DataScava and Robotic Process Automation
“Consistent High-Quality RPA Requires Deep Customer Understanding”
DataScava commissioned named IBM Distinguished Engineer Scott Spangler to write a series of three articles and participate in an Executive Q&A about how our patented domain-specific approach to unstructured text mining complements real-world big data applications in AI, Machine Learning, RPA, Business Intelligence, Research and Talent. Scott is a former IBM Watson Health Researcher, Chief Data Scientist, and author of the book “Mining the Talk: Unlocking the Business Value in Unstructured Information.”
In his article about DataScava and RPA, Scott explains:
- His views on the difference between knowing and understanding when it comes to implementing RPA.
- The drawbacks of using a pure Machine Learning/NLP approach to RPA.
- The need for customer understanding through three fundamental capabilities: classification of content, characterization of the customer, and customization of features .
- How DataScava technology can be employed to fill in these critical gaps and provide a better customer experience by readily capturing existing in-house expertise.
Here’s an excerpt from the article:
“Customers love being understood. It’s just human nature to want to be seen as a unique individual by those we interact with. Therefore, good RPA systems have to work by first understanding the customer’s needs (all of them!), being aware of what the customer doesn’t need, what the customer prioritizes, and only then suggest a course of action (or maybe several, or none).
The DataScava approach enables this level of deep understanding. Multiple customer intents within text can be determined based on a detailed analysis of the unstructured text. Business rules that encode the Boolean logic of the solution space combined with Weighted Topic Scoring can be designed to apply the right solutions to the right situation. This includes the ability to encode rules of form X AND Y BUT NOT Z, as well as to assign different levels of importance to each topic. This precise level of characterization is what’s required to make each customer feel heard and understood.”
“Who’s in Charge of Your Business: The Humans or the Machines?”
In this article about DataScava and AI/Machine Learning, Scott discusses:
- The pitfalls of using a fully automated approach to critical decision-making;
- The desirability of having a parallel human-machine partnership that regulates and monitors the inputs and outputs of automated approaches;
- The three basic ingredients that are needed to make that hybrid process successful and how DataScava implements each of these components.
Tailored Topics Taxonomies (TTT)
Domain-Specific Language Processing (DSLP)
Weighted Topic Scoring (WTS)