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While RPA can be used to digitize repetitive processes it can also be used to sift through large amounts of data in order to identify relevant information for the product owner.
Structured and clearly organised data, which is something RPA excels at. Digitising data collection removes many of the human complacencies that contribute to ‘bad’ data

where's the bad data come from?

1. Simple human error.
2. Inadequate training and/or poor process adherence by users.
3. Multiple systems with potentially overlapping data.
4. Business processes containing many manual steps.
5. System workarounds and reuse of data fields intended for another purpose.

HOw to det a good data

Automation Anywhere, a leading automation provider, delivers an "action framework" which commands RPA to sort data using a variety of parameters. Here are three ways
data could be distinguished:
• Good to know: All numbers are “green” and within their thresholds
(No action required)
• Data seems interesting: The number seems better than expected and intrigues me to
drill down further (Invites a drilldown or adhoc analysis action)
• Data to act upon it: Numbers are “red” and outside of their thresholds (Action needed
immediately)

Data cleasing

The most innovative robots are automated with .NET capabilities & VB expressions and functions, they can create, filter, merge,structure and analyze this isolated data