According to many market statistics, data volume is doubling every two years, but in future this time span may get further reduced. The vast portion of this data (about 79 percent) is text data. Natural Language Processing (NLP) is the sub-branch of Data Science that attempts to extract insights from “text.” Thus, NLP is assuming an important role in Data Science. Industry experts have predicted that the demand for NLP experts will grow exponentially in the near future.
The Future of Natural Language Processing explains that in NLP, machines are taught to read and interpret text as humans do. NLP is recognized as the “enabler of text analysis and speech-recognition applications.” This human capability for interpreting text comes in handy for analyzing large volumes of text data. NLP is set to capture the voice of the customer. As an example of this use of NLP, think of Google Drive, where users can search documents via conversational input.
With the exponential growth of multi-channel data like social or mobile data, businesses need solid technologies in place to assess and evaluate customer sentiments. So far, businesses have been happy analyzing customer actions, but in the current competitive climate, that type of customer analytics is outdated.
Now businesses need to analyze and understand customer attitudes, preferences, and even moods – all of which come under the purview of sentiment analytics. Without NLP, business owners would be seriously handicapped in conducting even the most basic sentiment analytics.
The Future Belongs to NLP
The DATAVERSITY® article Natural Language Processing: The What, Why, and How drives home a significant point: Although NLP is still a futuristic science to many, in reality it has entered the mainstream. Examples of NLP in common practice are MIT’s Laboratory for Social Machines, where NLP drives the analysis of social systems for positive change, and IBM’s Watson for Cyber Security, which uses NLP to “gain insights from security documents.”
In How is Natural Language Processing Data Science?, Dina Demner-Fushman, M.D., Ph.D., a leading researcher in Natural Language Processing, claims that most of the healthcare data acquired at National Institute of Health (NIH) in the U.S. contains textual notes, clinical notes, semi-structured data, and metadata. Much of the clinical decision-making process at NIH is guided by text-based evidences, which is possible due to NLP.
NLP in Enterprise AI
A Framework for Applying AI in the Enterprise explains that this renewed interest in NLP has been triggered by the rise in text data, which in turn has triggered research in advanced AI applications. With this Gartner paper in mind, the enterprise Data Science staff can seize fresh opportunities to combine NLP with Deep Learning techniques to capture hidden insights from business data very quickly.
NLP for the Future
A significant reward of NLP to businesses is the concept of a smart assistant, which has the potential to transform customer experience, leading to customer loyalty. The smart assistants have already proved their usefulness in customer service, and hopefully NLP will emerge as a game changer for CS in the future. However, for applications to be readily acceptable to both the customers and business staff, the future solutions have to merge conversational engagements with technology to deliver the most enjoyable user experience.
The second consideration is the omni-channel ecosystem of the enterprise. In the future, it will not be enough to combine advanced technology with user experience; customers will come to expect this amazing conversational engagement across all channels.
Is NLP the Future of Business Intelligence?
As NLP continues to make “data” more user-friendly and conversational, more and more mainstream users will adopt NLP-driven Data Platforms. In a way, NLP will remove the current barriers to entry for Big Data BI. Someday, business users may engage in BI tasks through “conversational” interactions with smart assistants or chatbots. The “conversational platform” will encourage lot of reticent users to attempt advanced BI. This growing trend is explored in Here’s Why Natural Language Processing is the Future of BI.
Let us hope that with the advancement of NLP, the differences between natural language and machine language will be blurred. Why Natural Language Processing is the Future of Business Intelligence explains that a future version of Watson may make multimedia analysis possible for machines, which was previously the guarded domain of human brains!
The Use of NLP in Business Sectors
Another remarkable use of NLP may be in sentiment analysis, where texts surrounding social gestures or comments may give a clue to whether such gestures or comments are positive or negative. With further improvements in speech recognition technology, the audio-video sources will offer rich data analysis, thus expanding the scope of traditional BI into every aspect of business.
Here are some common business areas currently leveraging NLP for increased returns:
- Businesses use NLP to exchange market intelligence with all stakeholders.
- Chatbots have become a solution for customer call centers. Chatbots can provide human-like assistance to customers, reducing call loads and customer frustration.
- As mentioned before, businesses operators are increasingly relying on social data to monitor customer sentiments. Much of this data is text and requires NLP for sentiment analysis.
- NLP has substituted several customer-service functions with reliable service.
- NLP has also helped target advertising funnels targeted at segmented customers.
5 Applications for Natural Language Processing for Businesses in 2018 reviews the above uses of NLP in depth.
Estimated Market for NLP Applications
A 2017 Tractica report estimated the 2025 NLP market, including hardware, applications, and services, would be around $22.3 billion. This same report states that that the AI-enabled NLP software market will rise from $136 million in 2016 to $5.4 billion in 2025.
In Natural Language Processing – Current Applications and Future Possibilities, Dan Faggella, the CEO of TechEmergence, makes note of current trends and future possibilities of NLP during his interview with Vlad Sejnoha, the CTO of Nuance Communications, a business offering AI and NLP solutions in voice, natural language, and related systems.
NLP Trends to Watch
- Gartner predicted that in 2018 (and beyond), that NLP would be combined with Machine Learning and Big Data techniques to build powerful question-answer systems such as chatbots.
- There is an increased perception that ML and NLP together with advanced data analysis, pattern recognition, and data-interpretation capabilities, have the potential to replace human Data Scientists.
- Platforms like iDS Cloud can help businesses reap the benefits of Data Science and NLP without the presence of human staff.
Augmented Analytics and Data Discovery explains how Business Analytics of the future will be fully automated due to Machine Learning and NLP. In the future, Augmented Analytics and Data Discovery will convert every ordinary business user into a Citizen Data Scientist through automated guidance on data analysis tasks.
NLP is still in its infancy compared to other Data Science technologies like Deep Learning or Neural Networks. However, NLP has piqued the interest of the global business community, which is a positive sign for future growth. If industries continue to sponsor NLP research, we can expect a quicker transformation in Business Analytics in the future.
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