Plumb5 Machine Translation Algorithm can make a huge difference in the way we perceive data discovery. You can overlay this algorithm over any kind of data sets over which you can simply query in natural language to visualize insights across any data parameter.
The algorithm plays over a modular semantic model, which can be tweaked based on incoming datasets. Based on the incoming parameters, the algorithm will translate the natural language text to machine query, in order to identify the desired parameters. Based on the parameters selected, the model presents raw or computed outputs, derived from rules of the model.
This algorithm can virtually wipe out manual report customization, which currently is a very expensive affair. Businesses using software applications collect data in silos and end up spending more in integrating data and customizing reports. The algorithm can deliver any kind of report instantly and save up 80-90% of BI expenses.
Enterprises can implement the algorithm over their current data warehouses or data lakes, where data volume is huge and unstructured, and save millions of dollars spent in data discovery and visualization. It would require 3-4 weeks to organize data and set rules, which is a one-time affair. Whenever new data parameters are inducted, data admin should manually tag the new parameters to the data model.
The algorithm can be applied to large healthcare data, unstructured social data, telecom data or any ad-hoc data that lacks structure.
Below is a demo video showcasing how the algorithm works within the conversational UI for managing data.