Unstructured data is widely prevalent in business. You likely interact with tons of it on a regular basis, including text files, email, social media, websites, communications, and more. Unlike structured data that resides in special databases and is searchable, unstructured data has no pre-defined data models or schema, making it difficult to find information.
80% of data that an organization sees and processes every day is unstructured. More likely than not, every individual on your team works with unstructured data on a daily basis. As a result, your business must adapt to handle this efficiently and effectively.
What happens when it’s time to find specific information or glean insights from all that data? Businesses collect a lot of unstructured data as large document sets, manuals, forms, and more, but have a hard time processing it. Anytime anyone has to review forms, they have to dig through this information manually which is time-consuming and error-prone. AI, specifically natural language processing, fixes this issue.
Natural Language Processing, or NLP, is a branch of AI that deals with the interaction between computers and humans using the natural language. The objective of NLP technology is to read and make sense of human languages to add or find value within an unstructured text.
AI techniques based on NLP can be utilized to large collection of unstructured texts to find the content of interest or certain types of content. NLP is valuable when companies have lots of unstructured text (big chunks of text, not in a database or well-formatted) and want the ability to quickly figure out what it contains.
AI can help extract information nuggets from unstructured data such as raw text (including
HTML and PDF documents). For example, we used AI to identify requirements statements in the materials standards documents maintained by the Society of Automotive Engineers. In this case, it was not possible to do accurate data extraction using an out-of-the-box solution.
By collecting domain-specific data and training a custom model, we were able to achieve a <1% false-negative error rate in identifying manufacturing requirements. A combination of information retrieval and extraction tools can be used to partially automate the process of answering questions in a natural language.
In this same example, without an AI tool, manual data extraction (someone going through and reading all the materials standards documents from the Society of Automotive Engineers) is the only other way to pull out manufacturing requirements.
Since unstructured data is so prevalent in business, it’s critical that you’re able to handle this efficiently and effectively. AI is your answer to automating information extraction and eliminating cumbersome manual efforts.
Contact Cognistx to learn more about NLP and how it can streamline your data extraction.
