DataScava is an automated system that helps you mine messy unstructured text data to unlock its value and make it more accessible, understandable and actionable. It uses your own business and domain language — with topics, searches and filters you control to produce highly precise results you can see and measure.
Our self-service solution is for data professionals, subject matter experts, business users and software engineers. It uses human intelligence, not artificial intelligence — machine training, not machine learning. And you don’t need to be a data scientist or programmer to use it.
With DataScava you can curate, search, filter, tag, match and route raw unstructured text for use in Business Intelligence, AI, Machine Learning, Robotic Process Automation, Research and other data-driven systems.
Our proprietary Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS) methodologies turn raw text into structured data you can act on, and work as an alternative or adjunct to Natural Language Processing (NLP).
Surface Relevant Information Faster
Automated Solutions to Unlock Unstructured Text Data
- Drastically reduce the time-consuming tasks of preparing large unstructured text data sets for use in BI/AI/ML/RPA/Research or other downstream systems.
- Ensure that data quality is high, reduce the risk of suggested actions and measure their output.
- Find, filter, tag, match and route text data in databases, subscription-based feeds, emails and documents based on content, intent and more.
- Ease of use and transparency enable collaboration between nontechnical and technical people, providing a rapid path to efficiency.
7 Ways Mining Unstructured Text Data with DataScava is Different
- It uses our proprietary Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS) methodologies.
- It uses Human Intelligence, not Artificial Intelligence; Machine Training, not Machine Learning, and excels at Navigational Search.
- It works top-down through your entire corpus at the file level, not at the sentence level.
- It indexes, measures and filters raw text, identifies and highlights on-topic files and eliminates irrelevant ones.
- It summarizes textual content in a usable, numerical form for routing purposes or to trigger an action using a process that is adjustable by users.
- It doesn’t use NLP or Semantics to try to disambiguate natural language or infer what you’re looking for — it finds what you are looking for.
- It encapsulates your organization’s subject matter expertise, business language, jargon and acronyms in your software.