IIC Journal of Innovation 9th Edition | Page 41

Trustworthiness Model Representation Figure 6: The Trust System quantifies the business model and increases confidence in the business outcomes model to the device that is generating the data and the system that supports its generation and transportation to its final destination for analysis. fill” techniques can be applied to camera video streams or patient medical data). Trust is important to ensure data quality for data analytics. Any data quality management effort should start with collecting data in a trusted environment. This in turn implies that the data sources (machines, IoT devices, etc.) and the data collection processes are all trusted. Too often data analysts find that they are working with data that is incomplete or unreliable. They have to use additional techniques to fill in the missing information with predictions. While techniques such as machine learning or data simulations are being promoted as an elixir to bad data, they do not fix the original problem of the bad data source. Additionally, these solutions are often too complex, and cannot be applied to certain use cases. (i.e., no “data When the Trust System is adopted within the business process, the initial Trust Score computation establishes a baseline or trust calibration at the very beginning of the process. During operations, as the Trust Score changes, the operator has to decide on the path forward based on other input criteria: U SE C ASES a) Take action to restore the Trust Score back to the original value OR b) Accept the newly generated Trust Score as the new normal (new baseline) by accepting the conditions that resulted in the new computation. - 37 - IIC Journal of Innovation