Data Maturity Assessment
Data Maturity in Life Sciences: Turning Potential into Real Business Value
Structured Data Maturity Assessment (DMA) tailored for the life sciences industry
In the life sciences industry, data is not simply an operational by-product - it is the foundation for scientific discovery, regulatory compliance, and market delivery. Yet many organizations underestimate the gap between their current data practices and the maturity required to fully leverage data as a strategic asset.
NNIT’s structured Data Maturity Assessment (DMA) is tailored for companies in the life sciences industry - giving a clear, evidence-based view of current practices and the steps needed for impactful improvements. Based on global best practices, it helps organizations shift from reactive data management to a proactive, value-driven approach.
How the assessment works
The Data Maturity Assessment is conducted through a structured survey. It captures organizational practices and perceptions across critical functions, providing a representative view of current capabilities.
With the DMA, your organization will:
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Understand data maturity across NNIT’s five core pillars
Data Operations, Data Capability, Technology Enablers, Data Compliance & Ethics, and Data Culture.
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Receive an overall maturity rating aligned to NNIT’s five maturity levels
Showing exactly where you stand today, and what is needed to reach the next level.
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Get targeted recommendations and a prioritized improvement roadmap
Actionable guidance to improve efficiently.
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Assess foundations for responsible AI adoption
Evaluating if your data practices are fit to support trustworthy, scalable AI.
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Map maturity across internal functions
Revealing strengths, gaps, and opportunities for cross‑functional alignment.
The Imperative: Data as a Strategic Asset
Life sciences operates in a highly regulated, research-driven environment, where success depends on the integrity, accessibility, and reliability of data. Effective data management enables:
Smarter clinical trials – reducing cycle times through access to high-quality, governed data.
Smoother regulatory submissions – minimizing rework, delays, and risk exposure.
Stronger patient outcomes – ensuring safety, efficacy, and trust in therapies.
Despite this, many organizations still operate with fragmented practices, siloed functions, and inconsistent governance - resulting in untapped potential and increased risk.
White paper: Ensure that your critical data is trustworthy
With the rise of technologies like GenAI, advanced analytics, machine learning and Real-World Evidence (RWE), well-organized and reliable data is more important than ever before.
In this white paper, we explain how a Data Maturity Assessment (DMA) designed specifically for life sciences can ensure that your data is trustworthy. It can also improve your data management practices and align them with your business goals.
A Framework Built for Life Sciences
The Data Maturity Assessment provides a comprehensive evaluation of your organization’s data capabilities and how they can enable clinical trials, regulatory submissions, drug safety operations, and overall data-driven decision-making.
The five assessment areas are:
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Data Culture
Leadership accountability and commitment to data‑driven decision-making.
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Data Operations
Processes for disciplined data collection, integration, and stewardship.
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Technology Enablers
Infrastructure ensuring secure, scalable, and reliable data flows.
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Data Capability
Skills and expertise to convert raw data into actionable insights.
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Data Compliance & Ethics
Safeguards for trust, integrity, and regulatory adherence.
AI Readiness: Embedded in the assessment
AI is increasingly relevant across the life sciences value chain — from trial design to regulatory workflows. However, its success depends less on technology alone, and more on a strong foundation of data quality, governance, and trust. As part of the DMA, AI readiness is assessed within the broader maturity framework, exploring:
Foundations – Is the underlying data of sufficient quality and governance to support AI use?
Adoption – Where is AI already in use, and how consistently across functions?
Trust – Do stakeholders have confidence in the outputs generated by AI?
This gives organizations a realistic view of whether they can scale AI use, or whether foundational gaps must be addressed first.
Insights
Cases
Life Sciences, Clinical
Empowering Clinical Trials Digitally
Online Events
Life Sciences, AI, Data, Veeva Solutions
Webinar: Stay Ahead of Veeva Releases - How AI can Streamline your Risk Impact Assessments
Online Events
Life Sciences, Regulatory Affairs
A Practical Guide to IDMP Compliance
Cases
Life Sciences, AI, Data
Biotech strengthens data foundation to scale innovation responsibly
Articles
Life Sciences, Data, Drug Safety
Building Pharmacovigilance That Lasts with Robust Data Governance
Cases
Life Sciences, Clinical, Veeva Solutions
Goodbye Paper, Hello Digital - The TMF Makeover
Articles
Life Sciences, Data, Regulatory Affairs
Navigating the Transition from XEVMPD to PMS: Preparing for IDMP Compliance
Articles
Life Sciences, Drug Safety
Harness the Power of Intelligent Automation in Drug Safety
Articles
Life Sciences, Clinical, Data
Data Enablement: Leverage eTMF for business transformation
From Assessment to Action: Delivering a Roadmap
The value of a maturity assessment lies not in scoring, but in actionable outcomes. The DMA provides a structured roadmap that balances near‑term impact with long‑term transformation:
Quick wins – targeted interventions that improve data practices immediately.
Strategic initiatives – programs to embed sustainable improvements across the enterprise.
This approach ensures organizations transition from ad hoc practices to strategic, proactive data management.
