It is known that ML/AI algorithms are data hungry. But more than quantity of the data, quality and the data with proper statistical distribution is key for attaining a good accuracy/performance.
The data is usually incomplete (missing fields in the table etc.) or non-coherent (anomalies) across databases. The data is normally not conducive to apply ML/AI algorithms directly, it needs de-duplication, normalization etc.
Gyrus offers services to clean up data and to improve the quality score in addition to providing services to annotate data for the specific business user-cases. Gyrus also can enrich the data by augmenting the dataset with third-party data.
In enterprises, data exists in several silos and is captured with different criteria. And the data across the silos is not usually linkable on one field as the master key is usually different. Mere merging of data is very expensive (at times size grows 100x or more). There should be methods to be able to cross link and use data across different data structures.
Gyrus has tools to assess the quality of the data and gives a “QUALITY SCORE” to the data based on the various factors mentioned above in addition to the statistical distribution of the different features of the data.