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DataScava and RPA 

As organizations increasingly adopt Robotic Process Automation (RPA) to improve efficiency and reduce costs, the importance of precise, context-aware data processing cannot be overstated. RPA systems rely on accurate inputs to automate workflows, make recommendations, and trigger actions. Without high-quality unstructured text data, even the most advanced RPA systems can fall short of delivering a seamless customer experience

Insights from Scott Spangler

We commissioned a series of articles from Scott Spangler, former IBM Watson Health Researcher, Chief Data Scientist, and author of the book “Mining the Talk: Unlocking the Business Value in Unstructured Information,”  in which he discusses how and why DataScava’s patented precise approach to mining unstructured text data perfectly complements real-world big data applications in AI, LLMs, ML, RPA, BI, Research, BAU, and TA, and more. He also contrasts our Tailored Topics Taxonomies, Domain-Specific Language Processing, and Weighted Topic Scoring methodologies with standard approaches such as NLP and NLU.

In “Consistent High-Quality RPA Requires Deep Customer Understanding, Scott discusses:

  • 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 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:

“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.”

Our Patented Approach

DataScava employs a combination of methodologies to optimize RPA workflows:

Domain-Specific Language Processing (DSLP)

  • Processes unstructured text using user-defined business language.
  • Generates precise, file-level metadata for actionable insights.
  • Ensures RPA workflows are powered by accurate, context-rich data.

Weighted Topic Scoring (WTS)

  • Prioritizes and scores data based on relevance and importance.
  • Classifies and tags unstructured data with precision, reducing errors.
  • Enables automated workflows to address complex customer scenarios.

Tailored Topics Taxonomies (TTT)

  • Builds taxonomies that reflect your organization’s unique domain expertise.
  • Captures nuanced business logic for accurate text processing.
  • Continuously evolves to adapt to changing business needs.

Pre-Built Taxonomies for Industry-Specific Needs

DataScava provides pre-configured taxonomies to accelerate deployment:

  • Financial and IT Domains: Address industry-specific requirements for precision and compliance.
  • Talent Matching and Skills Analytics: Streamline recruitment and workforce analysis with tailored taxonomies.

These taxonomies are fully customizable, enabling businesses to refine and expand them based on their unique needs.

 

Why Choose DataScava for RPA?

  1. Deep Customer Understanding: Capture and process multiple customer intents to deliver personalized automation.
  2. Accuracy and Transparency: Boolean logic and Weighted Topic Scoring ensure precise and explainable results.
  3. Seamless Integration: Works alongside existing RPA systems to enhance workflows without disruption.
  4. Scalable and Adaptive: Continuously refine taxonomies and rules to meet evolving business demands.

 

Unlock the full potential of RPA with DataScava—delivering precision, personalization, and actionable insights.