Sanity in data quality is key to operational excellence and processes that drive efficiency across different organizational functions.
With the rise of automations powered by Agentic AI, discussions around clean data and an organizations’ data capability are being questioned, and data governance is at the centre of this.
As it stands, ODM SAP is one of the most widely used data governance software solution, mostly, or at least in part, because of its deep integrations with SAP ecosystems, but it’s tricky to solve for some fairly narrow use-cases with the software.
Moreover, some features and master data capabilities are inherently better in some of these master data governance software.
Collibra
Collibra is a bundled solution for data governance with built-in AI that has been around since 2008 with a core value proposition – “Data Intelligence at Scale”. The solution makes it the platform of choice for industries with heavy regulatory, compliance and data stewardship requirements.
This includes Industries into Financial Services & Banking, Lifesciences & Pharma, Retail and Consumer Goods, public services & Telecom.
As far as their customers’ firmographics are concerned, companies with over 5000+ employees that are moving into Snowflake, Databricks or Azure are their typical customers.
The platform is designed to enable a stronger push towards data democratization and cross-functional collaboration.
The software works within hybrid data environments like CRM systems, multiple instances of ERP systems and other data lakes that need metadata harmonization.
Some of the features of Collibra
Centralized Data Catalogue
Collibra offers a business-friendly data catalog that makes it easy for users to discover, understand, and trust data across the enterprise. It connects business and technical users by linking business terms with metadata and lineage.
Automated Data Lineage
The platform automatically maps how data flows across systems and applications, providing full visibility into transformations and dependencies. This ensures transparency, simplifies troubleshooting, and supports compliance initiatives.
Data Quality and Observability
Collibra monitors data quality with rule-based checks and machine learning models. Users can set thresholds, track data health, and receive alerts to proactively resolve issues before they impact reporting or analytics.
Workflow & Stewardship Management
Collibra assigns ownership of data domains, datasets, and processes to clearly defined stewards. Built-in workflows help streamline approvals, issue resolution, and governance activities, ensuring accountability across business units.
Policy & Compliance Management
The platform enables organizations to define, enforce, and monitor policies related to sensitive data such as PII or financial records. Compliance teams can easily map regulations like GDPR or HIPAA to specific data assets for audit readiness.
Cloud & Hybrid Data Integration
Collibra integrates with a wide range of data sources including Snowflake, Databricks, Salesforce, and multiple ERP instances. It supports hybrid and multi-cloud strategies, ensuring governance extends across modern and legacy environments.
AI-Powered Recommendations
Collibra leverages AI to deliver intelligent suggestions on data usage, metadata enrichment, and policy application. This improves adoption of governance practices and helps guide users toward trusted and compliant data.
Some Noteworthy Use Cases of Alation
Financial Data Transparency
Large banks and financial institutions use Collibra to unify their data catalogs across multiple systems. By linking business terms with technical metadata, they ensure consistent financial reporting, reduce reconciliation errors, and meet strict audit requirements.
Regulatory Compliance and Risk Management
Collibra enables compliance teams to classify sensitive data such as PII or financial records, map them to regulations like GDPR, CCPA, or SOX, and automatically monitor for violations. This reduces risk exposure and makes compliance reporting more efficient.
Cross-Functional Data Collaboration
Enterprises with siloed CRM, ERP, and BI systems use Collibra as a single governance layer. Business and technical teams collaborate through shared workflows, glossaries, and lineage, which improves trust and accelerates data-driven decision-making.
Customer Data Management in Retail and CPG
Retailers and consumer goods companies use Collibra to create a unified customer view across loyalty programs, ecommerce platforms, and ERP systems. This improves personalization, reduces duplication, and supports better marketing ROI.
Data Stewardship in Life Sciences
Pharma and biotech companies adopt Collibra to manage clinical trial, regulatory submission, and patient data. With steward assignments and workflow tracking, they ensure data accuracy and compliance with FDA and EMA standards.
Telecom Network and Customer Analytics
Telecom providers use Collibra to govern network performance data and subscriber information spread across multiple systems. By consolidating and standardizing metadata, they improve service analytics, reduce downtime, and ensure regulatory compliance.
Integrity by Verdantis
Integrity is part of Verdantis’ Master Data Management (MDM) Suite that primarily solves for data governance and on-going data quality requirements for asset-intensive industries.
When paired with Armonice, Verdantis MDM Suite’s legacy data normalization software, the platform becomes a full service master data solution.
Integrity primarily solves for master data governance challenges for data domains that are key focus areas for companies in Manufacturing, Oil, Gas & Energy, Mining, Chemicals and Utilities.
Beyond standard master data governance, Integridad also syncs data records across different data domains and synchronizes with different ERP and EAM systems like digital BOMs, work orders and inventory management systems for a holistic integration that enables master data management.
Some of the supported data domains in Integrity includes;
Datos maestros de materiales
Also referred to as “Item Master” in some ERP systems like Oracle; Data pertaining to raw materials, MRO spares & Consumables, Finished Goods, Semi-Finished goods etc can be managed within Integrity and the software offers far superior features in these data domains.
Datos maestros de proveedores
Also referred to as “Supplier Master”; Data pertaining to key suppliers can be managed within Integrity and can be synced with other data domains like Materials, Fixed Asset and Service Master Data as well.
Datos maestros de servicio
Data pertaining to professional services like consulting, facility maintenance, maintenance upkeep and even IT or legal can be managed within Integrity and also synchronized across different data domains
Fixed Asset Master Data
Data pertaining to fixed assets and equipment is also supported within Integrity. Within this module, Integrity also integrates with ERP or EAM systems and extracts digital copies of Bill of Materials synchronizes this information within the materials master data as well.
Integrity is ideal for asset intensive operations and when combined with Verdantis’ MDM suite, solves for end-to-end use cases in Master Data management, especially for production-heavy enterprises.
A full list of some of their customers can be found on their website.
The software integrates with ERP and EAM systems like SAP and Oracle off-the-shelf but thanks to fairly-flexible, built-in APIs, it is compatible with pretty much any enterprise planning system.
Sistemas Stibo MDM
Stibo Systems was founded in 1976 and headquartered in Denmark – it is a leader in master data management, with a special focus on Multi Domain MDM with governance- features spanning Customer, Product, Supplier and Asset Master Data.
Stibo is a cloud-native platform with clients across retail, distribution, wholesale, food & beverages and pharma
Adidas, Home Depot, Sainsbury’s, Kellogg Company and Office Depot are some of their well-known and renowned clients.
STEP by Stibo is the company’s flagship product for master data management that also manages Governance workflows.
The software combines structured data policy enforcement, stewards-led workflows, and rich configuration governance to help enterprises trust and derive value from master data.
Governance is embedded at every level – from entity structure to change control to system metadata – providing transparency, compliance, and continuous improvement.
Some of the features of Stibo System are:
Datos maestros multidominio
Stibo enables organizations to manage multiple domains of master data including product, customer, supplier, location, and asset information.
This integrated approach ensures consistency across business functions and prevents silos. By linking domains together, enterprises gain a 360-degree view of data relationships that drive better decision-making.
Configurable & Flexible Architecture
The platform is built on a cloud-native and highly configurable architecture that adapts to complex enterprise requirements. Users can customize data models, workflows, and validations to fit their business processes without compromising scalability.
This flexibility supports integration with ERP, CRM, ecommerce platforms, and legacy systems to ensure smooth adoption.
Enterprise‑Grade Governance & Stewardship
STEP embeds governance at every stage of the data lifecycle. Data stewards can define roles, approval processes, and audit trails, ensuring accountability across the enterprise.
Built-in governance workflows manage entity structures, changes, and metadata, providing both transparency and compliance with regulatory standards.
AI‑Powered Enhancements
Stibo leverages artificial intelligence and machine learning to automate data matching, deduplication, classification, and enrichment.
Intelligent suggestions improve data quality and reduce manual effort, allowing data stewards to focus on higher-value governance tasks. AI-driven insights also help identify anomalies and ensure continuous improvement.
TIBCO EBX
TIBCO is a unified, multi‑domain Master Data Management (MDM) platform designed to help organizations model, manage, and govern master, reference, and metadata in a single solution
It supports on-premises or cloud deployment and powers data governance, stewardship workflows, integration, and lineage reporting – all in one configurable environment
Unlike some of the other names in this list, TIBCO doesn’t publish a detailed client roster publicly, EBX is known for adoption by large enterprises in regulated and data-intensive sectors, such as Insurance & Telecom, Retail & Consumer Packaged goods, Healthcare & Lifesciences, Financial Services and Industrial operations.
Some of the known data domains where their governance workflows are renowned and adopted widely are:
Customer Master – Where data across CRM, ERP and CDP platforms are unified and governed in a single location for compliance with GDPR, HIPAA and CCPA
Product Master – Where it supports complex Product Information Management (PIM) needs such as product hierarchies, classifications and packaging data
Supplier & Vendor Data – Where EBX platform is deployed to manage supplier profiles, onboarding workflows, risk scoring, certifications, and relationship hierarchies.
As far as data governance goes, these are the core capabilities within TIBCO EBX that can be leveraged for building
Policy Management & Enforcement
Administrators define policies on completeness, validation rules, data standards, and access policies. The system continuously enforces and flags violations in real time
Role-Based Access Control & Stewardship
RBAC ensures users only see authorized domains. Role assignment for stewards is integrated into governance workflows that route tasks through approval cycles with audit tracking
Versioning & Full Audit Trails
Version control enables working on parallel datasets (via dataspace isolation) and supports rollback. All changes – including policy actions and workflow tasks – are logged for traceability
Data Lineage & Reporting
EBX captures and visualizes data lineage across domains, enabling governance dashboards that report on data quality, compliance status, and stewardship metrics
Alation Data Governance
Alation is probably not as popular as some of the other names in the list but the way it solves some data governance challenges is truly unique.
The software sets itself apart from its competitors through a human-centric and embedded data governance model. Unlike traditional top-down governance tools that rely heavily on rigid rules-based workflows and IT ownership, Alation takes a different approach.
The software is built to empower data consumers – analysts, business users, data stewards by embedding governance into the flow of work, making it far more scalable and sustainable.
Below is a breakdown of what makes Alation unique along with some strong use-cases for the software.
Embedded Governance in the Data Experience:
The platform is built keeping in mind that governance should not be separated into a back-office function.
Alation brings policies, data quality info, and stewardship directly into BI tools, search experiences, and dashboards.
For example, while exploring a dataset in Tableau or Power BI, users get policy prompts, trust indicators, or PII warnings in real time – without leaving their tools.
Context-Driven Governance in Action:
Arguably, Alation pioneered the concept of Active Data Governance, where governance policies are enforced through usage nudges, intelligent suggestions and monitoring powered by Agentic AI.
The software takes a fresh approach and recognizes that governance is not just about control but about changing user behaviour, which it does with context-driven guidance rather than hard restrictions.
Best in-class Search & Discovery
Google-like natural language search for datasets, dashboards, policies, glossary terms, and lineage paths
ML-powered popularity scores and usage rankings help surface the most relevant and trusted data assets, reducing duplicate reports or “rogue data marts.”
Some Noteworthy Use Cases of Alation
Trusted Self-Service for Finance Teams
Alation empowers finance analysts to discover and access certified datasets on their own, complete with definitions, usage stats, and lineage. This reduces reliance on IT while ensuring consistency and trust in financial reporting and forecasting.
Regulatory Compliance Made Easy
With built-in machine learning, Alation automatically classifies sensitive data such as PII or PHI and links relevant policies directly to datasets.
This enables compliance teams to enforce GDPR, CCPA, or SOX controls and receive alerts for policy violations or unauthorized access.
Scalable Data Stewardship
Alation simplifies data ownership by assigning stewards to specific domains, datasets, or business areas.
Integrated workflows, dashboards, and task tracking improve accountability and close governance gaps across the organization.
Seamless M&A Data Integration
Post-merger integration is streamlined with Alation’s end-to-end data lineage and impact analysis tools, which clarify how data flows across systems. Glossary alignment and stewardship support resolve semantic conflicts between merging entities, ensuring a unified view.
Intelligent Data Rationalization
By analyzing real-world usage patterns, Alation highlights which datasets are popular, valuable, or underutilized. This helps organizations declutter their data environment, archive low-value assets, and concentrate quality efforts where they’re needed most.
IBM Infosphere
IBM InfoSphere is an enterprise-grade data governance and integration platform designed to support complex, large-scale data environments.
It is part of IBM’s broader Cloud Pak for Data ecosystem and focuses on ensuring data quality, consistency, security, and compliance across heterogeneous systems.
Unlike lightweight or decentralized tools, InfoSphere is designed for mission-critical data governance in sectors like banking, telecom, and healthcare – where lineage, quality, and auditability are non-negotiable.
IBM InfoSphere offers a modular, workflow-based governance approach, integrating data quality, metadata management, lineage, stewardship, and security under one umbrella.
It captures metadata from diverse sources, consolidates it into a central repository (Information Governance Catalog), and connects this to both business users and technical teams via policy management, quality rules, and lineage tracking.
Information Governance Catalogue (IGC)
The Information Governance Catalogue serves as the centralized repository for business glossaries, data policies, classifications, and stewardship activities.
It connects business users with IT by establishing semantic links between business terms and technical metadata.
This improves data transparency and ensures consistency across departments. The catalogue also plays a vital role in supporting compliance and governance initiatives.
Automated Data Lineage & Impact Analysis
IBM InfoSphere automatically captures and visualizes data lineage across complex data pipelines, from source systems to BI dashboards.
Users can trace how data flows, transforms, and impacts downstream reports. This helps teams anticipate risks before changes are made, improving agility and reducing the chance of breaking dependencies.
It also enables data teams to meet audit and compliance requirements with full traceability.
Data Quality & Rule Enforcement
A powerful rule engine allows teams to define and enforce data quality standards—covering profiling, cleansing, and standardization.
Users can monitor data health using scorecards and dashboards, ensuring continuous improvement across domains like finance, customer, and product data.
Built-in workflows help resolve quality issues collaboratively. This ensures that trusted, high-quality data flows into analytics and decision-making processes.
Metadata Management
InfoSphere automatically collects metadata from various systems, including DataStage, DB2, Oracle, SAP, Salesforce, and Snowflake.
It classifies data assets, applies sensitivity tags, and enriches them with annotations and technical lineage.
This unified view of metadata helps organizations maintain control over diverse and distributed data environments. It also supports faster data discovery, integration planning, and regulatory audits.
Role-Based Stewardship
The platform enables assignment of data stewards, custodians, and owners with clearly defined roles and responsibilities. Customizable workflows allow stewards to manage approvals, resolve issues, and monitor data governance KPIs.
Task management tools and stewardship dashboards improve accountability and collaboration across business units. This structured approach ensures no critical data domain is left unmanaged.
Security & Compliance Controls
InfoSphere integrates with IAM systems like LDAP and Active Directory to enforce granular, role-based access control.
It supports data masking, audit logging, and sensitive data classification to protect regulated information.
These features are essential for compliance with SOX, HIPAA, GDPR, and similar standards.
Organizations gain visibility into who accessed what data and when – helping reduce risk and strengthen data governance posture.
Some Noteworthy Use Cases of Alation
Streamlined Regulatory Audits
Financial institutions and healthcare providers use InfoSphere to automatically track lineage and maintain data quality standards, enabling faster preparation for GDPR, HIPAA, and SOX audits with reduced manual effort.
Trusted Reporting Across Business Units
Global enterprises rely on InfoSphere’s centralized governance catalog to align definitions and standards across departments. This ensures that finance, sales, and operations report using consistent, trusted data.
Accelerated Post-Merger Data Integration
During M&A activities, InfoSphere helps organizations reconcile metadata from different ERP and CRM systems, align glossaries, and consolidate sensitive datasets. This reduces the time needed to achieve a unified data view.
Proactive Risk Mitigation in Data Pipelines
With automated lineage and impact analysis, technology teams can test the effects of data model changes before deploying them. This minimizes the risk of breaking downstream applications or reports.
Improved Customer 360 Programs
Retailers and telecoms leverage InfoSphere to integrate data from multiple sources into a single governed repository. Clean, standardized data enables more accurate customer profiling and personalized experiences.
Enterprise-Wide Data Stewardship
Large organizations use InfoSphere’s stewardship workflows to assign accountability for critical domains like product, supplier, and customer data. This reduces governance gaps and improves overall data ownership culture.
Ataccama One
Ataccama ONE is a unified, AI-powered platform for data governance, data quality, master data management (MDM), and data cataloguing – all delivered through a single, modular interface.
It’s designed to help enterprises automate data governance processes at scale, especially across hybrid and multi-cloud environments.
Its uniqueness lies in the deep integration of AI and automation, a fully self-service UI, and its ability to blend data observability, stewardship, and quality management in one seamless platform.
Core Features in Ataccama One
Automatización con IA
Ataccama uses AI to auto-discover datasets, detect anomalies, classify sensitive data, suggest data types, and recommend quality rules. This significantly reduces manual workload for stewards, analysts, and governance teams.
Automation-First Governance
From profiling to rule enforcement to anomaly detection, Ataccama automates key governance steps. This allows organizations to scale governance efforts without scaling teams, and ensures real-time policy adherence.
Hybrid & Cloud Native Flexibility
Designed for hybrid environments, Ataccama ONE supports on-premise, cloud, and multi-cloud data landscapes – making it highly adaptable for modern data architectures using platforms like Snowflake, Azure, AWS, or Google Cloud
Self Service & Data Democratization
A modern, no-code user interface allows business users, not just IT, to manage governance tasks – such as approving policies, validating data quality, and exploring lineage. This self-service capability fosters organization-wide data ownership.
Noteworthy use-cases that make Ataccama one stands out;
Self-Healing Data Pipelines in Hybrid Data Architectures
In environments where data flows from multiple sources (e.g., SAP, Salesforce, Snowflake), Ataccama can detect anomalies or broken records mid-pipeline.
It can automatically correct, cleanse, or quarantine bad data, making data pipelines more resilient and reducing manual remediation.
Master Data Management for Complex Domains
Ataccama ONE supports multi-domain MDM, allowing enterprises to create “golden records” for suppliers, vendors, employees, and financial accounts.
Its built-in survivorship rules, matching algorithms, and stewardship interface enable high-trust, unified master records – critical for procurement, finance, and operations teams.
Real-Time Data Quality Monitoring Across Finance Systems
Ataccama continuously profiles transactional and master data in finance systems (GL, AP, AR, budgeting) to detect errors, inconsistencies, or missing values in real time.
It flags exceptions and routes them to stewards via automated workflows. This is crucial for avoiding reconciliation issues and ensuring data accuracy in financial reporting.
Data Cataloging for Business-Led Analytics
Business users can use Ataccama’s built-in data catalog with semantic search to explore available datasets, view trust scores, and understand data lineage.
Certification workflows and quality scores help analysts choose the most reliable data sources for dashboards and modelling.
Automated Regulatory Compliance & Sensitive Data Detection
The platform uses AI to auto-classify PII, PHI, or financial data and apply appropriate masking or policy tagging.
This supports compliance with GDPR, HIPAA, SOX, and other regulatory mandates – without relying solely on manual tagging.
Enterprise data governance and master data management are no longer optional investments. With growing regulatory pressures, increasing data volumes, and the need for seamless cross-functional collaboration, organizations must look beyond traditional approaches.
While SAP MDG remains a strong choice, there are several alternatives that offer unique strengths across flexibility, scalability, and AI-driven innovation.
The right solution ultimately depends on the complexity of your data landscape, industry-specific requirements, and the maturity of your governance practices.
By carefully evaluating these options, enterprises can build a trusted data foundation that not only ensures compliance but also fuels digital transformation and strategic growth.
