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Data Governance

Turn Data Chaos into Governed Intelligence

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Trusted data. Compliant AI. Faster decisions.

Axtria's Data Governance practice helps life sciences organizations build trusted, compliant, and AI-ready data foundations from strategy and catalog to marketplace and model governance, empowering faster decisions and measurable business value.

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Inadequate data governance can prove costly

AI models are only as good as the data they're trained on. Without governance, fragmented data pipelines with poor data quality, conflicting definitions, and untracked lineage undermine analytics and regulatory compliance.

Common governance pain points we solve
  1. Siloed & inconsistent data: Biology, clinical, and commercial teams work from disconnected data ecosystems with no shared language.
  2. Weak access & policy controls: Sensitive patient and HCP data moves through pipelines without consistent classification or consent controls.
  3. Poor data quality: Manual curation and untracked rules erode trust in the numbers decision-makers rely on.
  4. GenAI initiatives stuck in non-compliance: Promising GenAI pilots stall before production because outputs can't be explained, traced, or audited.

What every life sciences
data leader is asking right now

Our capabilities
Six pillars of enterprise data governance

Group 1000003698

Elevate
outcomes
with our offerings

End-to-end governance: consulting to operations

Discovery and Governance Consulting

  • Current state assessment & gap analysis
  • Target state definition & governance roadmap
  • Governance product evaluation & recommendation
  • Organization & stewardship design
  • Data policy & council charter definition
  • AI Risk Classification
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Implement and Enable Governance

  • Technology platform enablement (Catalog, Glossary, Lineage, Marketplace)
  • Enterprise taxonomy & classification setup
  • Data quality framework implementation
  • Data sharing policies & use agreements
  • Legacy platform migration & metadata retention
  • AI Agent and Model Registery and Model Cards
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Sustain and Optimize Data Governance

  • Stewardship & catalog enrichment operations
  • Training, enablement & change management
  • DQ monitoring & business rule management
  • Adoption tracking & continuous improvement
  • Agentic AI-powered automation uplift
  • CAPA Integration
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Measurable impact from
AI-assisted governance

reduction-in-metadata
~70%

Reduction in manual metadata curation effort through AI-assisted enrichment

faster-ai-ready
2x

Faster AI-ready data production with governed, auditable pipelines

assets-cataloged
100K+

Assets cataloged at scale without proportional headcount growth

Onboarding
1 month

Faster new-source onboarding reported by a global pharma client

Explore our
success stories

Building a Scalable Data Governance Foundation: The Modernization Journey of a Global Pharma

Building a Scalable Data Governance Foundation: The Modernization Journey of a Global Pharma

Building a Scalable Data Governance Foundation: The Modernization Journey of a Global Pharma Learn more
From Fragmented Data to a Unified Experience

From Fragmented Data to a Unified Experience

From Fragmented Data to a Unified Experience Learn more
Last-Mile Connectivity for AI-Driven Decision-Making

Last-Mile Connectivity for AI-Driven Decision-Making

Last-Mile Connectivity for AI-Driven Decision-Making Learn more

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Frequently asked
questions

What is Data & AI Governance and why does it matter for life sciences?

Data & AI Governance is the framework of policies, processes, and technology that ensures your data is trusted, traceable, and compliant and that AI models built on it are explainable and auditable. For life sciences, where commercial decisions, regulatory submissions, and AI-driven insights all depend on data accuracy, governance is not optional. Without it, organizations risk delayed analytics, compliance failures, and AI programs that stall before reaching production.

Where do we start if we have no governance program in place?

We start with a current state assessment, mapping your data landscape, identifying critical gaps in quality, ownership, and policy, and defining a pragmatic roadmap prioritized by business impact. Most companies begin with a focused MVP: a data strategy, core catalog setup, and a handful of governed data products. From there, we scale.

How does Axtria's approach differ from a standard tool implementation?

We go beyond platform deployment. Our end-to-end model spans strategy, implementation, and ongoing operations including stewardship resourcing, change management, and council facilitation. We also bring life sciences-specific accelerators: pre-built governance frameworks, domain ontologies, and AI agents that automate metadata curation and rule extraction, reducing time-to-value significantly.

How does Data Governance connect to our AI and GenAI initiatives?

Governed data is the prerequisite for trustworthy AI. Without catalogued, lineage-tracked, and quality-scored data, AI models are unverifiable and increasingly, unacceptable to regulators. Our Data & AI Governance practice directly addresses this by ensuring data products fed into AI pipelines are documented, consent-aware, and auditable, while our AI Model Governance capability manages model risk, explainability, and lifecycle compliance.

What does success look like, and how quickly can we see results?

Early wins typically arrive within the first few months like faster data discovery, reduced onboarding time for new data sources, and improved data quality scores. Longer-term, clients report harmonized reporting across functions, reduced IT dependency through self-serve data marketplaces, and the ability to move AI pilots to production with confidence. One global pharma client reduced new source onboarding time by a full month and moved from weekly to daily data refreshes within the first engagement phase.