Natural Language Processing: The Future of Business

The past decade has ushered in a groundbreaking new era of tools available to assist businesses in furthering their goals. Specifically, the rapid rise of new technologies and interdisciplinary fields like data science is worthy of note. These advances are being applied to everyday business problems in ways that were unimaginable until recently. Such advances are known as business intelligence, a wide-reaching term that refers to technologies and applications that allow business to make better decisions by leveraging the data they produce naturally as a result of their business processes.

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One emerging field of note is self-service analytics, a form of business intelligence that allows everyday business professionals to leverage the benefits and insights provided through analytics without the need for lengthy training or an in-depth understanding of data science principles. Self-service analytics, like many other types of business intelligence, has the potential to revolutionize the way everyday business professionals and organizations make decisions. Through democratizing a company’s native data sets, businessmen and women are able to make extremely informed decisions at a pace never before seen.

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            While the practice of utilizing analytics or data science principles is not revolutionary it its own right, as businesses have been applying these concepts in one form or another for many years, the ability to gain relevant insights from business intelligence platforms is sure to mark a revolutionary change in how companies approach data. Big data, or extremely large data sets that can be analyzed to reveal patterns, trends, or associations, provides a unique opportunity for businesses if approached properly. But how can the owner of a small business or lean startup leverage such advances on limited operating budgets? Thankfully, innovative companies have developed solutions that allow small business owners and lean startup CEO’s to tap into the power of analytics through a method known as natural language processing. This method serves as a sort of translator between requests entered by the user into a search bar and the underlying mathematical computations required to sift through an enterprise's big data.

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            So what exactly is natural language processing, and how does it work? To answer that question, we could delve into a long, jargon-filled monologue that covers recent advances in artificial intelligence and how they help make this technology possible. As tempting as that sounds, let’s instead use an analogy to compare the functionality of an everyday web search engine such as Google and a self-service analytics software platform. When you go onto Google, you can simply enter any question or term imaginable into the search bar and get a list of millions upon millions of relevant results instantly. Most importantly, these results are presented to users in an easy to read format. Now, let’s think of the offerings of self-service analytics firms as a search engine on artificial-intelligence-based steroids.

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 If a business operator wanted to get a quick answer to a detailed question such as, “What were our operating costs in the New England region last quarter?” they could enter the query into their search bar and get an informative answer almost instantly. Business intelligence platforms that rely on self-service analytics return answers to such questions in an easy to understand format that can immediately be applied to important business decisions, just as how a simple Google search can quickly lead you to the closest gas station.

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            The beauty of natural language processing is that it allows users to communicate with their device or software in a way that feels natural for them. If you’ve ever used Siri or Google Assistant to make a phone call or create a reminder on your calendar, you’ve tapped into the power of natural language processing to make your everyday life more convenient. The point of such technologies is to remove common sources of friction within a user’s everyday life, such as making appointments, entering search questions, or checking the weather. While applications like these digital assistants are certainly useful, the power of natural language processing can be leveraged to an even greater extent, especially in areas where business decision making is concerned.

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