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Unstructured Data and Text Analytics
Unstructured Data provides a valuable source of information, yet it is time consuming to use and challenging in case of automation.
Why should an organisation care about utilising its unstructured data?
- Knowing which data is relevant or valid could help us rapidly decrease cost for storage and back-up solution.
- Automation of manual entry/sorting process with dramatic increase of efficiency and accuracy.
- Better insight into the company reputation and brand value.
- Way how to comply with regulatory requirement (FATCA, AML, KYC …).
- Valuable data source for future decisions.
- Sometimes it is not possible to describe the relationship with customer only with numbers.
There are several types of unstructured data and text analytics. The types below are examples of the most frequently used ones.
Utilising its unstructured data
Analysing unstructured data
Extrapolate document classification on a training set using supervised machine learning models. For instance automatic classification of PII and relevance (computer-assisted review).
Content (Semantic) Analysis
There is a substantial value in written documents, notes, emails and other similar documents created by people. Natural Language Processing is still evolving and highly advanced technique that addresses uncountable possibilities and complexities in human communication. Utilising the information automatically for business insights creates a plethora of new possibilities in Data Analytics as human communication is one of the most valuable sources of information. We are helping to automate this.
The main benefits for our clients are for example:
- Enhance discovery of risks to the business – Churn, Competition, AML, Fraud;
- Enhance Propensity to Buy models;
- Discover Lifestyle and Sentiment of Customers.
The usual high-level process for Semantic Analysis can be depicted as follows. It can also be preceded by Speech-To-Text transformation when used on call data.