Data Dictionary Mapping
We standardize your data elements to consolidate unstructured information into a single, trustworthy structure — enabling advanced analytics, reporting, and machine learning capabilities to utilize clean, consistent datasets.
In enterprise environments, organizations generate tremendous amounts of structured and unstructured data across many disparate systems. Data dictionary mapping services can help standardize and create consistency between metadata platforms and ensure that data is read the same way, integrated in a uniform manner, and relied upon for vital analytics, compliance reporting, and AI/ML pipelines.
Our services include standardization of Meta-Data schemas, automated or rule-based mapping incorporating machine learning, manual validation and standardization of critical attributes, and cross-platform integration via tools such as Talend, Informatica, SAP, Oracle and SQL-based systems. We use and align business glossaries with compliance standards such as HL7, CDISC, and XBRL, and map these across platforms/interfaces.
We can support you in updating to a new platform, modernizing an old system or even implementing MDM! Statswork produces context-aware and high-quality data structure for all aspects of the business and across platforms, which drives interoperability, faster onboarding of data, smarter decisions, and decreased compliance costs!
Organizations gain from fully utilizing our entire set of Data Dictionary Mapping Services to integrate data across silos and platforms so that organizations and find value as evidenced through rich data integrity, improved data governance, and analytical value.
Alignment
Alignment
Schema Alignment
We sync field names, data types, and structural hierarchies across databases and systems so data integrations can proceed smoothly and provide both interoperability and consistent reporting frameworks
Terminology Alignment
We identify and map semantically similar terms across datasets (e.g., patient ID vs. PID) which is especially important in highly regulated fields of healthcare, finance, and scientific research, as no two fields or forms are identical and lexical consistency leads to regulatory and analytical advantages.
Cross-System Field Mapping
We let you determine how to do a proper one-to-one or many-to-one mapping across systems of different types in support of ETL, data migration, and Lakehouse population while greatly minimizing the chance of duplication, redundancy, and misinterpretation.
Business Glossary Development
We define and standardize the business terminology from across both technical and non-technical teams to create a shared understanding of important data elements to reduce disparity in the translation between IT and business stakeholders.
Regulatory Mapping & Compliance
We connect defining data elements and dictionary fields back to frameworks such as FHIR, HL7, CDISC, ISO 11179, & HIPAA, to facilitate audit-readiness, data traceability, and compliance in highly regulated industries.
Industry Specific Applications
Statswork’s hybrid AI + human oversight model combines the speed of automation with the judgment of experts, making it ideal for complex B2B settings where data heterogeneity, legacy systems, and compliance demands are the norm. We apply this across all industries.
Raising Data Dictionary Mapping to Another Level with Intelligent Automation & Regulatory-Ready Integration
At Statswork, we leverage a deep mix of industry-standard platforms, proprietary automation engines, and semantic technologies to provide powerful, scalable, and regulation-ready data dictionary mapping services.
Talend & Informatica
Enterprise-level ETL services for automated schema mapping, metadata harvesting, transformation pipelines, and lineage tracking
Apache Atlas & Collibra
Data governance and data cataloguing services for metadata management, lineage visualisation, namespace management, and compliance management, across business domains.
SQL & PL/SQL Scripts
Tailor-made scripts for field mapping, data profiling, data validation, and schema alignment across legacy data systems and current data systems.
Custom Python Mapping Engines
Proprietary tools that incorporate NLP, fuzzy logic, and similarity scoring that enable flexible field mapping and pattern analysis for unstructured and semi-structured data.
FHIR/HL7/CDISC Mappings
Schema mappings used for regulatory compliant health informatics and clinical research evaluation to know they are audit-ready.
OWL & RDF Ontologies
Semantic frameworks that facilitate mappings to domain-specific knowledge graphs for ontological consistency and interoperability for AI/ML.
Automated, Scalable & Semantically Driven Data Dictionary Mapping
This AI-powered pipeline is particularly valuable for large organizations managing heterogeneous data schemas, legacy metadata, and compliance-sensitive datasets (e.g., clinical, financial, or institutional).
1. Ontology-Based Mapping
Uses standard domain ontologies (e.g. FHIR, CDISC, ISO 11179) to identify datasets that align existing concepts in surroundings that are regulatory compliant (e.g., publication) and conceptually consistent in meaning
2. Schema Matching Algorithms
Uses rule-based and probabilistic models to auto-identify structural mappings based on field names, data types, and ways the fields are utilized to facilitate integration across systems
3. Natural Language Processing (NLP)
NLP identifies and interprets metadata gleaned from column headers, descriptions, and/or business glossaries to identify semantic equivalences between different datasets.
4. Semantic similarity scoring
Machine learning models can quantify the conceptual similarity of fields irrespective of labels (e.g. Patient_ID and PID), encouraging greater precision comparing datasets with unrelated labelling conventions.
5. Delivery & Ongoing Support
The processed data will be delivered to you according to the agreed timescales and we will continue to support you in your efforts to get value from it (and therefore your AI & ML solutions).
6. Audit Trail
Maintaining a detailed audit trail to ensure traceability and compliance throughout the data processing lifecycle.
All data dictionary mappings at Statswork undergo multiple stages of validation, with a human-in-the-loop (HITL) for review.
"Statswork's data dictionary mapping team helped us harmonize over 20 disparate EDC systems into a unified CDISC-compliant framework. Their domain expertise and attention to detail saved us months of effort and ensured regulatory readiness."
Dr. Aarti Nair,
Director of Clinical Data Management – MedNova Trials
"The semantic accuracy and automated field alignment Statswork delivered were exceptional. Their team combined automation with human expertise to resolve complex mapping issues that we couldn’t solve internally."
James K,
Data Architect – FinGen Analytics
"We needed precise mapping of medical terminologies between SNOMED CT and our proprietary schema for an AI model. Statswork’s meticulous HITL validation process ensured zero data drift and strong model reliability."
Priya Rao,
VP – Healthcare AI Division, BioSynapse
"Statswork’s mapping helped streamline our ETL pipelines across multiple financial reporting platforms. Their documentation was clear, mappings were validated, and the integration with our metadata registry was seamless."
Omar Fahim,
CTO – Agilis Risk SystemsData dictionary mapping is a systematic process of alignments of data fields and metadata definitions across systems or databases. The process is to normalize formats, field names, and structures to allow for seamless integration, interpretation, and governance of the data across systems.
It provides assurance around semantic consistency, regulatory compliance, and data interoperability-critical issues for the high-stakes domains like healthcare, finance, and pharmaceuticals, or machine learning analytics. It enables system migration, MDM, analytics-ready, trusted data pipelines, and ensures data confidentiality.
We target data-heavy and compliance-driven industries, including:
- Healthcare and Clinical Research (CDISC, HL7, FHIR)
- Pharmaceuticals and Life Sciences
- Financial Services and Insurance
- Academic Research Organizations
- AI/ML and Big Data Analytics Provider
- Government and Regulators
We use automation tools (AI/ML, ontology-based mapping) along with human domain expert validation (human-in-the-loop) to ensure precision, traceability, and semantic correctness. Every mapping is versioned, audited, and developed for long-term maintainability.
We seek to ensure our mappings conform to global standards in the industry, including:
- CDISC (SDTM, ADaM)
- HL7 / FHIR for health care data
- ISO 11179 for standards for metadata registry
Enabling compliance with regulations and audit-ready data.
Definitely! We have extensive expertise with complicated schema mappings involving:
- Large-scale enterprise systems
- Different or fragmented datasets
- Legacy and siloed environments
Our automated and scalable pipelines can produce accurate results even with more complex multidimensional data sources.
All our outputs are designed to fully integrate into your ETL, BI, or Data Governance tools. We provide customized outputs to your infrastructure, including:Â
- Excel, CSV, JSON, XML
- SQL scripts
- Metadata repositories (Apache Atlas, Collibra, etc.)
- Customized API-integrated outputs
All our outputs are designed to fully integrate into your ETL, BI, or Data Governance tools.
Definitely. We have:
- maintenance & versioning of mapping
- mapping for schema changes
- regulatory or operational change support
- change impact assessment and documentation updates
