When It Comes to Data… Don’t Stumble from the Kick-Off
Why most data strategies fail before they even start—and how to get the kick-off right
Every CIO and IT organization has lived through the drama: a new data platform, a massive migration, or the launch of a shiny analytics initiative. Millions spent. Countless hours consumed. The best of intentions, and yet too often, a disappointing finish.
The rise of AI has only amplified the pressure. Suddenly, “better data” and “better systems” are no longer aspirational; they are demanded! Technical teams are now asked to solve problems that have stymied strategic and tactical initiatives for decades. Silos that once worked in isolation are being forced to play in a much bigger sandbox, and the cracks are showing.
In fact, “data” has become the new war cry for why so many AI projects fail. But let’s be honest; it doesn’t matter whether the initiative is AI, cloud modernization, or the latest enterprise platform. The problem is always the same. The data problem is still the data problem.
And here’s the kicker: the culprit isn’t the technology stack, the business requirements, or even the transformation logic. It’s something far simpler, but far harder to get right. It happens at the starting gate … the kick-off.
The very first move on how you extract and capture data sets the tone for everything that follows. Get it wrong, and the pipeline, governance model, and analytics framework are compromised before you even start. In AI, that means broken RAG pipelines and models that don’t deliver. In Agentic AI and automation, it’s much worse. It means never getting off the ground and stuck with the same siloed, bespoke, and brittle efforts.
From ETL to ELT (and Still Stuck at “E”)
I’ve been around long enough myself to watch this play out more times than I can count. Decades ago, we had ETL. Extract, transform, and load. Then came ELT. Promised to be a new and better way. Even that was over a decade ago. It was a fashionable inversion that essentially punted the hardest problem further down the field. Vendors built dazzling dashboards, cloud warehouses, and real-time streaming frameworks, but the Achilles’ heel never changed: the “E.”
Figure out the “E” … the Achilles heel in data integration.
Extraction is where reality collides with ambition. It’s where accuracy meets context. And it’s context — the meaning of data in its business, operational, and compliance setting — where most organizations stumble.
And yes, we continue to see it over and over and over again. Critical knowledge about how fields map, what a column really represents, or why a calculation is valid is rarely captured in the system itself. Instead, it’s locked inside someone’s head, hidden in a spreadsheet, or buried in a PDF document. Lose that person, and you lose the knowledge … the playbook. You’re back at square one.
Here’s the part no one likes to admit: all those pipelines, ETL jobs, and AI models are quietly propped up by humans injecting undocumented knowledge every step of the way. The tech teams and analysts configuring these systems are making judgment calls, a lot of times unconsciously, that never get recorded. That “tribal knowledge” becomes the invisible glue holding everything together. And when it disappears, the business is left compromised.
The Real Trick is Getting “E” Right
At Charli, we’ve learned a simple but powerful truth; if you solve the “E,” everything else including transformation, loading, modeling, and reporting becomes manageable. Not trivial, not easy, but solvable.
Why? Because extraction done right isn’t about pulling numbers or labels. Any old script can do that. The real challenge and the real value is context capture. That’s the “tribal knowledge” that gets overlooked, yet it’s what makes the difference between a brittle integration and a resilient data fabric.
Keep reminding the humans: what’s in their heads about data needs to be codified — whether it is codified in AI or not.
At Charli, when our systems encounter data from a structured feed, a regulatory filing, a contract, or a 200-page annual report, we don’t just scrape values. We pass it through a mesh network of AI extractors, each tuned to interpret signals, infer context, and preserve the narrative behind the numbers. That context, along with the extracted values, is normalized and stored inside what we call our Contextual Memory Architecture™.
Think of it as an institutional memory that never forgets. It captures the nuance that usually disappears over time. The “why” behind the data, not just the “what.”
So when you hear us talk about Dynamic On-Demand Ontology, Contextual Cross-Retrieval or Contextual Memory Architecture™, it’s not jargon. It’s the backbone of a system built to capture both data and meaning, ensuring downstream AI can analyze intelligently.
Even the way we define ‘context’ is broader and richer than what most LLMs or vendors describe. For them, context is narrowly scoped. For us, context is the deep, diverse, multidimensional metadata that binds your entire data strategy together.
Why Enterprises Keep Fumbling the Ball
Most organizations race toward the end zone on use cases, dashboards, advanced analytics, AI pilots, and automated workflows without securing the ball at kick-off. They pour millions into outcome-driven layers while ignoring the fragile foundation. Then, when models misfire, pipelines collapse, or compliance alarms go off, they scramble for “root cause analysis.” Spoiler … they’re not finding the root causes … they are just patching symptoms.
And the root cause? Almost always the same: extraction without context.
Now we’re seeing the latest spin. Companies pressing pause on AI to make their data “AI ready.” Seriously? What does that even mean? It’s the corporate equivalent of announcing you’re “getting in shape” after a decade of failed diets.
“AI ready” usually means teams ran head-first into hallucinations, brittle pipelines, or leaned on LLMs for jobs LLMs were never meant to do. And it’s not the first time we’ve seen this. Back in the day it wasn’t called “AI ready” — it was “we’re moving to ELT” or “the data lake will be our ideal system” or “Big Data will change the game.” Same play, different jersey. And if your answer today is to pile on more humans, don’t be surprised when in 18–24 months from now you’re shouting the same angst.
At Charli, we learned not to play that game. Back in the day, our team didn’t fall into the IoT data mess on the industrial side, and we’ve moved well past the old-school AI/ML stacks. Now, we let AI do what it’s supposed to do and make sure the data is always AI ready. It’s specialized AI getting the data ready for AI. Not a one-off project, but built-in foundational intelligence that just takes care of the data — the “E.”
The Takeaways
If you’re responsible for enterprise data, here are the must-haves:
Forget the “T” and the “L” until you’ve mastered the “E.” Without a solid foundation, the rest is just window dressing.
Make context capture a first-class requirement, not an afterthought. Data without meaning is noise.
Codify knowledge into the system, not people’s heads. Institutional memory should live in your platform, not walk out the door with staff turnover.
Stop confusing “LLM = AI.” The right AI should handle your data as-is, with context. Not every problem is a language model problem.
At Charli, this is why we can work with any kind of data — structured, unstructured, or semi-structured — without breaking stride. We didn’t start with dashboards or clever APIs. We started where it matters most: the kick-off.
Get that right, and everything else falls into place. Fumble it, and no amount of transformation wizardry, or AI hype, will save you.
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