DataScava and Business Intelligence
DataScava represents a brand-new way of thinking about unstructured text data and Business Intelligence. It fits right into your existing BI tool-set and takes very little training to get started.
We have commissioned a series of articles written by 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 the series, 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, Research, Operations, Talent, and more. In addition, he contrasts our proprietary Domain-Specific Language Processing (DSLP), Weighted Topic Scoring (WTS), and Tailored Topics Taxonomies (TTT) with today’s commonly used methods such as Natural Language Processing and Natural Language Understanding .
In this second article of the series, “The Key Ingredients for Game-Changing BI from Unstructured Data,” Scott discusses:
- The importance of fully utilizing unstructured data in BI analytics.
- An overview of the most common current approaches to analyzing unstructured information in BI — Machine Learning, Generic Taxonomies, and Text Mining — highlighting the specific drawbacks of each.
- The importance of subject matter expert-driven taxonomies.
- How DataScava can be used to build and deploy these taxonomies at scale and mine unstructured data to maximize business value.
Our Patented Approach
Domain-Specific Language Processing (DSLP)
Weighted Topic Scoring (WTS)
Tailored Topics Taxonomies (TTS)
Highlighter
Taxonomies for Financial and IT Domains
Taxonomies for Talent Mining and Skills Analytics
