“Apicture is worth a thousand words” is an oft-repeated cliché, but organizations worldwide rely on visuals and other interactive elements to build a narrative around their data. These include reporting business intelligence (BI) solutions and dashboards. However, at times, this overwhelming number of dashboards filled with incomplete information fail to generate insights, trends, or patterns — the veritable “why” behind the data.
The utility of BI dashboards spans multiple sectors — finance, customer services, marketing, management, sales, and procurement, to name a few. However, despite their utility, the adoption rates have stagnated persistently and now stand at a mere 30%, with only 10% of the executives believing that their company is analytically mature. To overcome these shortcomings, organizations aim to develop self-supporting platforms with twofold purposes — real-time analysis on-the-go and delivering actionable insights to enhance decision-making
Organizations need to introduce artificial intelligence (AI) to the BI solution framework to ensure that the needs of a self-supporting platform are met. With AI in the picture, organizations can streamline their operations, increase efficiency, improve accuracy, and predict market trends. This article will focus on how AI assists organizations in transforming the process of analyzing and visualizing BI data — from traditional dashboards to intuitive decision boards.
The Utility of AI in Business Intelligence
The term “AI” dates back to 1956, but AI began gaining prominence in the 21st century. By mimicking human cognitive and learning processes, the applications of AI started with automating mundane and routine tasks — such as data collection, analysis, and customer services — to e-commerce, cybersecurity, education, finance, healthcare, military, and so on. Today, ChatGPT, an AI chatbot, turns heads by resolving queries and responding accurately to questions across domains.
Organizations need to make better, timely, accurate, and consistent decisions, which affect business operations. Integrating AI into the BI frameworks helps organizations analyze large datasets. These datasets are disparate and spread across the organization. AI assists them in quantifying data without human intervention, allowing organizations to enhance their decision-making and make accurate forecasts about future trends.
Furthermore, AI can handle anomaly detection, perform data processing, provide in-depth analysis, and manage trend recognition. For organizations that need more qualified resources in data science, AI has emerged as a game-changer since it enables line-of-business (LOB) users to discover data-driven insights.
And it is not just the IT specialists who derive significant benefits from AI. It also enables other users, that is, non-IT specialists, to mine data for valuable and accessible insights by operating independently of the IT setup but confining itself within an organization’s protected and managed IT architecture. AI also helps personalize and customize user data insights as it ushers in the power of natural language and learns from their interactions.
For instance, if users ask queries in plain language, they receive responses in English. Such a scenario is possible as AI uses a natural language interface (NLI), which combines natural language processing (NLP) and natural language generation (NLG). This allows users to get a better understanding of their data with the help of simple exploration tools.
Beyond making sense of data, the algorithm also delivers compelling, condensed, and explicable data by suggesting visualization, dashboards, and other easily understandable metrics. The implementation of AI in BI helps in automating the process of data preparation and cleaning. With AI handling significant data analysis tasks, it frees up time for IT specialists and LOBs to focus on critical and productive tasks.
Organizations are searching for answers regarding data. In combination with AI, BI enables them to upload datasets, unearth facts about data, and generate outstanding visualizations in a matter of minutes. Not only does AI provide answers to the most pressing questions, but it also suggests ways to explore data for additional insights. While BI solutions help explain a problem, AI helps find a resolution.
AI-Driven Visualization in BI
Let us examine what a typical dataset looks like. It contains numerical elements, such as numbers, amounts, measures, etc. With Power BI embedded with AI capabilities built into the platform, properly analyzing this numerical information unearths new and critical insights for organizations. Despite the cumbersome nature of analyzing non-numerical data, the AI tools in Power BI examine text data and uncover new insights. The various AI visuals used in Power BI are smart narrative, key influencers, Q&A, and decomposition tree.
With smart narrative, users can now perform driver analysis on the go. When we observe a spike or dip in a business KPI, smart narrative helps us understand the drivers. It will also help us explain the increase or decrease in KPI in detail by slicing and dicing the entire dataset across multiple dimensions. Waterfall charts are created automatically and will get updated dynamically as per the interest.
The primary function of key influencers is to analyze data, rank the necessary variables, and highlight them as major deciding factors. Key influencers help make sense of variables that influence a specific measure. Key influencers also undertake data analysis, rank the essential variables, and highlight those variables as major controlling elements. In addition, they also compare the relative weights of these aspects, enabling organizations to create visualizations while comprehending the influences on them and the reasons behind their appearance.
The fastest way for users to get an answer from data is by searching all over data using natural language, which is the task of Q&A, a fun and interactive feature in Power BI. It allows organizations to explore their data in their own words using natural language. The Power BI Desktop enables report designers to use the Q&A feature to analyze data and create visualizations.
Organizations need to see data in various dimensions, and the decomposition tree feature in Power BI allows similar functionality. It collects data automatically, enabling organizations to drill it down into multiple dimensions in any sequence. It is also valuable for discovering other dimensions that must be explored depending on a particular criterion since it is an AI visualization tool. The decomposition tree is helpful for organizations during ad hoc investigation and root cause analysis.
AI-driven BI solutions have emerged as a new and valuable tool to users at all levels of organizations. With such solutions, users can analyze and gain valuable insights about the data, ushering in an era of data democratization, which helps organizations accelerate the process of finding the correct answers to critical questions around data.
Al in BI has significantly transformed data analysis and enhanced decision-making processes in organizations. While there are always ethical concerns about the usage of AI, especially related to job losses, AI exists to complement organizations and guide them in decision-making, thereby saving time and resources. Eventually, this leads to more adoption of BI solutions and assists in analytical maturity of the organization.
uce artificial intelligence (AI) to the BI solution framework to ensure that the needs of a self-supporting platform are met. With AI in the picture, organizations can streamline their operations, increase efficiency, improve accuracy, and predict market trends. This article will focus on how AI assists organizations in transforming the process of analyzing and visualizing BI data — from traditional dashboards to intuitive decision boards.