Business Intelligence (BI) has long been the cornerstone of data-driven decision-making in organizations. However, traditional BI tools often come with steep learning curves and require specialized knowledge to navigate complex data visualizations, dashboards, and reporting systems. This creates a significant barrier for non-technical users who need timely insights but lack the expertise to extract them from vast datasets. The result? Valuable data remains underutilized, and decision-making processes become bottlenecked by dependence on data specialists.
Consider a marketing manager who needs to quickly analyze campaign performance across various channels. With traditional BI tools, they might spend hours trying to pull data from multiple sources, create relevant visualizations, and interpret the results. If they lack the technical skills or access to a dedicated BI team, these tasks become even more challenging, leading to delayed decisions and missed opportunities. This scenario is all too common in many organizations, where the gap between data accessibility and user capability hampers efficiency and responsiveness.
Natural Language Processing (NLP) is revolutionizing the way users interact with BI tools, breaking down barriers and making data analysis accessible to everyone, regardless of their technical expertise.
Language serves as the main tool for human communication, allowing us to express emotions and share information. Humans intuitively grasp the ‘context’ and ‘meaning’ behind words, but computers don’t naturally possess this ability. To computers, text is merely a sequence of characters, and spoken words are just sounds. Unlike humans, computers cannot extract ‘context’ from content. Given the growing significance of computer technology in our daily lives, it is essential to enable computers to understand natural language. This necessity has given rise to the field of Natural Language Processing (NLP).
Natural Language Processing (NLP) is a branch of computer science and computational linguistics that manages the interactions between human language and computers, enabling humans to communicate with machines as they would with each other. NLP arises from the intersection of machine learning, artificial intelligence, and linguistics. The essence of NLP is to allow computers to grasp the ‘context’ and subsequently the ‘intent’ behind any text or spoken communication.
Examples Of Natural Language Processing
Common examples of NLP in use today include technologies like Apple’s Siri, Amazon’s Alexa, and Google Assistant or Google Home. These systems recognize speech patterns to interpret the ‘meaning’ of messages. They are increasingly employed for both professional and personal tasks, responding to user queries and requests with notable accuracy. Another intriguing application of NLP is in parsing messages, emails, and calendar invites to identify meeting invitations, notifications, and reminders. Modern smartphones powered by Android and iOS utilize this feature, as does Gmail, which has been leveraging this technology for some time. Additionally, when using Google search, the system provides suggestions based on the keywords entered, demonstrating how NLP enables computers to seemingly ‘read’ our minds.
The Impact of Natural Language Processing on Business Intelligence
Business Intelligence (BI) encompasses the strategies and technologies used to analyze business data, aiding in informed decision-making. It enhances the understanding of the global market and offers insights into company operations. Nowadays, data insights are essential for decision-making, pushing organizations to rely on more than just intuition or gut feelings.
Natural Language Processing (NLP) has had a significant impact on business intelligence (BI) by making it more accessible.
Unstructured Data
A significant application of NLP in BI is the utilization of unstructured data. According to IDC, 80 percent of global data will be unstructured by 2025, and much of this data remains underutilized by businesses. With the surge of data from digital and social media, as well as IoT devices, the amount of unstructured data is expected to grow rapidly in the coming years. NLP facilitates the effective analysis of this data, unlocking its potential value.
Real-time business intelligence reporting
Historically, analytics and business intelligence have primarily been utilized by top executives for strategic decision-making. These leaders receive periodic business intelligence reports, which are prepared and curated by data analysts.
These reports, which include visualizations and analysts’ comments and recommendations, simplify the process for business leaders to gain critical insights and make informed decisions. However, manually generating such detailed yet straightforward reports for every employee in the organization, including those in operational roles, is impractical.
Unlike top executives, operations staff must rely on raw data, tables, and charts to interpret real-time analytics and make decisions. They need to stay informed about data that changes daily or even hourly.
Creating reports or explaining dashboards frequently is taxing for both analysts and executives. Consequently, the organization may fall short of achieving operational excellence.
Natural language generation can convert the information in business intelligence dashboards and reports into easily understandable written narratives. These dashboards present the most critical real-time data related to the enterprise’s operations.
For instance, a logistics supervisor can use a real-time dashboard to monitor the location and performance of delivery vehicles. With the right information, they can identify late deliveries and optimize routes to expedite the process.
By utilizing these dashboards and reports, other operations personnel can make informed decisions in response to real-time changes. Providing every employee with real-time information in a straightforward format enhances the enterprise’s overall operational agility.
Function-based customization of business intelligence reporting
With the help of AI and natural language generation (NLG), data-driven narratives can be crafted and customized for various functions. These tailored reports enable employees in different roles to comprehend business intelligence within the specific context of their functions. This customization boosts the adoption of BI tools across the organization, ultimately enhancing overall business performance.
For example, while the head of sales can extract actionable insights from an overall enterprise-wide sales performance report, regional sales managers may not find this data as useful. They need reports that concentrate on sales performance in their specific region, including a detailed analysis of the performance of individual sales employees in that area.
Business intelligence reporting powered by natural language generation can produce customized performance reports or dashboards. Likewise, managers of different bank branches can receive branch-specific performance reports, providing insights relevant to their operations.
Companies are recognizing the benefits of democratizing business intelligence and the pivotal role of natural language generation in this process. Consequently, the adoption of NLG is increasing.
Gartner forecasted that by 2020, natural language generation would be a standard component in 90% of BI and analytics platforms. Therefore, to remain competitive and relevant in a data-centric business environment, companies will need to embrace business intelligence reporting that utilizes natural language generation.
NLP Chatbots in Business Intelligence
Typically, accessing data with most BI systems involves logging into the application, creating the desired report, and navigating multiple dashboards to filter insights. This lengthy process and the need for some technical knowledge can reduce user adoption of BI tools.
This is why companies frequently need to hire skilled data scientists and analysts to extract insights from BI systems. However, envision a scenario where required insights could be obtained simply by asking questions in natural language.
An increasing number of global companies are now using Business Intelligence Chatbots that can understand natural language and perform complex BI-related tasks. This has significantly simplified data consumption for business users. By integrating NLP-enabled chatbots with existing BI systems such as Power BI, SAP, Oracle, and others, users can access data through natural language queries like “What is my predicted market share for 2020?” or “What was my marketing spend in 2017?”
Today’s chatbots can effectively abstract data from various sources, such as existing line-of-business (LOB) and CRM systems, and integrate with third-party messaging applications like Skype for Business, Skype, Slack, and others. This allows users to obtain actionable insights through a conversational interface without needing to access the BI application repeatedly. For example, if a marketing executive wants to know how a trade promotion performed at 2 AM, a BI chatbot can provide the information instantly. This level of convenience significantly promotes a culture of analytics within a company.
Natural Language Analytics
NLP makes analytical results comprehensible by translating them into everyday language, thus broadening data accessibility. Thanks to NLP, users across various departments such as marketing, sales, and finance can effortlessly retrieve information from the BI system without needing assistance from highly specialized data experts. Additionally, Natural Language Generation (NLG) enhances accessibility by converting visual analytical outputs into descriptive or narrative text, which benefits individuals with visual impairments or processing difficulties.
NLP-Based Search
Search functionality is crucial in any BI system. NLP improves BI search by comprehending the intent behind user queries and delivering highly relevant results. With NLP, users can experience a search experience similar to Google, offering a more consumer-friendly interface. NLP-based search also continues the interaction after a query, eliminating the need for users to rephrase their questions.
Conclusion
Ideally, BI data should be accessible to everyone, though this remains a persistent challenge. Employees might struggle with the complexity of BI software and its intricate interface. NLP can significantly alleviate these issues, thereby improving BI adoption rates. You can learn more about Technoforte’s BI capabilities here.
Technoforte is an IT services and solutions company working in various industry verticals since two decades. We offer Managed IT Services. Also read about our IT staff augmentation services here!
