Do We Actually Need Data Models?

Different People carry different attitudes toward data modeling - and each one is valid to some degree.

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Modeling is Bureaucracy.

• Right. No one wants to build boxes when you can ship a product. But the reality is that you are already modeling. Its just implicit, inconsistent, and in 5 places (SQL, dashboards, docs, excels, Slack, and someone's memory). That's just one of the reasons that creates rework and mistrust.

Only Data Architects Care.

• Right, but wrong. Creating data models is not just for 'data people'. Architects want to design the organization's data well. But when business terms aren't defined by the business domain experts, business leaders pay the price: conflicting KPIs, unclear ownership, slow decisions, and not knowing which numbers to believe. Definitions help the data pipeline.

We Tried Modeling. It didn't work.

• Right, we've seen a lot of failed attempts too, so why would it work this time? Most modeling fails because it's disconnected from delivery and change, and also because it's missing from the culture. If the model doesn't stay close and connected to sources, lineage, and implementation, and isn't a part of the business culture, it becomes an artifact, not a product.

AI Will Handle It.

• Right, you want to prompt your way out of the trouble. AI accelerates drafting, but without governed, human-defined context, you scale only inaccuracy risk. And the trouble is, someone needs to validate what the Customer means anyway.

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And the real pains that unite these attitudes?

• You can't trust the numbers
• You don't know where fields come from
• Every change breaks something downstream
• You keep rebuilding logic that already exists

Get Started with Ellie.ai

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Step 1. Pick One Painful Use Case

Choose the thing that's already bleeding time:

• conflicting KPI definitions
• unclear lineage for a critical metric
• onboarding a new domain/source
• chaos during delivery - 'where is the truth'

Step 2. Ellie Drafts Fast, You Validate

• Generate a first-draft conceptual model with AI, or
• Reverse engineer from your existing sources and auto-annotate metadata.

Step 3. Align Business + Tech In One Shared Artifact

• Expectation: You'll stop debating opinions and start editing one shared reality

Step 4. Turn It Into A Delivery

• Use the model as your first blueprint for implementation, documentation, and governance - so it stays alive.

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What Should You NOT Expect?

A tool that replaces thinking.

What Should You Expect?

A tool that makes thining faster, shared, and operational.

If you're a Data Architect or on the Data Teams, you'll love Ellie if you're tired of:

• reverse-engineering the same systems repeatedly
• definitions living outside the work
• lineage being 'ask Joe'
• Governance arriving too late

If your a Business Leader or Product Owner, you'll love Ellie if you want:

• fewer metric disputes
• faster time from question to answer
• confidence that data outputs reflect business truth
• visibility into what the data teams do with definitions, and how that becomes data you need

If you're an Analytics or Engineering Team member, you'll love Ellie if you want:

• faster source discovery
• less rework
• fewer surprises in downstream pipelines
• a reliable blueprint you can implement

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