating data across your enterprise’s storage systems helps support effective data governance.

Adopting a data catalogue and leveraging it to create a data asset inventory across your enterprise also helps with the challenges of standardization and data architecture. If your enterprise already has an enterprise-wide data asset inventory, then consider having a team create a metadata management process to help support keeping the underlying data up-to-date and relevant.

Aligning stakeholders and sponsorships

You may have to persuade your stakeholders of the value of your data and otherwise convince them to invest in your enterprise’s budget for data governance. Optimal data governance programs commonly leverage a two-level sponsorship system based on executive contributors, such as chief data officers (CDOs), and individual contributors, such as data stewards.

CDOs help support the adoption of data governance policies and provide oversight and accountability for the data governance program across your enterprise.

Data stewards promote policy awareness to consumers and data producers and support enterprise-wide compliance. Chief data officers and data stewards are a boon for any enterprise seeking to establish a data governance that supports compliance, data integrity, and data security.  

Three pillars of data governance 

Three core pillars support the data governance foundation in its processes of maintaining high-quality data. The pillars consist of the following concepts:  

Data stewardship

Data stewardship applies data governance as a practical framework for your enterprise to leverage. Data stewards are responsible for monitoring data assets and supporting the data governance journey. They help with maintaining enterprise-wide compliance and adherence to similar governance regulations and policies.

Documentation and procedures and an integral part of a data stewardship program. Data stewards support creating coherent outlines of all processes and support following procedures by leveraging employee training and incentives.

An effective data stewardship program helps reduce errors, inconsistencies, and costly mistakes. It enforces data policies and prioritizes following compliance and regulations to support data governance and maintain cyber resilience.

Data Integrity

High-quality data allows your enterprise to make decisions knowing that you have the facts. Steps to ensure data integrity should be built into processes and procedures per the data governance model and framework.

Integrating processes and procedures to help support data integrity into data governance frameworks helps your enterprise in getting, maintaining, and managing high-quality data that also helps with achieving your business objectives.

Having data integrity helps your enterprise produce accurate reports with insights that support strategic decision-making. It also helps minimize the risks of incorrect, inconsistent, and stale data, and potential drains on productivity because of bad data. 

Data management

Data management supports the technical elements of the data movement, data storage, and data security for data governance. It helps establish data ownership and denote who is responsible for data integrity. Similarly, data management helps with defining the data’s user access rules and the conditions under which users can access the data. It helps standardize processes for data storage, and how and when the data can be updated and archived.

By integrating efficient data management, your enterprise will benefit from a structured framework that helps oversee your data and maintain data access and organization. 

Styles of data governance

Most enterprises leverage a combination of centralized governance, decentralized governance, and federated governance. Data governance programs work best when they find the optimal balance between these data governance styles, depending on their organization’s requirements.

Centralized data governance

Responsible for policies, mission statements, and tools, centralized data governance relegates the day-to-day actions of data governance over to lines of business.

Federated data governance

Federated data governance helps empower divisions, departments, units, and initiatives to operate optimally for their needs, federated data governance. Federated data governance benefits from having a smaller “centralized” team that can focus on hard-to-solve problems or recurring issues.

Decentralized data governance

Sometimes called self-service data governance, decentralized data governance empowers divisions, departments, units, and initiatives around centralized policies. These different divisions (etc.) do what they need to for their projects and leverage tools and processes from other projects as needed.  

Data governance vs data management

It’s not uncommon for the terms “data governance” and “data management” to be used interchangeably. Although both are an integral part of a data-driven organization, they are separate entities.

Data management is an enterprise's information and data management for secure and structured storage and access. Data management involves collecting, organizing, protecting, and storing data to analyze for business decision-making. Data governance helps address the policies, procedures and standards around that data to help produce superior quality, security and compliance across the organization.

Data governance framework

Data governance offers plenty of measurable benefits to your enterprise in addition to helping it understand the value of its data, manage data risks, and streamline data management processes and costs.

A common understanding of data and improved data quality
Data governance offers a consistent view of data independent of the individual business units that retain flexibility. It helps support data accuracy, completeness, and consistency. This improved data quality helps enterprises to understand, trust, and access data that matters, reducing misuse of data and enabling them to make better and accurate decisions with confidence.

A data map and a 360 degree view
Data governance helps enterprises to get an overview of where their data is located and who has access to it. This capability is required for data integration, and functions similarly to a GPS, improving accessibility and business outcomes. 

Consistent compliance and improved data management
Compliance is a tricky, thorny subject that companies worldwide need to deal with. Data protection laws are constantly changing and evolving with their corresponding technology. In addition to risking data loss or damage from things like cyber attacks, adhering to regulatory frameworks and data protection standards helps to minimize loss and damage, along with any damage to an enterprise’s finances and reputation.

The data driven world is often highly automated, and data governance instills some humanity into it with procedures and best practices, and the concerns and needs beyond traditional data and technology areas. With robust data governance, companies can adhere to such demands of government regulations and protect their mission-critical data.

Data governance and AI models

An artificial intelligence (AI) is only as good as the data that it consumes, and as agentic AIs and generative AIs become increasingly sophisticated, the need for strong data governance grows with them. AI models leveraging incorrect, inaccurate, or inconsistent data are likely to yield similarly flawed insights. Enterprises leveraging these AI models are at risk for reputational damage and regulatory fallout.

Data governance frameworks help enterprises deliver high-quality data to AI and machine learning models, including agentic AI models and generative AI models, and protect their data while maintaining compliance and other relevant policies and regulations. Data governance frameworks maintain data integrity standards and enforce clear AI-driven outcome accountability. Certain data governance tools can also help ensure that customers’ personal data or similarly sensitive customer data is not fed to an AI.

Data governance and your enterprise

Data governance is more than just data protection and control. It is the strategy that allows you to make the most out of your enterprise’s data and drive business results. Elements such as creating a culture that values the importance of data governance and providing realistic and outcome-oriented use cases, engaging the right people to take governance initiatives and upskilling them with proper tools and technologies all help your enterprise to embrace modern data governance policies and processes and make the most of them.

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