How Natural Language Processing Will Save Us From the Information Abyss

Here’s an excerpt from an actual email sent circa 2019 that anyone who’s ever worked in a company anywhere can relate to:

Hey Andrew, remember that project meeting we had last year where we walked our teams through product ideas from our offsite? Can’t remember where I saved the doc or even the file name. I spent the entire morning looking through directories, old emails, even chat threads. Nothing! Any ideas?

Sound familiar? It should. McKinsey reports that the average information worker spends nearly 20 percent of the workweek looking for internal information or tracking down colleagues who can help with specific tasks. Crazy stuff.

Businesses today deal with massive amounts of unstructured data from sources like emails, chats, social media, support tickets, and documents. Making sense of all this data manually is impossible. This is where natural language processing (NLP) comes in. NLP is a branch of artificial intelligence that analyzes, understands, and generates human language. Using NLP, businesses can automate tasks, gain insights, and improve search from text data.

Here are some ways NLP can be applied in business:

Automating Tasks

NLP can be used to automate repetitive tasks that involve analyzing text. For example, customer support teams have to go through numerous inquiries every day to categorize them and route them to the right agents. NLP algorithms can be trained to read support tickets and automatically tag them based on the content. This saves a lot of agent time. Similarly, NLP can be used to scan resumes and shortlist candidates faster.

Extracting Insights

Valuable insights are often buried in text data like customer feedback, surveys, call center logs and social media posts. NLP techniques like sentiment analysis, topic modeling and named entity recognition can be used to extract insights. For example, brands can analyze customer feedback to identify common complaints. Or they can monitor social media to see which products have the most negative sentiment. These insights help brands improve products and services.

Improving Search

NLP powers semantic search technologies that go beyond just matching keywords. This allows people to find information using natural language queries even if the exact keyword match does not exist. For example, an employee searching for "last quarter's sales report" can find the document even if the title just says "Q3 Sales Report". NLP improves search accuracy and relevancy.

In essence, NLP brings human-like language understanding to businesses. It unlocks insights from unstructured text data— enabling more intelligent automation, insights and search. With the right tools and expertise, NLP can be applied across industries to optimize text-based processes. The future will see wider adoption of NLP as businesses embrace AI to get more value from their data.

Contact our team today to learn how NLP tools can be deployed in your business!