DoT Blog

  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...

  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

  SAP TechEd 2025 (Credit: SAP/Rücker) SAP’s 2025 TechEd event in Berlin served SAP as a platform to deliver a message that landed with unusual clarity: developers are now the architects of intelligent systems , and enterprise AI is shifting decisively from insights to agentic action . Although, SAP isn’t alone in this direction, its announcements carve out a distinct role in the emerging AI landscape, one where business applications, data, and autonomous agents converge.   SAP’s Big Bet: Developers + Agentic AI So, during the event, SAP framed the future of enterprise software around agentic AI , AI that doesn’t just answer questions but performs tasks , makes decisions, and automates workflows with context, yet this is somehow not a new message in the industry. Yet, what’s new is SAP’s insistence that developers, not data scientists alone, will be the ones shaping how this works inside organizations. So, to put SAP’s message in context, let’s explore SAP’s key announcements...

  Logo Image Courtesy of Oracle Oracle’s latest announcement doesn’t sound like just another product release; it’s a statement. With its new AI Data Platform, Oracle is telling the enterprise world that the time for siloed data and experimental AI is over; we’re now entering an era where AI needs to live inside business data, not beside it. Unveiled at AI World 2025 , the Oracle AI Data Platform aims to unify data management, AI model integration, and automation, all within a single environment. According to the company, it combines data lakehouse and analytics capabilities with built-in generative AI tools, vector indexing, and an “agent hub” for building intelligent applications. So, the message is clear: Oracle wants to make AI an operational layer across business workflows, not an add-on.   From Data Storage to Intelligence Activation For years, enterprises have struggled to connect their data systems to AI models efficiently. Oracle’s approach focuses on bringing AI to t...

  Logo Image Courtesy of IBM Business intelligence (BI) has always carried a big promise: giving organizations a way to clearly see the status of the business, to make sense of their data, and to ground decisions in facts rather than gut instinct. Over the years, we’ve moved from static reports to interactive dashboards and from dashboards to self-service tools, and yet, despite the evolution, the underlying complaint has remained consistent: BI adoption rarely matches the hype. Tools are powerful, but some users still struggle to trust, interpret, and act on the insights they generate. IBM’s recent launch of Watsonx BI introduces a new frame: BI not as a tool but as an agent. This subtle linguistic shift is meaningful; instead of thinking about BI as a system, you go with running a report and building a dashboard—the vision of BI coming to you, responding conversationally, surfacing trends proactively, and explaining not just “what” but “why” and “what next.” If dashboards were t...

  Image by Negative Space  (Pexels) Ok, so generative AI continues to dominate headlines, but what’s more telling is how quickly it’s cementing itself in real business strategies. Well, according to a recent Yahoo Finance report , the global chatbot market is expected to hit $15.5 billion by 2028, driven largely by the ubiquity of conversational AI tools. The numbers themselves are striking, but even more so is what they represent: a shift from AI as an “innovation experiment” to AI as a structural pillar of modern organizations. Customer service, sales enablement, and education are just a few of the areas being reshaped by chatbots and conversational platforms. In many ways, the chatbot market is just the tip of the iceberg. Recent surveys suggest that 95 percent of U.S. companies already use generative AI in some capacity, and production-level use cases have doubled in a short timeframe. We no longer talk about cautious pilots in innovation labs; we’re seeing generative AI t...

  Image by geralt (Pixabay) When I first read the Light Reading article “ AI looks increasingly useless in telecom and anywhere else,”  I had to pause. Not because the argument was new—I’ve seen plenty of skepticism about AI—but because of the tone. It doesn’t just question AI’s utility, but it paints a picture of a lobotomized society , drifting into an “AI psychosis” where people see machines as sentient companions. Boy, it’s an arresting way to start, but also, perhaps, too convenient a metaphor. The author compares our intellectual reliance on AI to muscles wasting away from disuse, citing early studies that show people who lean too much on generative AI may grow less critical, less precise, and even a little sloppy. It’s a provocative analogy, but one that, in my view, overreaches. Yes, there are legitimate concerns: copy-pasting AI outputs without scrutiny is a real problem, and treating chatbots as friends, or worse, as oracles, can be dangerous, but to equate this with...

  Logo image courtesy of ThoughtSpot Today, analytics is not about data, reports, and dashboards anymore. It is not even about insights. It seems ThoughtSpot is aiming for something bigger: agents that think, act, and close the loop, all on your data stack. If the last few years in analytics were about self-service and natural language, 2025 is clearly about something new: AI agents. Not just tools that explain business, but ones that act on its behalf, and ThoughtSpot, the company that once positioned itself as the Google of BI, is now doubling down on that vision with the launch of its Agentic Analytics Platform. This is not just rebranding. It seems to be a strategic repositioning, backed by AI agents, real-time cloud-native architectures, and native integrations, especially with Databricks and Snowflake . It is also a signal that ThoughtSpot wants to move way beyond charts and dashboards and instead become a living, breathing part of the new enterprise decision loop. ...