Data Enhancement and Transformation

There are many use-cases for enhancing and transforming data using intelligent contextual actions, such as: 

• Comparing two sources of the same information to get the best of both, for example if one data source has accurate geometries and the other has accurate attribution.
• Collating data from different countries or regions to ensure that they are consistent, aligned at the edges and contain no duplication.
• Transforming cartographically-oriented or linear data to produce seamless, polygonised, classified, real-world object-based spatial data.
• Matching possibly incomplete records (such as addresses) from one data source to the best matching record from a master data source.
• Generating a less detailed and intelligently simplified "generalised" or "schematised" version of the data from a ‘single source of truth’ master dataset while maintaining data connectivity and correctness.
• Transforming data from one structure to another using business rules to map the objects and attributes. Typically this requires intelligent reclassification and inferring of new information based on the surrounding data.