2025 is calling! Will your data pick up?
Gartner says that by 2027, over 50% of GenAI models in enterprises will speak the language of their industry or function. That’s quite a leap from just 1% in 2023.
But with GenAI hallucinating and misinterpreting data, we’re hardly there yet.
Most GenAI models can’t understand your business because they’re built on generic internet data. They guess when they should know, fabricate when they lack context, and fail when precision matters most.?
To fix this, enterprises need to start with smarter data. Data that’s semantically aligned, enriched with context, fully governed, and fluent in the language of your industry.
Read: Your Antidote to GenAI Hallucinations
A recent Stanford study shows hallucination rates as high as 88% for verifiable legal queries.
And it’s not just law, either. Think finance. Healthcare. Pharma. In regulated industries such as these, trust and accuracy are non-negotiable. They could mean lawsuits, fines, and reputational damage that can take years to repair.
AI hallucinations happen when GenAI has to piece together answers from incomplete, inconsistent, or context-blind data. Or worse, when it’s juggling conflicting sources answering the same question. Without semantic alignment or a clear “single source of truth,” GenAI fills the gaps.?
But the good news is you can prevent GenAI hallucinations. Here’s how:
Discover: Your Blueprint to AI-Ready Data
Here are three Gartner stats every data leader should pay attention to:?
AI-ready data isn’t a destination. It’s a road you’ll travel again and again. So, where should you start?
First, focus. AI-ready data isn’t about perfection. It’s about purpose. Your data needs context, flexibility, and constant refinement. Start by asking: Is your data fit for the job?
Next, show the value. Build your case to get executive buy-in. Tie it to real wins: faster insights, better decisions, measurable ROI. Executives don’t need buzzwords. They need proof.
Then, get to work. Data fabric. Active metadata. Knowledge graphs. Tools that bring meaning and context to your data and make it ready for GenAI’s demands.
Finally, govern and adapt. GenAI will change your systems, your teams, your workflows. Plan for it. Automate what you can. Build guardrails that move with your data at scale. Train your people to think faster and smarter. We map out each step and what actions you should take in our detailed ‘Data Leader’s Blueprint to AI-Ready Data’ blog.?Check it out here.
Case Study: Smart Data Migration in Retail
Picture this: you're moving 115,000 tables and 1.5 million columns of data to the cloud, all labeled in cryptic code with no clear context or meaning.
And the people who’d initially worked on the system? They had moved on. So there's no one to ask about the business context either. Kind of like playing 3D chess with your data. Each move has layers and ripples you can't always see.?
That’s the challenge a retail company has faced when migrating an ERP system from on-prem SAP ECC to the cloud-based S/4 HANA. Then, they brought in illumex and turned randomness into clarity. Here’s what they achieved:?
? 90% reduction in manual effort for data mapping and documentation. Two years of work condensed into one week.
? Auto-generated business context for SAP tables, bridging gaps and providing semantic clarity.?
? Lightning-fast semantic search, even with massive data volumes. Quick access to quality data for smarter decisions.
? Cost-efficient migration, moving only essential data to slash the pay-as-you-go cloud costs.
? Better stakeholder engagement: Complex data is now available to all.
We sat down for a chat with the retail company’s Head of Data, and you can catch it in this blog post. And if you want the full case study, grab your copy here.
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Explore: Your Data’s 2024 Highlights!
2024 was the year your data spoke your language (and nailed it). It was so iconic, we thought it deserved its own "illumexify Wrapped."
Learn: Why Skipping DataOps Is Like Skipping Day Leg
Your GenAI might be faking it. You ask it a question. It spits out an answer. Confident. Polished. Absolutely convincing. But what if that answer is built on broken data?
That’s what happens without DataOps. Silos stay siloed. Messy data goes unchecked. And your GenAI becomes less of a trustworthy decision amplifier and more of a random number generator.
But you know what? Fixing this isn’t as hard (or as costly) as you think.
Modern DataOps is the grease that makes your enterprise flow. When it works well, it makes sure your data is aligned and governed, and every answer your GenAI delivers is rock-solid. Get the full story in this blog.
Listen: Making Enterprise GenAI Work
In the latest Who’s Your Data? podcast, we chat with Gilad Barash to reveal how GenAI can truly serve enterprises.
Some fear GenAI will replace humans. But GenAI’s real power isn’t in replacing people. It’s in making them stronger. Faster. Smarter. A tool that helps teams work better across the board. This episode covers:
? The 3 waves of GenAI adoption—and what’s next.
? Why context matters more than ever. Without it, even smart GenAI models falter, leaving teams confused, wasting time, and eroding trust.
? How to break down data silos and finally connect business and tech teams.
A must-listen for anyone who wants to make GenAI work in the real world.
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???Happy Holidays and…
Stay Tuned!
Next month, we’ll share our 2025 predictions for data, governance, and GenAI. And continue to explore topics like responsible GenAI and decision augmentation.
??See you in 2025!
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