Language is the primary tool that humans use for communication. It acts as a medium to convey feelings, and information. When it comes to us humans, using language comes naturally as we are adept at understanding the ‘context’ and ‘meaning’ behind the words. But the same rule doesn’t apply to computers. For computers, text and spoken word is just a character string and sound respectively. Unlike human beings, computers cannot abstract the ‘context’ from the content. Since computer technology has become so important to our daily lives, it is crucial that we teach computers to ‘understand’ natural language. This is where Natural Language Processing (NLP) comes into the picture.
Natural Language Processing (NLP), a field of computer science and computational linguistics, governs the interactions between natural (human) language and the computer to ensure that humans can interact with machines in a way that they would with other human beings. NLP is a science born from a confluence of machine learning, artificial intelligence, and linguistics. The core of NLP is in making it possible for computers to understand the ‘context’ and in turn the ‘intent’ behind any textual or auditory communication.
Some of the common examples of NLP today are technologies such as Apple’s Siri, Amazon’s Alexa, and Google Assistant or Google Home that can recognize common patterns in speech to in turn understand the ‘meaning’ of the message. These tools are increasingly being used both for professional as well as personal tasks as they are capable of almost accurately responding to user queries and requests. Another fascinating manner in which NLP is being used is to parse messages, emails and calendar invites to detect things such as meeting invitations, notifications, and reminders from them. This is a feature that most modern phones powered by Android and iOS are putting to use, along with Gmail that has been using this technology for quite some time now. Even while using Google search, one can see that based on the keywords used, associated suggestions are made by the system – this is also a prime example of how with the help of NLP, computers seem to be ‘reading’ our minds.
Business Intelligence (BI) involves the strategies and technologies used to analyze business information and make informed decision-making It helps create a better understanding of the global market as well as provide an insight into how your company operates. Data insights today have become a crucial factor for decision-making, driving organizations to go beyond just their ‘instinct’ or ‘gut’.
The traditional way of accessing data through most BI systems is by logging into the application, generating the desired report and filtering the insights through multiple dashboards. Because of the long drawn out process of the traditional workflow and the fact that some amount of technical acumen is required, user adoption of BI decreases.
This is also the reason why companies often feel compelled to hire highly qualified data scientists and data analysts to glean insights from BI systems. Now, imagine if there was a way in which the required insights can be ‘fetched’ just by asking natural language questions.
A growing number of global companies today are adopting Business Intelligence Chatbots that are able to understand natural language and carry out complex tasks related to BI. Because of this, data consumption among business users has become much easier. Integrating NLP enabled chatbots with your existing BI systems like Power BI, SAP, Oracle, etc., enables users to access data via natural language queries like “what is my predicted market share for 2020?”, “What was my marketing spend in 2017?” etc.
In fact, chatbots today are capable of efficient data abstraction from multiple sources like your existing LOB, CRM systems and are also capable of integrating within third-party messaging applications like Skype For Business, Skype, Slack and many more. This helps users get actionable insights through a conversational interface without having to access the BI application every single time. So, even if a marketing exec wishes to know at 2 AM how well a trade promotion fared, a BI chatbot can get the info to his fingertips. This convenience goes a long way in fostering a culture of analytics in a company.
NLP in BI helps translate analytical results into common language, making data more accessible to a wider audience. Because of NLP, users spanning various business functions of an organization, such as marketing, sales, and finance, can easily access the desired information from the BI system, without intervention from highly technical data personnel. Another way NLP can be used to make data accessible to a wider audience is through the implementation of a Natural Language Generator (NLG). NLG translates the visual analytical output into descriptive or narrative text helping individuals with special needs such as visual impairment and visual processing deficits easily work with BI systems. NLP has democratized data, making it extremely easy for just about everyone to access data insights quickly and efficiently.
An important application of NLP in BI is the harnessing of unstructured data. According to IDC, 80 percent of worldwide data will be unstructured by 2025. And most of this data is not completely leveraged by enterprises yet. Due to the data explosion from digital and social media and IoT enabled devices, unstructured data is set to increase at an unprecedented rate in the coming years. NLP helps in effective analysis of this data and unlocks its value.
Search is an important functionality in any BI system. NLP enhances BI search by understanding the intent behind users’ queries and showing highly relevant results. With NLP, users can get a Google-like and consumerized BI experience. NLP-based search furthers the dialogue after a query and avoids the need for users to rephrase their questions.
BI data should ideally be accessible to everyone, something that is a constant challenge. Employees may find the complex BI software and layered interface a hassle to navigate, in turn affecting the employee adoption rate of BI systems. NLP can go a long way in addressing these issues, making data easily accessible to all and driving BI adoption rates.