ANNOUNCEMENT

Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services

Gartner, Magic Quadrant for Custom Software Development Services, By Jaideep Thyagarajan, Ryan McKinney, Nathan Davie, 28 September 2026. Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Hexaware.

Data Governance

What is Data Governance?

Data governance is the discipline of managing the quality, security, availability, and usability of an organization’s data. It involves defining roles, responsibilities, and processes to ensure accountability and ownership of data assets across the enterprise.

The data governance process ensures that data is accurate, consistent, and compliant with regulations, enabling organizations to make informed decisions. It is a strategic program that optimizes how businesses handle data throughout its lifecycle.

Data Management vs. Data Governance

While data management focuses on the technical aspects of storing, organizing, and maintaining data, data governance emphasizes the policies, processes, and principles that ensure data is used responsibly and effectively.

In essence:

Data management is about operational execution (e.g., storage, integration).
Data governance is about oversight and control, ensuring data quality, compliance, and proper usage.

Both are complementary, with data governance practices guiding the implementation of data management strategies.

Components of a Data Governance Framework

A data governance framework is a structured approach that outlines the policies, roles, and processes for managing data effectively. Key components include:

Policies and Standards: Guidelines for data usage and compliance.
Roles and Responsibilities: Defining who owns and manages data.
Processes: Steps for ensuring data quality, security, and accessibility.
Technology and Tools: Platforms and software to support governance efforts.
Metrics and Monitoring: Measuring the success of governance initiatives.

This framework ensures alignment with data governance principles and helps organizations achieve their data governance objectives.

What Are Data Governance Tools?

Data governance tools are software solutions designed to support the implementation of governance policies and processes. These tools help organizations manage data quality, ensure compliance, and streamline workflows.

Examples of capabilities offered by data governance platforms include:

Metadata management
Data cataloguing
Policy enforcement
Data lineage tracking

These tools are essential for enterprise data governance, especially in environments like cloud data governance and big data governance, where data is distributed and complex.