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.

Robotic Process Automation (RPA) is one of the most popular technologies for automating business processes. This technology enables fast and, above all, efficient automation of standardized processes. However, the range of uses is limited by the need for structured data and programmable decision-making. However, this shortcoming can be overcome through the use of artificial intelligence. In the following, we will show you RPA and AI, and how artificial intelligence can help RPA bots become smarter.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Latest reports show that the global master data management (MDM) market is expected to reach $27.9 billion by 2025. Looking at the humongous amount of data that is being generated -- 1.145 trillion MB per day -- the growth of the MDM market is inevitable. Fueled by data-heavy new technologies like artificial intelligence (AI), machine learning (ML), internet of things (IoT), robotic process automation (RPA), augmented reality (AR), virtual reality (VR) and social media platforms, this market will continue to grow at an unimaginable rate in the coming days. Enterprises need robust data management capabilities to make better business decisions, improve operational efficiency and drive superior customer experience (CX).

In a widely circulated and discussed article on Forbes, Nallan Sriram, Global Technology Strategist of Unilever makes a compelling argument for the need for master data for AI initiatives in the enterprise. The article describes that master data gets siloed in operational systems like ERP with the key decision-makers realizing the need for correct master data when faced with revenue loss or increased operational expense. As master data provides context to business transactions, it is fundamental to business operations. In earlier times, we could manage master data through human intervention. But now with cloud data lakes and our aspirations to build predictive algorithms for business operations and operations, the need for clean, contextual and unified master data is all the more enhanced.

Last month, we announced the Informatica Intelligent Data Management Cloud. One of the key attributes of our industry-first offering is AI native at scale. CLAIRE is the AI powerhouse behind our Intelligent Data Management Cloud. Built on an enterprise unified metadata foundation, it provides AI-driven automation of data management activities. In this blog post, I'll discuss 10 ways AI improves master data management (MDM).

BEGIN ARTICLE PREVIEW: In this white paper,”Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data,” our friends over at Profisee discuss how Master Data Management (MDM) will put your organization on the fast track to automating processes and decisions while minimizing  resource requirements, while simultaneously eliminating the risks associated with feeding AI and ML data  that is not fully trusted. In turn, your digital business transformation will be accelerated and your competitive  edge will be rock solid. The lifeblood of AI and

Many have emphasized the need for data for artificial intelligence (AI) and machine learning (ML) algorithms, and metaphors from "data is the new oil" to "data is the new sun" further exacerbate the dire need for better data. However, one aspect of data that is often not explicitly mentioned in these circumstances is the role of master data and how it fundamentally impacts the quality of data that is driving the ML algorithms. In the spirit of paying tribute to management guru Peter Drucker, who's credited with the saying, "culture eats strategy for breakfast," this article explores: According to The DAMA Guide to the Data Management Body of Knowledge, master data represents "data about the business entities that provide context for business transactions." Simply put, for any enterprise, it is the customers whom they sell to, the brands they market, the products they sell, the consumers who use their products, the materials used to make the products, the plants that manufacture their products, the suppliers that supply the materials, the employees who build the products directly or indirectly, and the list goes on. Why is there a lack of awareness in enterprises about master data?

Informatica is acquiring GreenBay Technologies, a Wisconsin based startup that it funded to fill in a gap with its machine learning capabilities when it comes to matching data entities and the schema that represent them. Informatica's latest acquisition extends machine learning capabilities into matching of data entities and schemas. And the acquisition came out of Informatica's first formal partnership effort with a university. The new capabilities will find their ways into Informatica's existing master data management (MDM), enterprise data catalog, privacy, governance, and data integration offerings. The company, GreenBay Technologies, was co-founded by a University of Wisconsin at Madison computer science professor and began operation with ties to the university and its alumni research foundation.

Digital transformation is the "buzzword du jour" in every industry. There have been many initiatives that should have led to a digital transformation across many industries -- supply chain integration, global ERP systems, etc. These likely should have prepared us for the digital life. This fell far short in large part to one key element -- data. Data is key to any digital transformation journey, but the foundation of all data is master data.

Explore how SAP Master Data Governance Rule Mining can support you in analyzing your master data for new data quality rules, through machine learning technology that finds patterns in the master data and proposes new rules based on these patterns: http://sap.to/60101E5Fr