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.

Welcome to our fabulous world. Creator of a unique model that disrupted the restaurant industry 15 years ago, we are now the leading dining platform across Europe and Australia. We are experiencing an exciting period of growth, and we need the greatest folks onboard. Together, we will make our wildest dreams come true! We strongly believe that our mission can only be achieved if we also bring happiness to our working environment.

If you have ever worked with a Machine Learning (ML) model in a production environment, you might have heard of MLOps. The term explains the concept of optimizing the ML lifecycle by bridging the gap between design, model development, and operation processes. As more teams attempt to create AI solutions for actual use cases, MLOps is now more than just a theoretical idea; it is a hotly debated area of machine learning that is becoming increasingly important. If done correctly, it speeds up the development and deployment of ML solutions for teams all over the world. MLOps is frequently referred to as DevOps for Machine Learning while reading about the word.

Our purpose is to put our energy where it matters, to decarbonise the New Zealand energy sector and promote #changematters. We are passionate about our mission and proud to have a tribe of people behind us working towards a common purpose. With such an ambitious goal, you might ask yourself – how does this opportunity help support a better, cleaner NZ? Contact is transforming its business with a data-first focus on operational excellence, enabling our team to do their best. Kōrero mō te tūranga - About the role We are on a journey to lift our organisational data capability to enable our people to do what they do best – deliver amazing customer experiences, create growth, and increase the value of our business. You'll be part of a data team working with business stakeholders to deliver these outcomes.

Wish is a mobile e-commerce platform that flips traditional shopping on its head. We connect hundreds of millions of people with the widest selection of delightful, surprising, and--most importantly--affordable products delivered directly to their doors. Each day on Wish, millions of customers in more than 100 countries around the world discover new products. For our over 1 million merchant partners, anyone with a good idea and a mobile phone can instantly tap into a global market. We're fueled by creating unique products and experiences that give people access to a new type of commerce, where all are welcome.

This is part of Solutions Review's Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, dotData Founder and CEO Ryohei Fujimaki offers commentary on data quality strategies to get your data machine learning-ready. As the world embraces machine learning (ML) and Artificial Intelligence (AI), data leaders are adjusting and perfecting data quality management frameworks. Traditionally, there are two stages in data quality: raw unprofiled data and cleansed data, free of common errors and commonly used for business intelligence (BI). But, companies at the forefront of data-driven decision-making have realized that data quality needs to level up--and this is where ML-ready data comes in.

The scowl on Cristiano Ronaldo's face made international headlines last month when the Portuguese superstar was pulled from a match between Manchester United and Newcastle with 18 minutes left to play. Few footballers agree with a manager's decision to substitute them in favour of a fresh replacement. During the upcoming football World Cup tournament in Qatar, players will have a more evidence-based way to argue for time on the pitch. Within minutes of the final whistle, tournament organizers will send each player a detailed breakdown of their performance. Strikers will be able to show how often they made a run and were ignored.

Agoda is an online travel booking platform for accommodations, flights, and more. We build and deploy cutting-edge technology that connects travelers with more than 2.5 million accommodations globally. Based in Asia and part of Booking Holdings, our 4,000 employees representing 90 nationalities foster a work environment rich in diversity, creativity, and collaboration. We innovate through a culture of experimentation and ownership, enhancing the ability for our customers to experience the world. Within our Data Team, we build and maintain the engines behind the Machine learning driven optimizations we serve at real time to our customers.

As I speak with data scientists, especially those working in Global 1000 companies, many express concerns about their situation. In some sense, they're victims of their own success: Data scientists are producing models that are making substantial contributions to the business, and thus more and more models are being used in production applications. But as a result, data scientists face several challenges. The causes of both issues are very consistent across most organizations, and as such, lend themselves to straightforward solutions. This is good news for both data scientists and the organizations that they work for – provided that organizations act, and do so with some urgency.

Peak's inaugural Decision Intelligence (DI) Maturity Index found that while the U.S. was an early leader in artificial intelligence (AI), India is now the more mature market when it comes to readying their business to adopt AI. While the U.S. was an early leader in AI, with 28% of U.S. businesses adopting the technology over six years ago – compared to 25% in India and 20% in the U.K. – India is the more mature market when it comes to leveraging AI, scoring 64 (out of 100) on Peak's DI maturity scale, while the U.S. charted 52 and the U.K. just 44. What's setting Indian businesses apart is internal communication and education about AI to ensure broad support – 18% of U.S. workers weren't sure if their business used AI, compared to only 2% of Indian workers. Further, 78% of junior staff in India expect AI to have a positive impact on worker well-being over the next five years, compared to 47% of those in the U.S. The report also found that the way businesses structure data teams is crucial to successful AI adoption, with the majority of Indian businesses having data practitioners embedded in commercial teams to support analysis – by contrast most U.S. businesses have a central data team. Moreover, while California is historically seen as the mecca of tech innovation, New York is ahead in AI leadership as it scored an average of 61 out of 100, compared to California, which charted at 58. This is because New York is the top financial services center in the U.S. – an industry that is the second most-mature industry behind IT, computing and technology with a mean maturity score of 56 across all three markets (U.S., U.K. and India).