Experts estimate that as much as 90 percent of the data that is generated daily is unstructured data. This includes everything from documents to text files, emails, videos, audio files, social media, presentations, blog posts, photos, and more.
It’s a challenge to extract information from this daunting volume of digital data so companies can realize business value. Technologies such as AI, Machine Learning, RPA, NLP, and NLU try to make it more accessible, understandable, and actionable. A key first step is to identify highly precise subsets of documents with relevant context and content for use in training data, analysis, and automation.
Up to 80% of their efforts are spent in this time-consuming process — finding, cleaning, and reorganizing huge amounts of messy data — because these systems require accurate input to ensure they don’t use documents about “viral Tweets” if their focus is scientific research about “viral infections” like COVID-19.
But mining unstructured text data isn’t simple. To work effectively, a solution must be fine-tuned to meet your specific needs and address the quirks in your textual content.
DataScava addresses these types of issues and others by helping experts in Data, Business, and Software use their own business language and domain expertise to mine unstructured text data.
