DoT Blog

  Logo courtesy of  Amazon.com, Inc. For the past few years, the AI conversation has been dominated by large public models and cloud-scale services, but quietly, another trend has been gaining traction: the push toward private, autonomous AI agents running closer to where data and decisions live. With the recent introduction of OpenClaw on Amazon Lightsail , Amazon Web Services (AWS) is nudging that conversation forward. This new offering essentially allows developers and organizations to run autonomous AI agents in their own AWS-controlled environment using relatively simple cloud infrastructure. On the surface, this might look like just another developer-friendly deployment option, but if we step back for a moment, it hints at a deeper shift into how organizations might design AI systems in the near future.   From AI Assistants to Autonomous Agents So far, most of today’s AI implementations still operate basically as assistants. You prompt them, they respond, even in en...

  OpenText logo courtesy of Opentext Corporation When I read that OpenText has decided to sell Vertica in order to pay down debt, my first reaction wasn’t surprise, I admit, it was a bit of discomfort. Not because selling assets to reduce leverage is inherently wrong; for sure it isn’t. But because which asset you sell says a lot about how you see your future, and in this case, OpenText may be divesting one of the very pieces that could have mattered most in the next phase of enterprise software: high-performance analytics in an AI-driven world. In my view, this may turn out to be a sensible financial move, for the best, or it may end up being a strategic mistake that only becomes obvious later. Let’s unpack why.   Vertica Wasn’t Just Another Product Vertica is not a shiny, hype-driven analytics toy; it’s a battle-tested, high-performance analytical database designed for large-scale, complex workloads. Over the years, it earned a reputation for speed, efficiency...

  Teradata logo courtesy of Teradata, Inc. For the last two years, the enterprise AI market has been stuck in an awkward in-between state. Plenty of pilots, plenty of proofs of concept, but far fewer systems that run the business. So, with its newly announced Enterprise AgentStack, Teradata is clearly trying to address this gap, not by introducing yet another model, but by focusing on how AI agents are built, governed, and deployed at enterprise scale. What makes this announcement interesting, in my view, isn’t the buzzword density; it’s the direction of travel.   From “AI features” to agentic systems AgentStack is positioned as a framework for building and running AI agents that operate directly on governed enterprise data. The emphasis is not on experimentation but on operational AI: agents that can reason, retrieve, and act within defined business constraints. This matters because most enterprise AI failures don’t happen at the model layer; they happen at the integration ...

  Image by Freepik Two recent pieces, one from Wired and another from The Guardian , describe an experiment that feels equal parts absurd, fascinating, and unsettling: Moltbook , a social network designed almost entirely for AI agents talking to other AI agents. At first glance, Moltbook sounds like a gimmick: a bot-only social platform where AI personas post, reply, argue, form alliances, and generate content, without humans taking part directly. But once you look past the novelty, Moltbook becomes something more interesting and at times even concerning: a mirror held up to the direction social platforms, AI agents, and digital interaction may be drifting toward. So, let's address it.   A Social Network Without Humans (Mostly) According to the reporting, Moltbook allows users to deploy AI agents as social actors; these agents create profiles, generate posts, comment on each other’s content, and build reputations. Humans can see, tweak prompts, or set high-level goals, bu...

  Image by DC Studio on Freepik Dear friends, Yes, it’s true, organizations worldwide are racing to adopt artificial intelligence (AI), but many are tripping over a surprising obstacle: their data. But why? Well, because the very fuel AI relies on, the business data that should inform decisions, is often fragmented, inconsistent, or simply unreliable. This is not a niche issue; surveys consistently show that more than half of business and technology leaders cite poor data quality as a top barrier to AI adoption. The paradox seems clear: companies invest in sophisticated AI models without first ensuring their data is ready to support them. The result? AI projects stall, insights are misleading, and innovation slows. But fixing this doesn’t mean just collecting more data; it means rethinking how data is structured, connected, and understood. So, below are five takeaways that challenge conventional wisdom and reveal how organizations can, potentially, turn data from a liability into a...

  As the year winds down, I just wanted to pause and say thank you . DoT exists to make sense of complex topics: analytics, AI, data, and enterprise software. Without the hype, the fear, or the empty buzzwords.  This year brought no shortage of noise, and the fact that you’re still here reading, questioning, and connecting the dots means everything. The holidays are a good moment to step back, disconnect a bit, and think more deeply about where technology is actually taking us and where we want it to go. Wishing you a restful holiday season and a thoughtful start to the new year. We’ll be back soon with more analysis, sharper questions, and more dots to connect. — Jorge 🎅 AI Santa: How the North Pole Adopted Automation | A Holiday Tale from D of Things

  Image by Geralt (Pixabay) In the last couple of years, I have seen one of the most consequential shifts in the modern data platform  landscape market happening quietly but decisively: the point of control is moving away from the database management system (DBMS) and toward the governance catalog. This transition is not accidental. It seems to be driven by three reinforcing forces: the industry’s move toward open data models, the growing necessity of governance in the AI era, and the strategic positioning of major players such as Databricks and Snowflake,   among others. Together, these forces are redefining where power, control, and differentiation sit in the data stack. What follows is a brief look at why these matter and why it is happening now. From Execution Control to Governance Control Traditionally, the DBMS served as the primary control plane of the data platform. It enforced access policies, managed read-write operations, and effectively functioned ...