For years, businesses have been deriving highly valuable insights by leveraging their structured data. However, in the past few years, the surge in the unstructured enterprise data is incredible. It has become an asset with the maximum potential to drive business decisions.
Given the recent disruption in the light of the world-wide pandemic businesses from every industry and of every scale has adopted a new working model. This model is highly driven by emails, research documents, social media posts, voice, and videos. These are vital resources that deliver significant value to businesses that cannot be overlooked. However, the unstructured nature and diversity in form and sources make it highly complex to leverage this data.
To this point, NLP (Natural Language Processing) comes into play. NLP in simple words is a field of AI that has dramatically transformed the way unstructured data is processed and analyzed. This game-changing technology fuels the decision-making for present-day enterprises and plays a vital role to help them survive and thrive.
In the modern business model, analytics has reached all the corners of an enterprise propelling the implementation of advanced analytics technologies like NLP. It not only increases productivity but also accelerates and enhances the decision making process and decision quality. Already, over 50% of the internal queries in a business are either generated automatically or through natural language (voice). Experts believe that the global NLP market is likely to grow with a CAGR of over 21% by 2025. It is likely to embrace much more than half of the enterprise employees including in many user classes. NLP is set to revolutionize enterprise BI and unearth multiple concealed unstructured resources adding new dimensions to decision-making.
√ Capitalizing on interactions
NLP has already found its place in the customer-facing side of the businesses in the form of chatbots. However, there are other ways in which this technology can be used to streamline customer services. It empowers businesses with the ability to analyze the shared communications in real-time. It can reveal the complex behavioral patterns and common pain points that can be targeted to boost sales. This information is crucial in making futuristic decisions and driving R&D work.
NLP is also a powerful tool to extract competitive intelligence and stay abreast with the latest and future trends and opportunities. Companies can harvest information through social media feedback, reviews, and ratings to understand their customers' demographics. Using NLP enables extracting deep insight into sentiments and impact of events like a product launch or a specific marketing strategy.
In addition to influencing decisions from external factors, NLP transforms the internal processes. For instance, using NLP in recruitment helps in quick scanning of candidates' profiles and classification based on skills and personal traits. It eliminates human bias and ensures fair and quality decisions being taken.
√ Enhancing the value of documentation
Unstructured data forms nearly 80% of the data assets for an enterprise in general. A major section of this data is in the form of documents comprising reports, contracts, policies, and others. Often the insights hidden in these documents are left unused. NLP helps businesses to efficiently and effectively classify these documents and extract valuable insights with much lesser time and effort invested in them.
NLP is a great tool to demystify the information in the enterprise documentation that is imperative in streamlining workflow. It revitalizes document processing and analysis, especially for document-hefty industries such as aviation.
For instance, every pilot can report a common recurring deficiency or issue in different words which may be inconsistent with the mechanical engineering jargon. NLP can efficiently process these reports and extract the underlying information in a more digestible form. By combining this information with metadata, NLP can convey the issue in the most exact and actionable form. This helps in making important decisions in proactive maintenance and boosting operational performance.
√ Making virtual assistance more meaningful
Interactions with customers are one of the major driving forces in enterprise decision making. It delegates valuable information that drives marketing and production decisions and enables companies to provide enhanced customer experience.
This fact is already established with virtual assistance such as Siri, Alexa, and google homes being ubiquitous. These are transforming everyday interaction between AI and humans. The impact of AI and NLP is also apparent in the way companies are responding to customers' requests and queries. As humans are getting accustomed to virtual assistance they expect a similar work environment calling for further enhancement of computer-human interaction.
Incorporating technology-based assistance to the users boosts the collaboration within the enterprise and fuel information discovery and exchange. Thanks to AI and NLP, more than 80% of the business interactions in the past year including the ones with the customers and among employees have been accomplished with no human interaction.
Simple support portals and search-based intranets can be transformed into powerful virtual assistance by combining AI, NLP, ML, content processing. It can elevate the interaction and knowledge-seeking experience between partners, employees, and customers.
Optimizing document searches
As mentioned earlier, enormous enterprise information is stored in the form of documents. It is important for enterprises to be able to search, sort, and process these documents based on themes and not just keywords. Consider the staffing industry. Every organization is looking to clinch the best talent before their competitors take them away. Given the enormity of data and diversity of sources, it is likely to lose track and eventually the candidate matching best to the requirements.
Combining the potential of AI technologies including ML and NLP can present a great solution to the problem. This combination can yield valuable information about the potential candidates by accurately and efficiently matching their skills for the given job. It can quickly compare and check resumes and other documents for authenticity. It accelerates the recruitment process and your decision to hire the right candidate. You can scan millions of CVs and job postings to get the best man for your organization with a much-reduced cost of acquisition.
√ Analyzing sentiments
Sentiment analysis is a big breakthrough that has revolutionized customer experience in every industry vertical. It helps companies to dig deep into the emotions of customers to understand their needs and expectations. This information is the biggest weapon to battle the ever-changing demands of the customers and delivering them with the most comfortable experience.
Sentiment analysis or opinion mining is an implementation of NLP that helps identify the opinion or emotions concealed in a piece of content or subject matter. Companies are using technology to draw valuable and actionable information from emails, reviews, social media posts. It enables organizations to discover the feelings of customers and employees regarding products, services, and processes. Utilizing this valuable information facilitates decisions and outcomes that would be unreachable otherwise.
NLP fused with AI, ML, text, and linguistic analytics can recognize positive and negative sentiments. It can even classify sentiments in more granulated form as very positive, positive, neutral, negative, very negative. Knowing the sentiments gives deeper insight into the impact of an event and a marketing strategy. It improves customer retention, competitive intelligence, and enriches product development decisions.
√ Strengthening text-based predictions
Predictions and forecasts are among the key drivers of high-level enterprise decision-making. Until recently these factors have been evaluated based on numbers. However, NLP allows businesses to harness the predictive power of words and incorporate them into decisions conveniently at both macro and micro levels.
Consider a financial institute looking to generate a credit score of a customer. Using NLP the institute can leverage information from his/her social media accounts, geographical locations, browsing history, profile scan, and assessing the digital footprints. Such a detailed analysis of the customer increases the credibility of the decision of his/her eligibility for the loan. This helps in making a precise decision even if the customer does not have sufficient banking records.
The same can be applied at bigger levels. For instance, a bank with millions of debtors can apply NLP on high-risk segments to determine the recovery rate of funds in a given period. This can help in risk mitigation and making strategic decisions for recovery and load grants.
√ Take away
Decisions, predictions, and analytics is becoming sophisticated at an exponential rate. It calls for exceptional ability to process unstructured data in real-time with optimum accuracy. Organizations are increasingly acknowledging this revolutionary shift and investing in NLP based solutions. These solutions are enabling a deep understanding of the diverse unstructured content and taking the BI and analytics up multiple notches.
An increasing number of organizations are tempted towards this technology as it nearly eliminates the guesswork and intuitive decisions and delivers unprecedented accuracy in planning and forecasting. It is also boosting workforce confidence during the disruption by enhancing the people's skill and their understanding of various critical metrics. This is making every user and employee a valuable business asset thereby creating an inclusive ecosystem that encourages collaborative working.
Most NLP solutions are cloud-based which makes them extremely resource-light and highly powerful and enticing for organizations. Continuous evolution in technology is further solidifying the NLP capabilities in deciphering the voice and videos. It is delivering enterprises with the tiniest nuance from an interaction's data.
