Contemporary businesses operate in dynamic environments requiring rapid adaptation to achieve goals and maintain competitiveness. Existing data platforms often fall short by emphasizing tools over alignment with business needs, resulting in inefficiencies and delays. To address this gap, I propose the Business Semantics Centric, AI Agents Assisted Data System (BSDS), a holistic system that integrates architecture, workflows, and team organization to ensure data systems are tailored to business priorities rather than dictated by technical constraints. BSDS redefines data systems as dynamic enablers of business success, transforming them from passive tools into active drivers of organizational growth. BSDS has a modular architecture that comprises curated data linked to business entities, a knowledge base for context-aware AI agents, and efficient data pipelines. AI agents play a pivotal role in assisting with data access and system management, reducing human effort, and improving scalability. Complementing this architecture, BSDS incorporates workflows optimized for both exploratory data analysis and production requirements, balancing speed of delivery with quality assurance. A key innovation of BSDS is its incorporation of the human factor. By aligning data team expertise with business semantics, BSDS bridges the gap between technical capabilities and business needs. Validated through real-world implementation, BSDS accelerates time-to-market for data-driven initiatives, enhances cross-functional collaboration, and provides a scalable blueprint for businesses of all sizes. Future research can build on BSDS to explore optimization strategies using complex systems and adaptive network theories, as well as developing autonomous data systems leveraging AI agents.

Key Point Analysis (KPA) aims for quantitative summarization that provides key points (KPs) as succinct textual summaries and quantities measuring their prevalence. KPA studies for arguments and reviews have been reported in the literature. A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence. Recent abstractive approaches still generate KPs based on sentences, often leading to KPs with overlapping and hallucinated opinions, and inaccurate quantification. In this paper, we propose Prompted Aspect Key Point Analysis (PAKPA) for quantitative review summarization. PAKPA employs aspect sentiment analysis and prompted in-context learning with Large Language Models (LLMs) to generate and quantify KPs grounded in aspects for business entities, which achieves faithful KPs with accurate quantification, and removes the need for large amounts of annotated data for supervised training. Experiments on the popular review dataset Yelp and the aspect-oriented review summarization dataset SPACE show that our framework achieves state-of-the-art performance. Source code and data are available at: https://github.com/antangrocket1312/PAKPA

Technology has a purpose to address the issues of our world and perfect our lives as far as possible. Like every January, we are at the doorstep of another important year that promises bigger developments across the tech lanes. While 2022 witnessed many new platforms and products, 2023 will testify to their worth amidst the growing concerns of a recession, says Yash Mehta, an IoT and big data science specialist. In this post, I pick my top 6 technologies that shall dominate the narrative for 2023. IoT had a great stint in 2022 which is most likely to continue this year. Till now, we have seen successful case studies in home automation, healthcare, vehicles etc.

Decision intelligence (DI) is a new field aimed at transforming and improving the way business decisions are made. It uses technology to support, augment or automate business decisions. DI combines technologies such as machine learning, optimization, analytics and process automation. It helps with business decisions by leveraging three modes: decision support, where the machine provides analytics and other tools to assist human decision-making; decision augmentation, where the machine suggests decisions for a human to review and accept; and decision automation, where the whole process is automated and the machine executes its decisions autonomously, under human supervision. Implementing DI provides significant business impacts to organizations.

Today's world is moving with constant changes in different sectors, including business and technology. According to the changing trend, you need to update your skills regardless of your profession. You need to adapt to the latest changes taking place at your work and learning new skills can enhance your career growth. Also, you can boost up many new career chances with this. If you're in a technology profession or planning to become a professional techie then you must need to grasp some important skills. The skills that are most in-demand and help to stay competitive can change your future.

We propose a model architecture Although knowledge graphs (KGs) and ontologies have that combines the advantages of graph convolutional networks been exploited successfully for data integration [Trivedi (GCNs) [Kipf and Welling 2017] and siamese networks et al. 2018; Azmy et al. 2019], entity matching involving [Bromley et al. 1993] to address the entity-matching structured and unstructured sources has usually been task. GCNs are a type of graph neural network that shares performed by treating records without explicitly taking filter parameters among all the nodes, regardless of their location into account the natural graph representation of structured in the graph. Our Siamese Graph Convolutional Network sources and the potential graph representation of unstructured (S-GCN) incorporates two identical GCNs, as shown data [Mudgal et al. 2018; Gschwind et al. 2019].

As artificial intelligence re-writes business models, how will its application and adoption revolutionize business and commerce further? From the production and marketing era to the relationship and intelligence era, business models have been evolving over the centuries. Over the years, the rise of artificial intelligence (AI) has fundamentally transformed the very meaning of ideas, innovation, and inventions. As a result, business models are evolving further. As we witness businesses across industries undergo a profound and dramatic shift in the relative balance of intelligence power, AI applications and adoption are offering each business entity as many new opportunities as it does challenges.

Since digital marketing has become an integral part of a business' marketing strategy, companies have been fighting tooth and nail to claim the top spot in the digital world. But compiling and comprehending huge amount of customer data coming in every moment is no cakewalk. Evidently, digital incompetence has been a major reason behind a large number of small startups failing in their first four years. But does it mean that this disadvantage is going to be around for years? Certainly not, because Machine Learning and AI solutions are here and they are transforming the way people run their businesses.

Chatbots are artificially intelligent platforms that interact and communicate with customers on behalf of a business entity. They are making a difference, developing a different user experience and raising customer retention and loyalty. With the latter being vital to the running of any business, it has also become vital to have chatbots personalize their services for customers. Personalization is the best way for bots to help customer loyalty grow. It is also a feature that is quite difficult to achieve.

When you think of the rapid evolution of technology, the first thing that comes to mind is likely self-driving cars or artificial intelligence, not the real estate industry. But just because the real estate industry is not at the forefront of the technological revolution, it doesn't mean there aren't exciting new developments happening in the sector – and some of them can benefit you as a real estate investor. Nearly every industry has benefited from the advent of "big data," but what does that really mean for real estate? Together, these factors mean we're now able to access and analyze higher volumes of data more quickly. As a result, real estate data companies can now deliver more insightful information to the investment community faster, allowing investors to make better decisions.