Data Quality & Cleansing

While there is no shortage of studies and information worldwide on a wide array of medical subjects, there are, however, barriers to gathering and comparing them and a pervasive problem with the quality of the data.

"According the HBR, 3% of company data meets the lowest standard of data quality."

Melissa Informatics brings their legacy of data quality into focus on maintaining the highest standards of quality in mission critical healthcare informatics.

Data cleansing must be built right into the pipeline or bad data has the potential to completely destroy the machine reasoning process by derailing ontologies and confusing semantic linkages.

Melissa has built data quality and cleansing into multiple pieces of the health data pipeline, and even further integrated machine reasoning into our informatics tools to streamline the process and maximize its efficiency during – ‘harmonization’ – or the normalization and classification process.

Products like Druginator and the Sentient Suite leverage not only our experience in general data cleansing but advance the notion with the implementation of harmonization.

Data harmonization methodologies in data quality integrate machine and human intelligence to combine both the specific intended uses of data as well as our best practices in data quality.

By assessing past decisions by humans and learning from them, machine reasoning can use that intelligence to help users effectively work with clean data.

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