DataScava mines messy unstructured text data so you can unlock its value and make it more accessible, understandable and actionable.
It’s a self-service system that curates, searches, filters and routes raw unstructured text data for use in Business Intelligence, AI, Machine Learning, Robotic Process Automation, Research and other data-driven solutions. It uses your business language and domain-specific topics, searches and filters tuned to your business and in your control.
DataScava is for Data Professionals, Subject Matter Experts, Business Users and Software Engineers, and keeps the human in command. It works as an alternative or adjunct to NLP/NLU. You don’t need to be a Data Scientist to use it.
Our technology uses human intelligence, not artificial intelligence. Machine training, not machine learning. And our proprietary Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS) methodologies, which produce fast, highly precise and visible results.
Surface Relevant Information Faster
Automated Solutions to Unlock Unstructured Text Data
- Drastically reduce the time-consuming tasks of curation of large unstructured text data sets required as input to BI/AI/ML/RPA or other downstream data-driven systems.
- Ensure that data quality is high, reduce the risk of suggested actions and measure their output.
- Find, filter, match and route unstructured 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, quantifies 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.