TalentBrowser’s Automated Skills Analytics, Patented Talent Matching, and Domain-Specific Search are powered by DataScava’s unstructured data miner, which is built upon our two U.S. Patents in matching unstructured documents. They generate value-added metadata from the raw unstructured text on resumes and profiles for use in Talent Matching, People Analytics, Business Intelligence, and other initiatives.

DataScava’s Domain-Specific Language Processing (DSLP), Weighted Topic Scoring (WTS) and Tailored Topics Taxonomy (TTT) methodologies work as an adjunct or alternative to Natural Language Processing (NLP) and Natural Language Understanding (NLU).  They provide precise results you can see, control, and measure.

DataScava helps you model and capture features and topics within heterogeneous text using specialized taxonomies you can edit, create, import, and control to capture your business and domain expertise, allowing for the highly customized vocabulary and specific business logic necessary for complex document processing.

To contrast our approach to mining unstructured data with others, DataScava commissioned an “Executive Q&A: DataScava, AI and ML” and 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.”

Scott discusses how and why DataScava’s patented precise approach to mining unstructured text data perfectly complements real-world big data applications in AI, ML, RPA, BI, Talent, Research, Operations, and more.

In addition, he compares Domain-Specific Language Processing (DSLP), Weighted Topic Scoring (WTS), and Tailored Topics Taxonomies (TTT) with standard methods such as Natural Language Processing (NLP) and Natural Language Understanding (NLU).

Click to view 

“Who’s in Charge of Your Business: The Humans or the Machines?”

“Executive Q&A: DataScava, AI and ML”

“DataScava and Business Intelligence”

“DataScava and RPA”

 In this first article in the series, 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.

View the published article


Go to the DataScava website to learn more.