How Natural Language Processing Is Changing Data Analytics

How Natural Language Processing Is Changing Data Analytics

As it becomes more prevalent, NLP will enable humans to interact with computers in ways not possible before. This new type of collaboration will allow improvements in a wide variety of human endeavors, including business, philanthropy, health, and communication.


By Malcom Ridgers, BairesDev

Natural language processing (NLP) is the process by which computers understand and process natural human language. If you use Google Search, Alex, Siri, or Google Assistant, you’ve already seen it at work. The advantage of NLP is that it allows users to make queries without first having to translate them into “computer-speak.”

NLP has the potential to make both business and consumer applications easier to use. Software developers are already incorporating it in more applications than ever, including machine translation, speech recognition, sentiment analysis, chatbots, market intelligence, text classification, and spell checking.

This technology can be especially useful within data analytics, which analyzes data to help business leaders, researchers, and others gain insights that assist them in making effective decisions. As we’ll see below, NLP can support data analytics efforts in multiple ways, such as solving major global problems and helping more people, even those not trained in data processing, use these systems.

 
Managing Big Data

With the help of NLP, users can analyze more data than ever, including for critical processes like medical research. This technology is especially important now, as researchers attempt to find a vaccine for COVID-19.

In a recent article, the World Economic Forum (WEF) points out that NLP can help researchers tackle COVID-19 by going through vast amounts of data that would be impossible for humans to analyze. “Machines can find, evaluate, and summarise the tens of thousands of research papers on the new coronavirus, to which thousands are added every week….” In addition, this technology can help track the spread of the virus by detecting new outbreaks.

According to the WEF article, NLP can aid the research process when data analysts “[train] machines to analyze a user question in a full sentence, then to read the tens of thousands of scholarly articles in the database, rank them and generate answer snippets and summaries.” For example, a researcher may use the question, “Is COVID-19 seasonal?” and the system reviews the data and returns relevant responses.

 
Solving Problems

In addition to pressing health problems, NLP used in conjunction with artificial intelligence (AI) can help professionals solve other global challenges, such as clean energy, global hunger, improving education, and natural disasters. For example, according to a Council Post appearing on Forbes, “Huge companies like Google are setting their sights on flood prevention, utilizing AI to predetermine areas of risk and notify people in impacted areas.”

 
Enabling More Professionals

According to an InformationWeek article, “With natural language search capabilities, users don’t have to understand SQL or Boolean search, so the act of searching is easier.” As the quality of insights depends on knowing how to “ask the right questions,” this skill may soon become essential for business operators, managers, and administrative staff.

For example, anyone within a company could use NLP to query a BI system with a question like, “What was the inventory turnover rate last fiscal year compared to this fiscal year?” The system would convert each phrase to numeric information, search for the needed data, and return it in natural language format. Such queries allow any employee in any department to gain critical insights to help them make informed decisions.

 
Creating a Data-Driven Culture

In the past, business intelligence (BI) powered by data analytics required trained data professionals to correctly input queries and understand results. But NLP is changing that dynamic, resulting in what some experts are calling “data democratization”: the ability for more people to have access to data sets formerly reserved only for those with the advanced skills needed to interpret it.

The more people within a company who know how to gather insights based on data, the more that company can benefit from a data-driven culture, which is one that relies on hard evidence rather than guesswork, observation, or theories to make decisions. Such a culture can be nurtured in any industry, including healthcare, manufacturing, finance, retail, or logistics.

For example, a retail marketing manager might want to determine the demographics of customers who spend the most per purchase and target those customers with special offers or loyalty rewards. A manufacturing shift leader might want to test different methods within its operations to determine which one yields the greatest efficiency. With NLP, the commands needed to get this information can be executed by anyone in the business.

 
In Summary

NLP is not yet widespread. According to the InformationWeek article, “A few BI and analytics vendors are offering NLP capabilities but they’re in the minority for now. More will likely enter the market soon to stay competitive.”

As it becomes more prevalent, NLP will enable humans to interact with computers in ways not possible before. This new type of collaboration will allow improvements in a wide variety of human endeavors, including business, philanthropy, health, and communication.

These advancements will become even more useful as computers learn to recognize context and even nonverbal human cues like body language and facial expressions. In other words, conversations with computers are likely to continue becoming more and more human.

 
Bio: Malcom Ridgers is a tech expert specializing in the software outsourcing industry. He has access to the latest market news and has a keen eye for innovation and what’s next for technology businesses.

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