#data-governance

#data-governance

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.

In England's schools, children are not only pupils but also data subjects. From the moment they are born, a digital record begins to take shape - one that will follow them through nursery, primary school, secondary education, and in many cases well into adulthood. What was once a matter of paper registers and filing cabinets has become a complex infrastructure of digital systems, databases, and analytics tools, managed by both the state and private companies, for AI, surveillance, and more.

You can't outsource accountability, but many organizations are doing just that, often without even realizing it. This is especially the case when it comes to data. As businesses rely more heavily on third-party suppliers to store, move, and manage their data, the risk of something going wrong multiplies. Whether that's compliance, the ability to restore lost data, or susceptibility to cyber attack.

Who controls the data? Every meeting should be captured, but not every recording needs to be shared. Use private meeting settings, control access permissions, and set retention policies that auto-delete after a certain number of days. Who needs access? The power of AI is capturing everything. The responsibility is controlling who sees what. Share broadly for team updates, narrowly for performance reviews, not at all for sensitive discussions.

Governments are increasingly relying on data-intensive systems, both to wage wars and to administer public services. These systems, increasingly provided by the same firms using similar tools, will come to affect our day-to-day lives whether we are in war zones or town squares. This is the era of Militarisation of Tech. The technologies that our governments rely on to deliver services and pursue their objectives are becoming increasingly data-intensive and militarised, which threatens our privacy, dignity, and autonomy.

Regulatory frameworks such as HIPAA, GxP, GDPR and 21 CFR Part 11 are not optional; they are the guardrails that protect sensitive health data, ensure scientific integrity and maintain public trust in healthcare systems. Yet, I repeatedly observed that while these frameworks provided critical safeguards, they often slowed the momentum of digital transformation initiatives, particularly those involving artificial intelligence. Early AI projects faltered not because the models lacked accuracy or relevance, but because the underlying data architectures were not designed to satisfy regulators from the outset.

DOGE is an active component in the government. They meet on a regular basis. They are made up of professionals who have a mission,

Over the course of several years designing and delivering enterprise data platforms for a global pharmaceutical leader, I witnessed firsthand how data had evolved from a backend enabler to a frontline business asset. The organization was no longer just looking to report historical performance; it needed to predict outcomes, personalize patient engagement, customer engagement, brand performance and make regulatory decisions in near real time.

More than three million Californians are expected to lose health coverage in the next several years due to federal Medicaid cuts. At the same time, over half of Californians say they have skipped or delayed care because of rising costs -with nearly half in that group saying their health got worse as a result. As leaders of health organizations serving Alameda County, Santa Cruz and the Central Coast, we can say unequivocally that these are the two greatest health challenges we've faced since the pandemic:

Successful organizations aren't just aligning marketing and IT - they're building systems of collaboration that last.

The leap from chatbot to AI agent is not just about adding automation - it's about architectural transformation, embedding reasoning and action in context.

As I like to say, this path to agentic 'AI heaven' goes through some form of data hell, and that's the grim reality.