Data
Data Quality: Build Trust in Your Data Before Migration and Beyond
A structured approach to assess, enhance, and future-proof your data for compliance, migration, and operational excellence
In the life sciences industry, data quality isn't just a best practice - it's a regulatory necessity. Poor data quality can lead to misinformation, costly rework, compliance risks, and inefficiencies across business functions. Whether you're generating reports or preparing for a system migration, the impact of unreliable data is tangible and expensive.
With increasing demands for digitalization, companies are moving from legacy systems to modern platforms. However, this transition often highlights hidden challenges in data structure, consistency, and completeness. Migrating unclean data can compromise the value of your new system from day one.
That’s where NNIT’s Data Quality Services come in. Our offering helps organizations assess, enhance, and future-proof their data to ensure successful migrations, streamlined operations, and confident decision-making.
Cloud-Native. AI-Driven. Ready for the Future.
Built entirely on cloud infrastructure, our solution taps into powerful computational and analytical capabilities. By leveraging AI and machine learning, we offer advanced data profiling and natural language processing (NLP) to analyze both structured data and unstructured documents — making it easier to spot issues, trends, duplicates, and inconsistencies at scale.
Data Profiling: Know Your Starting Point
Our data profiling module reveals the current state of your data quality — a critical first step toward building a structured, consistent, and compliant data landscape. Using a flexible profiling tool that supports multiple data sources, we benchmark your data against custom metrics as well as authoritative standards like RMS and GINAS.
The output is a detailed analysis with actionable recommendations for where and how to improve, helping you prioritize your efforts before any enhancement or migration begins.
Data Enhancement: Clean and Prepare with Confidence
Based on the results of profiling, our Data Enhancement process uses an automated transformation engine to elevate the quality of your data. We offer two enhancement tracks:
1. Standardized Working Model
This model uses a structured engine to perform rule-based cleaning and enhancement activities — whether manual, automated, or intelligent — resulting in validated load files aligned with your target system’s data model.
2. AI Document Data Intelligence
When working with documents that lack structured metadata - a common challenge during migrations - our AI engine extracts and enhances key information. This significantly reduces the manual effort typically required to identify, validate, and prepare documentation, ensuring greater accuracy and operational readiness.
Our five-step enhancement process includes:
Action Planning: Based on profiling insights.
Approach Selection: Tailored to your data and transformation needs.
Rule Definition: Using both business and technical knowledge.
Effort Monitoring: Continuously tracked through profiling outputs.
Load File Generation: Cleaned data delivered in migration-ready formats.
Migration & Interface Readiness: Structured Data for Modern Systems
We prepare your data for two key use cases:
Stable Operations: Improve the quality of live production data for better reporting and compliance.
System Migration: Deliver clean, validated data aligned with new data models, ready to be loaded into your future platform.
By implementing continuous Data Quality Monitoring, we empower your teams to make faster, smarter, and more informed decisions - whether your data comes from structured systems or complex documents.
Why NNIT?
With a strong foundation in life sciences and regulatory understanding, NNIT combines cutting-edge cloud, AI, and data capabilities to help you:
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Ensure regulatory compliance
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Improve business agility
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Reduce migration risk
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Eliminate data silos
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Build trust in your digital infrastructure
