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.
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IIC Journal of Innovation