In any organization, text is the most abundant and, arguably, most underutilized data asset. The now accepted fact that 80% of enterprise data is unstructured still stands in 2022, and as volumes of language-based information—documents, email, reports, social media—continue to grow, this only underscores the urgent need to take advantage of it.
Enter NLP. Natural language processing refers to the technologies that allow computers to understand human languages.
If you’re leveraging NLP to optimize language data and automate business processes—and many are, according to Forrester, there is one decision that can make or break your outcomes: The text analytics that power your NLP applications.
Why NLP + Text Analytics is a Business Game-Changer
So, why is your choice of text analytics such a game changer? To understand, let’s go back to that important four-letter word we mentioned at the outset: data.
Not all data is exploitable. Structured data, the data that is often stored in databases, is quantifiable and easily extracted and leveraged. On the other hand, your unstructured data—the text formats we mentioned above, but also audio and video formats—require a high degree of contextual understanding to interpret.
For example, the date and time of an email are structured data, whereas the body text of the email is unstructured data. Any application that works on this unstructured data must be able to understand the text, the words that it’s written in, and the context: that’s human language.
According to Gartner, enterprises sit on unexploited, unstructured data, with the opportunity to extract differentiating insights. They say that solutions that work on this language data—natural language technology solutions like intelligent document processing, conversational AI and insight engines—can help uncover the insights it contains.
In NLP, text analytics is used to examine the structure and meaning of written content. Together, they break down language data so components can be analyzed and understood.
Just consider all of your sources of potential intelligence that are in the form of language data: your customer service interactions, your contracts and intake forms, your risk assessment reports, your emails and social media channels (just to name a few), and think of the signals and insight hidden here.
Why Your AI Approach Can Make or Break NLP Investments
Different AI approaches vary by type and level of text analytics. Some approaches go for accuracy, while others prioritize automation and scalability. Less accurate text analytics results lead to expensive rework that can degrade ROI. If your DIY solution requires big investments in model development and even larger model training and compute costs, this can also impact ROI.
These are just some of the ways that the choice of approach for text analytics can hinder NLP ROI. This was highlighted in a recent Forrester Wave report: “Text analytics is table stakes, but not all text analytics capabilities are equal. Challenges with a vendor’s text analytics are key reasons enterprises add other technologies to their stack.”
Errors or shortfalls in language understanding can wreak havoc on NLP applications; that’s why the accuracy with which NLP applications operate hinges on the ability of your chosen text analytics application—and its underlying approach—to provide language understanding at scale.
We’re here to help you choose your text analytics wisely.
Our new white paper explores six text analytics essentials for ensuring reliable, scalable and highly accurate NLP applications.
You’ll learn why open source isn’t as free as you think, why domain knowledge is essential (but isn’t always guaranteed), why it’s a deal killer if NLP can’t support language ops…and much more.
Download Text Analytics Buyer’s Guide
