Simply having data isn't enough-how you organize, govern, and activate it makes all the difference. Leading organizations implement three specific practices: connect all your data together, label and organize it so it's easy to find, and set controls to ensure only the right people (or agents) have access to sensitive data sets.
Red Hat AI Enterprise provides a foundation for modern AI workloads, including AI life-cycle management, high-performance inference at scale, agentic AI innovation, integrated observability and performance modeling, and trustworthy AI and continuous evaluation. Tools are provided for dynamic resource scaling, monitoring, and security.
Across the world, governments are redefining data. It is no longer a commercial byproduct, but a strategic resource. One that carries economic weight, political influence, and long-term national consequences. At the center of this shift is what most people never consciously see but continuously produce: their digital DNA.
AI was everywhere, but I wasn't focused on product launches. I was looking at how companies think about data itself: how it's shared, governed and ultimately turned into decisions. And across conversations with executives and sessions on security and compliance, a pattern emerged: the technical limitations that once justified locking data down have largely been solved. What remains difficult is human. Alignment, trust and confidence inside organizations are now the true barriers.
You aren't short on data; you're surrounded by it. But when that data is trapped in disconnected systems and conflicting dashboards, it feels less like an asset and more like a "data prison." We know the frustration of having plenty of information but limited ability to turn it into trusted action. The upcoming March 4th MarTech Conference session, "Break out of data prison with a strategy to end the silos," addresses this head-on.
Salesforce data migration sounds straightforward on paper. In practice, it almost never is. The system goes live, everyone gets access, and nothing seems obviously wrong at first. Then little questions start popping up. A report doesn't quite line up. A dashboard only makes sense after a few extra filters. Sales reps pull numbers into Excel just to feel sure. Before long, Salesforce is technically running, but confidence in the data hasn't caught up.
Glean Chat offers an experience very similar to OpenAI's ChatGPT, but limited to an enterprise's content and resource boundaries, Jain said. When a user makes a natural language-based query, the company's search technology uses APIs to check all the content and activity - including information in applications - pertaining to the query before storing it in a customer's cloud environment. The data stored is then fed to large language models (LLMs), which have been trained on that particular enterprise's data,
In the 2026 installment of the State of Digital Media Benchmark, the media consultancy analyzed the governance protocols of 143 major advertisers representing about $35 billion in annual spend. One chilling if unsurprising conclusion: agencies blame clients for being so siloed the agency doesn't have clarity on client data, which is arguably the lifeblood of modern digital marketing. It all translates to what the report cited as a "dangerous disconnect between 'having data' and 'having visibility', particularly among the world's largest advertisers, those spending at least $1 billion in media annually.
Unverified and low quality data generated by artificial intelligence (AI) models - often known as AI slop - is forcing more security leaders to look to zero-trust models for data governance, with 50% of organisations likely to start adopting such policies by 2028, according to Gartner's seers. Currently, large language models (LLMs) are typically trained on data scraped - with or without permission - from the world wide web and other sources including books, research papers, and code repositories.
In a move that surprised no one, OpenAI has confirmed it will begin testing advertisements in ChatGPT. Ads will be trialled with free users in the United States, appearing in clearly labelled slots above or below chatbot responses. Paid tiers (Plus, Pro, Business, Enterprise) remain ad-free. OpenAI insists ads won't influence the chatbot's answers, won't be displayed alongside sensitive topics such as health or politics, and user conversations won't be shared with advertisers...
The AI-centric security product demo looked impressive. The vendor spoke confidently about autonomous detection, self-learning defences, and AI-driven remediation. Charts moved in real time, alerts resolved themselves, and threats seemed to vanish before human analysts even noticed them.
As early adopters have discovered, Copilot delivers real transformation only when it becomes an integral part of critical workflows. Enterprises are beginning to treat generative AI not as an isolated productivity tool, but as the connective layer linking business applications, data, and human judgment. Nowhere is this shift clearer than in organizations using Microsoft 365 Copilot as part of a broader architecture that spans CRMs, low-code platforms, and specialized AI systems.
Keenan wrote that Salesforce users have been building their own MCP servers using OpenAI's Apps SDK and exposing Salesforce data to various frontier LLMs in the bargain. That puts the company's data outside the governance and usage metering of Salesforce. "The thing that I worry about, and what I wanted to get ahead of, was homegrown MCP servers from customers just spitting out data to OpenAI around the trust boundary," Billmaier told Keenan. "And with this, we're actually kind of being full of our destiny as we think about other players emerging in this space."
For technology leaders, this year has been defined less by what to promise and more by how to deliver. The conversation has matured, but unevenly. Some organisations are now treating sustainability as an organisational capability, whilst others are still trying to reconcile their ambitions with fragmented systems and incomplete data. The task for 2026 will be to embed sustainability into the digital and operational fabric of business - to move decisively from strategy to systems.
Upper is based on W3C standards such as RDF for conceptual graph representation and SHACL for validation, and it enables the principle of "model once, represent everywhere" across the data ecosystem.Upper organizes concepts through keyed entities, their attributes, and their relationships across domain boundaries. The modeling grammar and validation structure are designed to maintain consistency as definitions evolve. Keyed concepts can be extended monotonically, allowing new attributes or relationships without modifying existing definitions allowing domains to expand over time without breaking existing models.
Allie Miller, for example, recently ranked her go-to LLMs for a variety of tasks but noted, "I'm sure it'll change next week." Why? Because one will get faster or come up with enhanced training in a particular area. What won't change, however, is the grounding these LLMs need in high-value enterprise data, which means, of course, that the real trick isn't keeping up with LLM advances, but figuring out how to put memory to use for AI.
Computer Weekly met up with Glasneck during the UK and Ireland SAP User Group (UKISUG) conference in Birmingham at the start of December to discuss the role of strong data management in successful SAP S4/Hana projects. The company has grown through acquisition. This resulted in 50 or so ERP systems in operation, which were not truly integrated. Imperial Brands recognised it needed to bring all of these legacy ERP systems together into one single S4/Hana instance.
Europe's long-running struggle to define digital sovereignty - and to turn it into something practical - is reaching an inflection point. That was the message at this year's Gaia-X Summit in Porto, where executives and governments argued that the continent finally has the technical foundations for sovereign data sharing. All it needs now is the political will, economic models, and global partnerships to make it work at scale.
2025 has been a transformative year for cybersecurity, with emerging technologies and evolving threats changing the landscape as we once knew it. Reflecting on the year, there are several trends that come to my mind, both good and bad. Organizations prepared for a quantum future, foreign adversaries and cybercriminals alike made strategic moves, and industries as a whole found themselves targeted with waves of cyberattacks (such as the case with the retail sector).
Snowflake has signed an agreement to acquire Select Star. This company's technology will expand Snowflake Horizon Catalog by integrating with databases, BI tools, and data pipelines. This will increase the context for AI agents such as Snowflake Intelligence. The full context of data assets is often scattered across upstream and downstream systems. This fragmentation makes it difficult to find the right data and understand the full context. In the AI era, this limited context poses a problem for both humans and agents.
Our industry is rushing headlong toward an AI-powered future. The promise is captivating: intelligent systems that can predict market shifts, personalize customer experiences and drive unprecedented growth. Yet in that race, many organizations are short-changing or even skipping a critical first step. They are building sophisticated engines but trying to run them on unrefined fuel. The result is a quiet crisis of confidence, where powerful technology underwhelms because the marketers don't trust the data it relies on.
Salesforce has completed its acquisition of Informatica. The CRM provider paid more than $8 billion (€6.9 billion) for the data management company. The acquisition is intended to lay the foundation for reliable AI agents within the Salesforce ecosystem. The completion brings Informatica's data catalog, integration tools, and governance services to the Salesforce platform. CEO Marc Benioff emphasizes the importance of this step: "You have to get your data right to get your AI right. Data and context is the true fuel of Agentforce."
U.S. Immigration and Customs Enforcement has published a request for information for private-sector contractors to launch a round-the-clock social media monitoring program. The request states that private contractors will be paid to comb through "Facebook, Google+, LinkedIn, Pinterest, Tumblr, Instagram, VK, Flickr, Myspace, X (formerly Twitter), TikTok, Reddit, WhatsApp, YouTube, etc.," turning public posts into enforcement leads that feed directly into ICE's databases.
In the UK, artificial intelligence (AI) is no longer a buzzword reserved for tech conferences and research labs - it's an engine driving tangible transformation across industries. Healthcare, in particular, stands at the forefront of this change. From automating administrative tasks to accelerating medical research, AI is creating opportunities for both the public and private sectors to work smarter and more efficiently. A new era of intelligent healthcare solutions
As artificial intelligence transforms every industry, organizations are placing an increased focus on the quality of their data. The ability to generate value from AI depends not only on computational power or model sophistication but also on the trustworthiness of the data that underpins every insight, decision, and interaction. For Aon, the global professional services firm, this recognition is central to its enterprise transformation. For more than a century, Aon has served as an advisor to clients navigating complex and interconnected risks.
