Forbes Technology Council
Human-Computer Communication: How Businesses Are Using Natural Language Processing To Talk To Data

Human-Computer Communication: How Businesses Are Using Natural Language Processing To Talk To Data

Today’s business users rely on a collection of reports and dashboards to better understand the data underlying their operations. These tools are most often designed by IT organizations, which use coding languages like SQL to ask questions of their database and report the findings back to business users.

Because of this, the modern enterprise has access to a huge collection of data, offering feedback on everything from sales and marketing campaigns to supply chain operations and logistics. However, not every organization has the tools and skills to maximize the value of this data.

Speaking Different Languages

Why is it so difficult for business users to communicate with their AI-backed tools? At their core, humans and computers process information and communicate in fundamentally different ways. When humans ask questions, they gather their thoughts into phrases and sentences. When computers are given a question, they break down these long phrases into individual entities, which must then be classified into different categories for the computer to make sense of them.

Natural Language Processing 

For artificial intelligence tools — and business intelligence platforms in particular — to be useful, business employees must be able to directly ask questions of the data. The key solutions to this problem are text and language processing-based, which enable computers to understand human questions and their semantic structures, meanings and how they can be applied to the data.

A natural language interface is necessary to deliver maximum value and to provide a utility that can adapt to the needs of the business. Businesses are taking different approaches to natural language interfaces, whether building their own in-house or adopting a pre-built solution from a vendor. Each of these options comes with its own set of challenges. 

Read this full article to understand the challenges when building NLP solutions on Forbes here.

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