Normalization is the important process of restructuring information from a database in accordance with a specific format in order to reduce data redundancy and improve data integrity. Applying classification dictates how that information will be interpreted and used. The process of incorporating both is called ‘Harmonization’.
Now you can employ Melissa Informatics’ solution to connect data in innovative ways. Data models or “ontologies” link data in ways that make connections easier to create, easier to modify for different purposes, more useful for creating “golden records”, and ready for deep machine reasoning.
Melissa’s tools allow the user to pull from multiple records, apply machine reasoning and reference information in order to create golden records – even when original data quality is poor or inconsistent. You can now align your data to different standards for harmonization “out of the box”. Melissa provides rich resources for standards alignment and easy re-alignment to different preferred terms and standards as needed, without refactoring the entire database.
Data Integration for Machine Learning: A Data Harmonization Primer
Nestled between basic, academic insights and advanced, educated foresight – and exhaling the breath of life into machine reasoning from big data, is machine reasoning enabled data harmonization.
Data harmonization with Melissa Informatics delivers improvement of data quality through guided machine reasoning, so data can be used – advanced analytics applied, applications developed - more efficiently, accurately and effectively.
Ontology enabled machine reasoning provides an essential bridge between disparate sources, identifying and correcting all potentially misleading, inaccurate, missing or incomplete terms.
Melissa Informatics' leads in clinical information retrieval, clinical data quality, master data management, and continuous improvement of clinical information systems”
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