As per recent Gartner reports, around 70% of organizations say poor data quality costs them $10M+ annually and 85% of AI failures are due to governance-related issues. We directly address these challenges with our years of experience and hands-on expertise.
✅ Ensuring unified governance across data, AI models, and GenAI systems.
✅ We have proven expertise across Azure, AWS, GCP, and enterprise MLOps stacks.
✅ Helping business on regulatory compliance, data privacy, and ethical AI.
✅ We deliver AI-powered governance accelerators for data quality, model drift, and bias detection.
✅ Leverage our industry-specific governance blueprints for finance, healthcare, manufacturing, and more.
Data strategy, governance frameworks, tool evaluation, and implementation (Collibra, Informatica, Talend, etc.).
Designing explainability, fairness, and risk management layers over AI/ML pipelines.
End-to-end governance for prompt lifecycle, embedding security controls, usage audits, and hallucination detection.
Implement fine-grained access policies, data masking, and PII identification.
AI/ML-driven monitoring for anomalies and errors in data pipelines.
Auto-generating lineage graphs, tagging sensitive data, and regulatory mapping.
Integrate governance throughout the ML lifecycle: versioning, approval workflows, drift detection, and rollback.
Perform independent audits of AI systems to evaluate compliance, explainability, and fairness.
GDPR/CPRA/HIPAA rule enforcement using AI for record-keeping, PII detection, and subject rights fulfillment.
| Name | Details | Price (SMBs) | Price (Mid-Size Business) | Price (Enterprises) |
|---|---|---|---|---|
| Business Glossary & Metadata Starter Kit | Help teams define common business terms, KPIs, and metadata tags across tools. | $4,000–$8,000 | $8000–$20000 | $20000–$40,000 |
| MDM Health Check | Audit their master data across key domains (Customer, Product, Vendor) for duplicates, inconsistencies, and gaps. | $5,000–$10,000 | $10000–$30000 | $30000–$50,000 |
| Data Catalog PoC (Proof of Concept) | Scope: Set up catalog for 1–2 domains or systems, basic metadata, lineage, training | $5,000–$10,000 | $10000–$30000 | $30000–$50,000 |
| Data Governance Playbook | Scope: Define policies, ownership models, stewardship roles, processes | $5,000–$10,000 | $10000–$30000 | $30000–$50,000 |
ThirdEye Data is a trusted partner of Alation, leveraging its AI-powered data cataloging capabilities to improve data discovery and governance.
We help organizations enhance collaboration, ensure compliance, and enable data-driven decision-making with confidence.
With years of hands-on experience in Informatica, we specialize in setting up automated Master Data Management (MDM) solutions, data quality pipelines, and governance frameworks.
Our solutions enhance data integrity, security, and accessibility across enterprise ecosystems.
With hands on expertise with Collibra, ThirdEye Data helps organizations streamline data governance by implementing robust frameworks for data cataloging, lineage, and compliance.
Our expertise ensures seamless integration, empowering businesses with data democratization and regulatory alignment.
Our expertise with Atlan allows businesses to establish dynamic, agile data governance frameworks. From metadata management to automated lineage tracking, we enable organizations to harness their data assets securely and efficiently.
Though both terms are closely related to enterprise data, they differ in their areas of focus. Data management focuses on storage, organization, and accessibility, whereas data governance ensures compliance, quality, and security.
Absolutely. AI models require high-quality, unbiased, and compliant data to function responsibly and effectively. Most importantly, it is crucial to secure sensitive data from potential leaks, especially since many LLMs are open-source or rely on publicly accessible architectures.
Data governance is a framework of policies, standards, and practices ensuring secure, accurate, and ethical data use.
It is a structured approach to managing AI risks, ensuring fairness, compliance, and ethical AI deployments at enterprise scale.
Data governance manages enterprise data assets, while AI governance focuses on responsible AI model development and usage.
Leverage our years of experience in setting up data governance framework and a suitable platform.
