Data harmonization is the improvement of data quality and utilization through the use of machine learning capabilities. Data harmonization interprets existing characteristics of data and action taken on data and uses that information to transform or suggest subsequent data quality improvements.
Data harmonization takes an approach to data quality that involves both machine analytics and human control. It learns which past decisions made to data are most trustworthy and relevant and then uses that intelligence to help present users effectively work with data. It combines both business-side uses of data as well as IT best practices for data quality.
At its simplest, data harmonization enhances the quality and utility of business data. However, data harmonization also makes it possible for business users to transform data and create new data analyses and visualizations without IT involvement. Thus, data harmonization significantly decreases the time to create and access business intelligence insights, while also lowering the total cost of data analysis.
Data harmonization technology is applicable in a variety of business functions, particularly sales and marketing. As a relatively new approach to data analysis and visualization, data harmonization is not yet widely used or understood.