Natural Language Processing and Modern Data Analytics: Extracting Insights From Unstructured Data Sources

Natural Language Processing (NLP) is machine learning technology that allows computers to interpret, manipulate, and comprehend human language. Many people have been using NLP without realizing it, encountering it in their daily lives via Google Search, Alexa, Siri, and Google Assistant. Something that not many people outside of the tech industry are familiar with, however, is modern data analytics. However, NLP can facilitate modern data analytics by assisting with extracting insights from unstructured data sources.

Defining Modern Data Analytics

For those not already in the know, modern data analytics is the science of collecting raw data and analyzing it to find trends and uncover patterns. Artificial intelligence (AI) and other advanced technologies make it modern.?

With growing interest in both big data and artificial intelligence, modern organizations need to familiarize themselves with the capabilities of NLP for data analytics to stay competitive. In particular, NLP’s ability to extract actionable insights from large datasets are very promising.

What is Unstructured Data?

Unstructured data is essentially data that has not been constrained to a form in which a computer can read it easily. Examples include websites, memos, and reports. These are generated in human language but generally are not easy for a computer to understand. It’s easy to see how a technology like NLP, that focuses on computers understanding human language, could make a big difference in bridging the gap between human and machine when it comes to this unstructured data.

A Real Look at How NLP Is Being Used To Improve Modern Data Analytics and Extract Insights From Unstructured Data Sources

Now that we are more familiar with NLP and modern data analytics, let's shift gears and discuss how they work together. According to Forbes, Google and other large corporations already use NLP and AI to identify historical floods and other natural disasters in a given area. That information, they say, can be used to coordinate with local or national governments to issue warnings or evacuation orders should a new emergency arise. It's because NPL and modern data analytics make it easy to extract insights from unstructured data sources that any of this is possible.?

In another important example, NLP and data analytics played a crucial role in helping Pfizer, Moderna, Johnson & Johnson, and others to develop a vaccine against COVID-19. The combination helped scientists and researchers go through larger amounts of data than humans ever could without AI. In short, NLP and data analytics helped get potential vaccines into clinical trials sooner, which led to the rapid development of multiple vaccines that eventually stopped the spread of COVID-19. While researchers and scientists were using NLP and modern data analytics to bring about a vaccine that would go on to save countless lives, it became clear that these technologies could do a whole lot more.?

How NLP Is Improving Modern Data Analytics

Some of the ways the use of NLP makes modern data analytics easier, even with unstructured data, include improving speed and accuracy, providing the ability to interact with data, enhancing objectivity, and more.

Speed and Accuracy?

A single data analyst can easily and quickly comb through small data sets. However, large data sets are another matter entirely. Companies that use NLP to manage survey results, for example, can process raw survey data into valuable insights far more efficiently and accurately thanks to powerful visualization software built into some NLP-based technology.

The Ability To Interact With Data

NLP allows more people in an organization to interact with data rather than just data analysts, which is the modus operandi for organizations that do not use the platform.?That's because NLP allows non-data analysts to approach data conversationally.? Think of it as using prompts in ChatGPT to return data answers.

Obtaining More Objective Results

Another way NLP simplifies extracting insights from unstructured data sources is by reducing the risk of human bias. Instead of data analysts, NLP can be used to analyze text, speech, and raw data, which makes arriving at an objective conclusion, rather than a subjective one, more likely (although bias in AI is not impossible, due to the human-created sources on which AI models are trained).?

Reducing or Eliminating the Need for Manual Data Annotation

NLP can convert unstructured text from word processing programs, presentations, and the like into clean, structured data. These conversions can help with machine learning, a form of AI that allows systems to improve their own models based on their experiences. For example, machine learning plays a role in converting electronic health records pulled from unstructured text into structured data. Without NLP, this process would require costly manual data annotation.?

Text Mining

Arguably, the biggest and best benefit of using NLP to extract insights from unstructured data sources is the technology's capacity to resolve language ambiguities and provide numeric structure. These two things make combing through survey data that contain free-form text easier than it would otherwise be. But it doesn't end there; NLP uses visualization, also known as text mining, to identify characteristics like sentence length, word frequency, and word length, and put them into a visual form such as a histogram or bar chart for further review by a data analyst.

Customer Service?

While nothing yet trumps a real, human customer service experience, NLP is proving to be the next best thing. Many corporations are using NLP technology to create customer service chatbots that sound more human and less robotic. The data application comes in when the chatbots analyze information gathered from its interactions with customers and use machine learning to become better chatbots.

Stay Current with NLP’s Capabilities for Data Analytics with Square Peg Technologies

NLP and AI in general are actively revolutionizing and modernizing data analytics. The combination is helping data analysts extract more accurate information faster and use that information in new and innovative ways. That said, if you're a business or government entity not already using these platforms, there is no time like the present to get started. The best way to integrate this technology into your operations is to hire experts that will work with you to understand your organization’s needs, like those at Square Peg Technologies. Contact us today.