DataScava and Business Intelligence
“The Key Ingredients for Game-Changing BI from Unstructured Data”
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.” 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, and Talent. In addition, he contrasts our proprietary Domain-Specific Language Processing (DSLP) and Weighted Topic Scoring (WTS) with today’s commonly used methods such as Natural Language Processing (NLP) and Natural Language Understanding (NLU).
In his second article of the series, 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.
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.
Domain-Specific Language Processing (DSLP)
Weighted Topic Scoring (WTS)
Tailored Topics Taxonomies (TTS)
DSLP Taxonomies for Financial and IT Domains
DSLP Taxonomies for Talent Mining and Skills Analytics