Creating data reports is time-consuming, as it requires iterative exploration and understanding of data, followed by summarizing the insights. While large language models (LLMs) are powerful tools for data processing and text generation, they often struggle to produce complete data reports that fully meet user expectations. One significant challenge is effectively communicating the entire analysis logic to LLMs. Moreover, determining a comprehensive analysis logic can be mentally taxing for users. To address these challenges, we propose ReSpark, an LLM-based method that leverages existing data reports as references for creating new ones. Given a data table, ReSpark searches for similar-topic reports, parses them into interdependent segments corresponding to analytical objectives, and executes them with new data. It identifies inconsistencies and customizes the objectives, data transformations, and textual descriptions. ReSpark allows users to review real-time outputs, insert new objectives, and modify report content. Its effectiveness was evaluated through comparative and user studies.

Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth knowledge of the dataset and expertise in data analysis techniques. Not being familiar with either can create obstacles that make the process time-consuming and overwhelming for data analysts. To address this issue, we introduce InsightPilot, an LLM (Large Language Model)-based, automated data exploration system designed to simplify the data exploration process. InsightPilot automatically selects appropriate analysis intents, such as understanding, summarizing, and explaining. Then, these analysis intents are concretized by issuing corresponding intentional queries (IQueries) to create a meaningful and coherent exploration sequence. In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts and simplifies the exploration process for users. By employing an LLM to iteratively collaborate with a state-of-the-art insight engine via IQueries, InsightPilot is effective in analyzing real-world datasets, enabling users to gain valuable insights through natural language inquiries. We demonstrate the effectiveness of InsightPilot in a case study, showing how it can help users gain valuable insights from their datasets.

SESAMm, a leader in natural language processing (NLP), a field of artificial intelligence, announced the close of a Series B2 funding round of €35 million (USD 37 million) to accelerate its ambitious growth and global expansion plans. "Since we started working with SESAMm as investors and clients over two years ago, we've been impressed with both the company's growth and the advanced analytics that have supported our deal sourcing, diligence, and portfolio company value creation efforts" Securing this funding will enable SESAMm to further expand into U.S. and Asian markets, support technology development to generate AI-powered ESG and sentiment analytics, and hire key talent across sustainability, technology, sales, and marketing. The Series B2 round was co-led by Elaia, a deep tech VC firm, and Opera Tech Ventures, the venture capital arm of BNP Paribas (BNP). Other participating companies include asset manager Unigestion, Raiffeisen Bank International's (RBI) venture capital entity Elevator Ventures, AFG Partners, CEGEE Capital, and historical backers, including Carlyle (CG) and New Alpha Asset Management, who participated in the previous Series B1 round. This latest round brings the total funding raised to €50 million.

NEXT50, an Abu Dhabi based-technology company, on Sunday launched Platform50, a one-stop-shop solution for organisations to maximise business value and address tomorrow's challenges. The announcement was made at Next50's inaugural executive day – which spotlighted the latest in state-of-the-art technologies including artificial intelligence (AI) and real-world applications accelerating a data-driven future in three key industries – mobility, logistics, and utilities. The Next50 executive day saw the attendance of key industry leaders in the UAE. With AI is set to contribute up to $15.7 trillion to the global economy in 2030 and $320 billion to the Middle East alone, Platform50 is designed to support organisations as they accelerate the adoption of AI, advanced analytics-based solutions, and automation to meet their growth and sustainability goals. The launch aligns with the UAE National Innovation Strategy and the UAE's Fourth Industrial Revolution Strategy which aims to contribute to the national economy by advancing innovation and future technologies, with AI expecting to contribute 13.6 per cent of the UAE's GDP by the end of the decade based on a report published by the World Economy Forum.

Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries. Nevertheless, interactive data analysis is a demanding process, especially for novice data analysts. When exploring large and complex SQL databases from different domains, data analysts do not necessarily have sufficient knowledge about different data tables and application domains. It makes them unable to systematically elicit a series of topically-related and meaningful queries for insight discovery in target domains. We develop a NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions. The system adopts a data-driven approach to suggest semantically relevant and context-aware queries for application domains of users' interest based on their query logs. Also, the system helps users organize query histories and results into a dashboard to communicate the discovered data insights. With a comparative user study, we show that our system can facilitate a more effective and systematic data analysis process than a baseline without the recommendation module.

In the early 19th century, textile workers in Nottingham rebelled against their factory owners As factory owners began to use new machinery that reduced the number of employees and factories they needed, workers felt that their skillset was being wasted and their livelihoods threatened. This rebellion was the Luddite movement. The term ‘Luddite’ has since been used to describe those who opposed industrialisation, automation, and in more recent times some cutting-edge technologies threatening to disrupt the mainstream. When it comes to artificial intelligence (AI), you can sympathise with the Luddite philosophy to an extent. The idea that we can teach

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! When it comes to artificial intelligence (AI) adoption, there is a growing gap between the haves and the have-nots. According to IBM, the global AI adoption rate went up by 4 percentage points in 2022, reaching nearly 35%. However, the study also found that the gap in AI adoption between larger and smaller companies also grew significantly in the past year.

In a 2013 high-profile cybersecurity incident, the personal and financial information of nearly 110 million Target shoppers were compromised. The potential path of entry: an HVAC vendor who conducted business with the retail giant. The headline-grabbing news took place a few years ago, but the take-home message remains constant: Internet of Things (IoT)-driven building automation systems (BAS) are a double-edged sword. They deliver a range of much-needed efficiencies but increase the number of threat vectors. Using IoT for managing BAS does not have to be a game of chance.

During crises, we are confronted with an opportunity to think differently and create rapid change that can have long-lasting impact. The COVID-19 crisis is no exception. According to research by McKinsey & Company, COVID-19 has dramatically accelerated the adoption of new technologies, and many of these are here to stay. Organizations were forced to adopt new technologies overnight to survive, or risk becoming irrelevant. As a result, almost every sector has altered the way they interact and do business with their customers over the past two years.

Collaboration is a golden rule for organisations to truly leverage the scope of their data and enhance their business. Organisations need to be flexible to be interactive with data. This entails the freedom to access insights, conduct exploratory analysis and collaborate. Collaborative analytics makes it easy to share analytical initiatives and insights that can lead to enhanced business performances. It not only makes it easier to discover new data but also to make the most of it where different stakeholders and teams can use relevant data to draw insights and take actions.