Ready to dive into the lake?
lakeFS is currently only
available on desktop.

For an optimal experience, provide your email below and one of our lifeguards will send you a link to start swimming in the lake!

lakeFS Community

Data Quality vs Data Governance: How Are They Different?

Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

February 20, 2024

Data quality and data governance refer to different aspects of organizational data management methods, but they’re not mutually exclusive. They may help your company enhance its bottom line by delivering improved insight into corporate assets, all while generating efficiency and operational gains for greater business agility. 

Keep reading to dive into both data quality and governance, learn the key differences between them, and gain best practices when using them together for the most optimal results.

Data quality vs. data governance: What’s the difference?

The primary difference between data quality and data governance is as follows: 

  • Data quality refers to the accuracy, completeness, reliability, and consistency of data, ensuring that it’s fit for its intended use,
  • Data governance includes the overall management, control, and strategic utilization of data assets within an organization.

Poor data quality jeopardizes the accuracy of insights. Data governance emerges as the sentinel of integrity, working tirelessly to create a harmonious environment in which data is fostered for optimal application, all the while preserving the pillars of security and privacy.

What is data quality?

Data quality refers to the correctness, completeness, consistency, and other aspects of data. Organizations cannot become data-driven unless they have high-quality data that they can trust. 

The lack of trust prevents data consumers from using their data to make meaningful business choices, resulting in inefficiencies, missed opportunities, and, ultimately, financial losses. 

6 dimensions of data quality

Data quality dimensions are data properties that teams can analyze and assess against predefined criteria to determine the level of data quality. 

Measuring data quality dimensions helps to identify data quality issues and determine whether your data is acceptable for its intended purpose.

Even among data quality experts, the critical aspects of quality aren’t universally agreed upon. This generates some concern in the data quality community, and it’s much more confounding for those new to the field and, more importantly, for business stakeholders.

However, these 6 data quality dimensions serve as the North Star for many teams looking to ensure data quality:

  • Accuracy – Checks if the data values are accurate depending on the agreed-upon source of truth.
  • Completeness – The number of usable or full data points in a typical data sample.
  • Timeliness – Checks if data is ready within a specific timeframe.
  • Consistency – Compares data records from two distinct databases.
  • Reliability – Checks how much data meets the appropriate format for any business standard.
  • Uniqueness – Tracks the amount of duplicate data in a dataset.

What is data governance?

Data governance is the process of governing data availability, accessibility, integrity, consumption, and security by using internal data standards and rules. The key objectives here are to break down data silos and provide consumers with data that is consistent and trustworthy. 

This area becomes increasingly important as companies confront new data privacy requirements and begin to depend more on data analytics to optimize operations and drive business decisions.

5 components of data governance

A well-designed data governance program includes a governance team, a steering committee that serves as the governing body, and a group of data stewards. They collaborate to develop standards and rules, as well as the implementation and enforcement tactics that data stewards often employ. 

Data governance framework

A good framework consists of policies, processes, and technological tools. It outlines the mission statement, goals, and how success will be measured, as well as decision-making roles and duties for the program’s many components. 

The governance structure is important because it helps everyone understand how the program will work. On the technology front, teams deploy data governance solutions to automate parts of program management and assist with workflow management, governance policy formation, process documentation, data catalog creation, and other tasks. They can work in tandem with tools for data quality, metadata management, and master data management (MDM).

Data governance policies and standards

A data governance policy specifies how data processing and administration will be carried out to guarantee that data is accurate, consistent, and easily available throughout an organization’s systems. The policy also states who is accountable for data in certain scenarios.

Such guidelines often contain individual rules for data quality, access, security, privacy, and usage, as well as various roles and duties for implementing such policies and monitoring compliance with them. The policy should outline the principles, procedures, and standards that top business and IT leaders believe are critical to ensuring security from both internal and external threats.

Policies usually include points about:

  • Data accuracy – Clean, accurate data sets are the most reliable measure of effective governance. 
  • Data access – This includes role-based access restrictions and the implications of unauthorized data access.
  • Data use – Such policies are intended to ensure that a company observes data privacy policies and doesn’t endanger consumers by exploiting their personal information. 
  • Data integration – Rules for harmonizing data definitions and eliminating data silos to make relevant data available to users throughout an organization.
  • Data security – Policies often address data security and privacy, including end-user data security duties. 

Data stewardship and data owners

Users will continue to create and use data, which, if not properly managed, will become a burden. Compliance infractions may result in huge penalties. That’s why data management success begins with data stewardship.

Stewards guarantee that data consumers:

  • Are accountable for the data under their control.
  • Track the performance of data governance efforts.
  • Identify important business data pieces.
  • Develop and maintain data quality guidelines.
  • Establish data policies and rules.
  • Keep the definitions and terminologies updated.

How do data quality and data governance overlap?

Data governance and data quality standards overlap in several domains, with the most important one being compliance. Consider the healthcare and education industries as examples – they both fall under legislation governing information rights and how data may be accessed and shared (HIPAA, Family Educational Rights, Privacy Act).

A complete data governance strategy should include governing regulations outlining the requirements for managing an organization’s specific data storage, privacy, and security needs. To meet these requirements, the organization needs to build data quality systems that monitor data they’re obligated to protect or submit to a regulatory agency.

At the same time, the data should be checked for completeness, timeliness, correctness, and validity to guarantee regulatory compliance. This is where data quality aspects  – most importantly, improved data quality – align with the governance norm.

Data quality vs. data governance: Key differences

Data governance is a broad concept that includes various components, including areas, such as compliance, privacy, and security.

Data quality, on the other hand, focuses on assessing and improving the quality of data across the six dimensions mentioned above. A comprehensive data quality system defines clear paths for resolving issues, making changes, and providing ongoing monitoring.

The end consumer of the data also influences what constitutes high-quality data. If the data provided isn’t usable, regardless of its correctness and completeness, the data may be termed low quality.

Let’s dive into the key differences between data governance and data quality you should know.

Focus

Data quality standards assess the degree to which data is accurate, dependable, and useful for its intended application. Data governance, on the other hand, is a comprehensive strategy and set of methods for managing data as a valued resource.

Goal

Data quality rules ensure that datasets fulfill the precise requirements for correctness, consistency, and relevance. The purpose of data governance is to establish an organizational structure for data management that builds towards alignment with business objectives, regulatory compliance, and efficient data use.

Scope

Data governance encompasses the roles, responsibilities, procedures, and technologies involved in data management as a whole. Data quality focuses on the characteristics of the data to improve data quality and guarantee that the data is accurate, comprehensive, and useful to its consumers.

Processes

Data quality includes activities such as data cleaning, enrichment, validation, and auditing. Data governance is all about setting regulations, assigning responsibilities such as data stewards, assuring data security, and defining data lineage.

Responsibilities

The key responsibilities of data governance are to:

  • Define data ownership, roles, and responsibilities
  • Establish data access controls
  • Enforce data privacy and security policies.

Data quality entails the following responsibilities:

  • Establish data quality metrics and standards
  • Implement data cleaning, validation, and other quality improvement processes

Activities

Data governance includes tasks such as policy formulation, establishing data ownership and responsibility, data classification, data access restrictions, data retention regulations, and regulatory compliance.

When it comes to data quality, you’ll find a range of data-related tasks such as setting data quality rules, data profiling, cleaning, validation, standardization, monitoring, and the establishment of data quality measurements and standards.

Impact of failure

Poor data quality can result in incorrect decisions, inefficiencies, decreased trust, and financial losses. Poor data governance may lead to legal penalties, security breaches, inefficiencies, and organizational upheaval in the data management space.

Data quality and data governance: Why you need both

While they play different functions, the significance of data quality and data governance cannot be overstated across the following areas:

  • Trustworthiness – Stakeholders, both internal and external, are more inclined to trust data that is well-governed and of high quality.
  • Regulatory compliance – Proper governance enables firms to comply with data-related requirements, whereas data quality guarantees that the information utilized and reported is correct.
  • Efficiency – With both in place, businesses may minimize redundancies, decrease mistakes, and simplify data-related processes.
  • Strategic decision-making – High-quality data combined with a strong governance structure guarantees that choices are made based on trustworthy and accurate information.

Data quality and data governance aren’t mutually exclusive. While data quality focuses on the underlying characteristics of data and assuring its suitability for use, data governance provides a comprehensive framework for ensuring that data is managed, safeguarded, and used appropriately.

How to integrate data quality and data governance for your organization

The data governance standard should include data quality methods and dimensions to guarantee that the data used to make decisions is meaningful. Organizations can connect these two strategies to ensure data quality activities and enhancements align with the data governance standard’s objectives. Ultimately, these two categories should be consistent with the organization’s strategic goal.

Furthermore, the people responsible for and owning the execution of an organization’s data governance standard should be strategically integrated into various areas of the data quality system.

For example, management roles should get regular briefings on data integrity concerns inside a business. They can locate staff members to assist a quality or data quality team in addressing integrity concerns. 

These managers often oversee personnel that enter data into a system and thus have a direct effect on the quality of data at the time of collection. Managers may assist in simplifying processes and standardizing how data enters a system, eventually lowering the incidence of mistakes and improving data quality and validity.

Those in charge of an organization’s information should also engage in data quality improvement activities since they are subject matter experts on the organization’s governance standards. This is especially useful when these improvement activities are focused on compliance.

Managers in the governance system will also guide what constitutes excellent data, as they often get numerous data analyses and summaries. Managers should contribute to the development of how the quality dimensions are implemented in their companies, as well as define what data correctness, completeness, and timeliness imply for their business.

As the data governance framework evolves and standardizes over time, businesses should strive to incorporate quality standards into their data oversight structure to guarantee that the information saved and shared inside their systems is legitimate and can be used to satisfy organizational requirements.

Implementing data quality and data governance with lakeFS

There are plenty of tools on the market that help bring these two areas together in organizations, and some of them are open-source. lakeFS is a data versioning tool that lets teams build CI/CD processes for data with high quality and governance in mind. 

CI/CD is a key part of any data quality framework. Continuous integration and deployment of data are automated operations that rely on the capacity to detect and prevent data errors from cascading into production. Ideally, you should execute data quality tests whenever the situation calls for them. This is where data version control systems, such as lakeFS, may help.

lakeFS provides zero-copy isolation, pre-commit, and pre-merge hooks to facilitate automated data quality checks. The system also works with data quality testing tools, which offer the testing logic outlined above, allowing you to test your data effortlessly and at all critical points.

Illustration showing how pre-merge hooks facilitate automated data quality checks

Prioritizing data governance enables you to maximize the value of data and gain a competitive advantage in a data-driven world. This practical guide, Data Lake Governance at Scale with lakeFS, shows how to build data lake governance at scale with an open-source solution.

Conclusion

In today’s data-driven world, the quality of the data we use has a significant impact on the decisions we make, the insights we derive, and the strategies we develop. 

Poor data quality jeopardizes the accuracy of our insights while also undermining the basic foundations of our business plans. Data governance is there to build an optimal environment in which data is fostered for optimal application while preserving the pillars of security and privacy.

Data quality and data governance are two critical pillars of modern data management, contributing significantly to strategic decision-making, regulatory compliance, operational efficiency, and stakeholder trust. Together, they form a solid basis for enterprises to succeed in the complex terrain of data-driven success.

Git for Data – lakeFS

  • Get Started
    Get Started