Large Language Models (LLMs) have shown promise in enabling natural language interfaces for structured data querying through text-to-SQL generation. However, their application in real-world Business Intelligence (BI) contexts remains limited due to semantic hallucinations, structural errors, and a lack of domain-specific evaluation frameworks. In this study, we propose a Fact-Consistency Evaluation Framework for assessing the semantic accuracy of LLM-generated SQL outputs using Exaone 3.5--an instruction-tuned, bilingual LLM optimized for enterprise tasks. We construct a domain-specific benchmark comprising 219 natural language business questions across five SQL complexity levels, derived from actual sales data in LG Electronics' internal BigQuery environment. Each question is paired with a gold-standard SQL query and a validated ground-truth answer. We evaluate model performance using answer accuracy, execution success rate, semantic error rate, and non-response rate. Experimental results show that while Exaone 3.5 performs well on simple aggregation tasks (93% accuracy in L1), it exhibits substantial degradation in arithmetic reasoning (4% accuracy in H1) and grouped ranking tasks (31% in H4), with semantic errors and non-responses concentrated in complex cases. Qualitative error analysis further identifies common failure types such as misapplied arithmetic logic, incomplete filtering, and incorrect grouping operations. Our findings highlight the current limitations of LLMs in business-critical environments and underscore the need for fact-consistency validation layers and hybrid reasoning approaches. This work contributes a reproducible benchmark and evaluation methodology for advancing reliable natural language interfaces to structured enterprise data systems.

In the field of business data analysis, the ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. Conversely, Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), offer significant potential in pattern recognition and predictive analytics but can lack the precision necessary for specific business applications. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of LLMs in generating actionable business insights.

The present-day business landscape necessitates novel methodologies that integrate intelligent technologies and tools capable of swiftly providing precise and dependable information for decision-making purposes. Contemporary society is characterized by vast amounts of accumulated data across various domains, which hold considerable potential for informing and guiding decision-making processes. However, these data are typically collected and stored by disparate and unrelated software systems, stored in diverse formats, and offer varying levels of accessibility and security. To address the challenges associated with processing such large volumes of data, organizations often rely on data analysts. Nonetheless, a significant hurdle in harnessing the benefits of accumulated data lies in the lack of direct communication between technical specialists, decision-makers, and business process analysts. To overcome this issue, the application of collaborative business intelligence (CBI) emerges as a viable solution. This research focuses on the applications of data mining and aims to model CBI processes within distributed virtual teams through the interaction of users and a CBI Virtual Assistant. The proposed virtual assistant for CBI endeavors to enhance data exploration accessibility for a wider range of users and streamline the time and effort required for data analysis. The key contributions of this study encompass: 1) a reference model representing collaborative BI, inspired by linguistic theory; 2) an approach that enables the transformation of user queries into executable commands, thereby facilitating their utilization within data exploration software; and 3) the primary workflow of a conversational agent designed for data analytics.

Collaborative Business Analysis (CBA) is a methodology that involves bringing together different stakeholders, including business users, analysts, and technical specialists, to collaboratively analyze data and gain insights into business operations. The primary objective of CBA is to encourage knowledge sharing and collaboration between the different groups involved in business analysis, as this can lead to a more comprehensive understanding of the data and better decision-making. CBA typically involves a range of activities, including data gathering and analysis, brainstorming, problem-solving, decision-making and knowledge sharing. These activities may take place through various channels, such as in-person meetings, virtual collaboration tools or online forums. This paper deals with virtual collaboration tools as an important part of Business Intelligence (BI) platform. Collaborative Business Intelligence (CBI) tools are becoming more user-friendly, accessible, and flexible, allowing users to customize their experience and adapt to their specific needs. The goal of a virtual assistant is to make data exploration more accessible to a wider range of users and to reduce the time and effort required for data analysis. It describes the unified business intelligence semantic model, coupled with a data warehouse and collaborative unit to employ data mining technology. Moreover, we propose a virtual assistant for CBI and a reference model of virtual tools for CBI, which consists of three components: conversational, data exploration and recommendation agents. We believe that the allocation of these three functional tasks allows you to structure the CBI issue and apply relevant and productive models for human-like dialogue, text-to-command transferring, and recommendations simultaneously. The complex approach based on these three points gives the basis for virtual tool for collaboration. CBI encourages people, processes, and technology to enable everyone sharing and leveraging collective expertise, knowledge and data to gain valuable insights for making better decisions. This allows to respond more quickly and effectively to changes in the market or internal operations and improve the progress.

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AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, announced that registration is now open for its second Semantic Layer Summit. This unique event brings together today's top minds in the data, analytics, artificial intelligence (AI), and business intelligence (BI) industries to discuss the evolution of the semantic layer technology category. "The Semantic Layer is a critical, but often poorly understood, component of the rapidly evolving modern data stack" "The Semantic Layer is a critical, but often poorly understood, component of the rapidly evolving modern data stack," said David Mariani, co-founder and CTO of AtScale and conference chair for the Semantic Layer Summit. "We are thrilled to be bringing together a cross-section of industry practitioners and technology visionaries to share perspectives and give practical advice." The inaugural Semantic Layer Summit was held in 2022, drawing over 8,000 registrants and featuring speakers from across the industry.

The world of video analytics has come a long way in the past few years. What started as a complementary security surveillance technology, has evolved into a critical decision-making solution for stakeholders beyond law enforcement and public safety. Powered by AI and deep learning, today's sophisticated video analytics have far-reaching and impactful applications, from accelerating investigations for criminal or commercial claims to increasing operational productivity across industries and end users, delivering cost efficiency, enhanced safety, and elevated experiences. These applications only continue to gain strength, and in this article, I'll walk you through some examples of diverse industries innovatively supporting operational and business decision making with the power of data-driven intelligence derived from video analytics. But first, a quick word on how it works: Video intelligence software detects and extracts objects in video, identifies each object based on trained Deep Neural Networks, and classifies each object to enable intelligent video analysis through search and filtering, alerting, data aggregation, and visualisation capabilities.

Cloverleaf Analytics, the leading provider of Insurance Intelligence solutions, today announced that Michael Schwabrow has joined the company as EVP of Sales and Marketing. Reporting to Cloverleaf President Robert Clark, Schwabrow will be responsible for Cloverleaf's go-to-market strategy and for cultivating relationships with insurers to maximize the value of Cloverleaf's Insurance Intelligence platform which includes Business Intelligence (BI), Artificial Intelligence (AI)/Machine Learning (ML), Natural Language Processing (NLP), and other technologies. Schwabrow has a long track record of collaborating with carriers and MGAs to attain meaningful digital transformation with immediate and long-term business results. With Cloverleaf, he will help carriers and MGAs to understand and unleash the real-world value of Insurance Intelligence across core business operations. "The insurance industry is like a big family, and our community is at a critical juncture for how to make smarter and more efficient decisions to reduce risk, improve product offerings, and strengthen the overall health of carrier books of business," said Schwabrow.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Entropy is a scientific concept associated with a state of disorder, randomness, or uncertainty. It is widely used in diverse fields, from classical thermodynamics to statistical physics and information theory.