Qlik Predict: No-Code Predictive Intelligence for Business

🔍 “Data variety” is killing more AI & analytics efforts than lack of models. An article in TechRadar titled “Data variety: the silent killer of AI — and how to conquer it” struck a chord. The gist: messy, inconsistent, poorly documented data sources are often the hidden blocker in digital transformation. Companies can have all the aspiration for AI / insights, but if they don’t have a clean map of where their data lives, how it flows, who owns each piece — the rest becomes fragile.   At Bees Computing, that’s a problem we help solve. Here’s what we do: 🐝Data audit & mapping — identify what data you have, where it comes from, where it goes, who touches it. 🐝Schema alignment, normalization, versioning — ensure your data formats / definitions don’t drift, making it easier for analytics/AI to consume reliably. 🐝Data lineage / change tracking — monitor when systems change sourcings, new APIs, or introduce new sources, so the map stays current. 🐝Governance & ownership — defining who is accountable for each data flow or transformation. If something breaks, you have a “go‑to” person and a readable trail. 🐝Automated tooling + recommendations — combining automation with human review to reduce manual toil and risk of error. The upshot? With better mapping & data hygiene, AI and analytics deliver more predictable results. Projects are less likely to stall. You spend less time firefighting and more time extracting value. Curious: what’s the biggest data variety challenge you’ve seen in your organization — schema mismatches? changing APIs? poorly documented sources? Let’s trade war stories, and we can show you how we’ve helped folks move past them.   article: https://lnkd.in/g2fUYsEr   #DataMapping #DataGovernance #AI #SMB #Analytics #BeesComputing

🧠 AI doesn’t fail because the tech is broken—it fails because the data is. This TechRadar piece nailed something we see all the time at Bees Computing: organizations aren’t lacking ambition or fancy algorithms—they’re tripping over inconsistent, undocumented, and siloed data. It’s not enough to “have data.” You need to trust it, track it, and tune it so it can fuel your AI and analytics efforts instead of undermining them. Here’s what I believe: ➡️The best AI models can’t fix a broken foundation. ➡️Data mapping isn’t just IT’s job—it’s a business survival skill. ➡️If your data flows are undocumented, your risk flows are invisible. We help businesses fix this every day—and when they do, the results aren’t just cleaner dashboards. They’re faster decisions, fewer surprises, and more confidence. Would love to hear from others: What’s one data mess that caught you off guard or blocked progress? Let’s make these challenges visible so we can all learn from them. #AI #DataGovernance #Leadership #DigitalTransformation #DataMapping #SMB #BeesComputing

Enterprise IT vendors are moving to position themselves for the AI era. As subsequent M&A activity picks up, a tendency to bolt together "AI stacks" for IT teams should be challenged. True value from enterprise AI will come from empowering those familiar with the context that makes business processes tick: business users and analysts. In my latest article for @TechRadarPro I discuss how the AI Data Clearinghouse process helps address the root causes of stunted enterprise AI rollouts by empowering businesses to uncover transparent, trusted AI use cases. Read more here: https://lnkd.in/gVqumAfJ

90% of companies are building or considering building #data #AI products. In reality, they only have data assets, dashboards, and AI models. And in the age of AI, that’s not enough. I was reminded of this in a workshop last week. The team had built a beautiful platform. Well-known and highly respected technology vendors. Data pipelines were humming. Dashboards looked sleek. They even trained a few machine learning models. They launched a self-service text-to-BI (AI-driven) capability Everyone nodded in approval. But just six months later? The platform was abandoned with a creation of "my own single source data copies". The dashboards were getting no users. The AI models sat idle. Business leaders went back to gut feel and Excel. What went wrong? They never had data products. They had data assets (and some AI experiments). Here’s the difference from my perspective: ▶︎ Data assets You’ve collected, cleaned, and organized data. Maybe even trained many AI models. It looks promising, but usage is optional. Everyone continues working if nothing has changed. ▶︎ Data products You’ve created usable, reliable, and trusted services that deliver value repeatedly. Teams use them daily without reminders. They embed into business workflows. They generate business impact. And in AI, they provide the fuel and foundation for scalable, responsible automation. Why the two get confused in AI & data: A model ≠ a product An impressive AI tool demo doesn’t mean adoption in real business workflows. Pipelines ≠ trust A technically elegant setup doesn’t guarantee reliability or explainability. Accuracy ≠ value An AI model that’s “80% right” but never acted on is zero value. Innovation pilots ≠ scale and production-ready Proof-of-concepts excite stakeholders but often vanish when budgets tighten. How many times did we see this? The truth is simple: --- Data assets get you visibility. --- AI-ready data products get you value. Now when leaders talk about data products, I push them to ask: --- Who is the user of this data/AI product, and how do they rely on it daily? --- Does it solve a recurring, must-have problem, or just a “nice to demo” one? --- Can it scale across teams with the same reliability and guardrails, and without an extra cost? --- Is it maintained, versioned, and governed like a real product, not a science experiment? So are you building data assets and AI experiments? or are you creating AI-powered data products your business can’t live without?

Qlik Predict drives enterprise AI adoption with no-code forecasting. “Qlik Predict embeds trusted forecasts directly in workflows,” said Brendan Grady, EVP. Read more: https://lnkd.in/eZYiJ4Aw #QlikPredict #AIForecasting #DataAnalytics #EnterpriseAI #PredictiveAnalytics #TechIntelPro

Data has a story to tell.. but it gets lost in translation. I just wrote an article about the learnings we have gained from the past 3,5 years of building our Revenue Intelligence Platform at 180ops. Even though we had a clear vision to start with, it has taken a lot of time and effort to make it accurate and scalable. I believe that these learnings are interesting to anyone operating in data & intelligence, creating infrastructure level solutions to enable datadriven management or simply making decisions based on data. AI, LLMs, and the promise of Agentic AI are currently on everyones lips. However, if you are creating data for the purpose of managing a company, neural networks based technologies have deficiencies. For a sustainable management, you need stable data products and neural networks based solutions were never designed to serve that purpose. Its not an one/other game, its about processes designed to leverage each technology's unique advantages. There is a difference between leveraging data and creating new fundamental data products. This difference needs to be understood for the enablement of Agentic infrastructure. I believe this article has a lot of learnings to offer and I'd love to hear what you think :) #AgenticAI #daap #revenueintelligence #datastrategy #datadriven #cx https://lnkd.in/dSrpYkGN

BREAKING: Turns out throwing data into a “lake” doesn’t magically create insights. Who could have seen that coming? Finally got to this fantastic reality check from 180ops (written by my friend Toni Keskinen) about why 90-95% of agentic AI initiatives are face-planting harder than a drunk penguin. Spoiler alert: it’s not the AI’s fault, it’s the data mess underneath. My favorite stat: 100% of failed AI cases had data-related challenges. Not 99%. Not “most.” ALL of them. Because apparently, feeding garbage data to AI and expecting Shakespeare is still a thing in 2025. 🤡 Here’s the tea ☕: Companies have been hoarding data like digital packrats for a decade, cramming everything into warehouses and lakes without any actual plan. Now they’re shocked—SHOCKED—that their shiny new AI can’t perform miracles with their Frankenstein data architecture. The folks at 180ops spent 3.5 years (and probably several therapy sessions) building their Revenue Intelligence Platform because—plot twist—data needs to actually tell a coherent story, not just exist in 47 different formats across 6 ERPs. This couldn’t be more timely with Massachusetts Institute of Technology’s recent study showing 95% of enterprise AI pilots are failing, and Gartner predicting 40% of agentic AI projects will be canceled by 2027. But sure, let’s keep buying more AI tools instead of fixing our data foundation. That’ll work. 🙃 Props to 180ops for doing the hard work so the rest of us don’t have to suffer through “data transformation purgatory.” Sometimes the best way to avoid a 3-year learning curve is to let someone else take the hits first. Read the full post-mortem of corporate data dysfunction here: https://lnkd.in/g9uixngn #RevOps #DataStrategy #AI #RealTalk #180ops #DataManagement

Interested in data and intelligence? (We know, it's kind of a silly question, because who isn't striving for data-driven decisions and strategy in this era?!) Toni has written a really fantastic article on our site that talks about infrastructure-level solutions (and what you might be missing when it comes to being able to apply a data-driven strategy!) Check it out! 👇

A couple of months ago, I had the opportunity to discuss AI disintermediation in the consulting industry at Catalant's West Coast forum. Much to the benefit of organizations like Stratus Data, and to the demise of what Patrick Petitti calls "Consulting 1.0." The conversation shifted from, "what can consultants do?" to "what can our clients do?" To that end, we've put together a 2 minute read on establishing a foundation for Analytics and AI: https://lnkd.in/gB6sDjrn

Data isn’t just the fuel of AI—it’s the foundation of every breakthrough. But here’s what’s often overlooked: without open semantic interchange (OSI), even quality data remains trapped in silos. OSI enables different systems, formats, and organizations to share and understand data seamlessly, creating the interoperable ecosystem that AI desperately needs to reach its full potential.