Data Classification

Data Classification and Thematic Analysis are machine learning techniques that address the challenges of pattern recognition, by assigning categories, and values to text or documents being analyzed, based on domain-specific models. Specialized classifier models are trained using a combination of Data Dictionaries, Semantic Graphs, data residing in files, databases or any storage medium. They are smart enough to recognize parts of speech or phrases and can be easily made to understand technical terms, medical or legal jargon to infer meaning based on key terms and content similarity.

Classification services make it possible for Cognitive AI to discover, organize, and retrieve data based on similar content, unique features, or semantic meaning. They can also be used to establish intent or sentiment in a body of text and predict features that are unknown or may appear in the future. Classification and Thematic Analysis have broad application in decision support systems, legal due diligence, direct marketing, regulatory compliance or data privacy, insurance, fraud detection and medical diagnosis.

What Problem Does This Solve?
Classification addresses the challenges of data discovery and outcome predictability in human decision making. It helps users make sense of information and find patterns. Sorting through data across an enterprise can be an expensive and cumbersome process. Few organizations are equipped to handle data classification by traditional (manual) methods. Cognitive Automation tools can help streamline the process, and simplify the tasks of defining objectives and determining the categories and criteria used to classify data.

Streamscape's Cognitive AI platform lets users analyze and classify data at scale, significantly improving knowledge worker productivity and decision making by automatically discovering useful patterns, trends or data relationships based on semantic meaning and context. Analyzing term relationships, themes and identifying similar key words or phrases will often uncover deeper meaning in data and help organize information into common areas of interest. This type of semantic analysis lets machines simulate human cognition by classifying results based on specialized vocabulary, in the same way subject-matter experts do when analyzing complex information.

Download the White Paper and learn how you can leverage StreamScape's Data Classification Services to improve human decisions with Machine Guided outcomes.
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