Most companies have Layer 3 and skip everything else.
Whether your AI is already giving wrong answers because of stale data or you're planning to build AI that needs current information, the answer is the same: Data Systems with all 6 layers.
Your data exists. It's in databases, spreadsheets, CRMs, ERPs, SaaS tools. You've invested in storage. You've bought platforms. The data is there.
But it sits in silos. Changes in one system don't propagate to others. Nobody knows which numbers are current. The same data gets entered manually into three different places.
You've built integrations. Some of them even work. But they're brittle. They break when vendors update APIs. They don't scale. They don't know when data is stale.
The data exists. It just doesn't work for you.
Having data isn't the same as having Data Systems.
Fix Perspective
Sound familiar? Your data exists. It just doesn't work for you. That's not a data problem. It's a systems problem.
Enhance Perspective
Planning to build AI? This is what happens if you skip the data foundation. AI is only as good as the data feeding it.
These are the patterns everyone tries. And the patterns everyone fails.
Connect System A to System B. Then System A to System C. Then B to C. Then add System D and connect it to everything.
Why it fails: Doesn't scale. With N systems, you need N² connections. Each one breaks independently. Change anything and half your integrations stop working.
Centralize everything in one place. Build a single source of truth. Run reports from there.
Why it fails: Becomes stale the moment it's built. Optimized for querying the past, not acting in the present. Great for dashboards, useless for real-time operations.
Expose APIs everywhere. Let systems call each other when they need data.
Why it fails: No orchestration. No awareness of what's fresh. No intelligence about what matters. Systems call each other blindly, hoping the data is current.
Fix Perspective
Sound familiar? These aren't execution failures. They're architecture failures. You can't solve a flow problem with more storage or more connections.
Enhance Perspective
Planning to try one of these? Don't. These patterns fail systematically. Build a real Data System instead.
Storage is Layer 3. Most companies have Layer 3. They're missing Layers 1, 2, 4, 5, and 6 entirely.
Data Systems have six layers. Each builds on the one before it. Skip a layer, and the system breaks.
This isn't theoretical. We've diagnosed enough broken data architectures to see the pattern. Every one that failed was missing at least one layer. Every one that worked had all six.
| Layer | Name | Purpose |
|---|---|---|
| 1 | Ingestion | Normalize inputs from many sources |
| 2 | Routing | Direct data to where it's needed |
| 3 | StorageWhat most have | Organize for different use cases |
| 4 | Scoring | Add intelligence about quality and importance |
| 5 | Freshness | Track what's current and what's stale |
| 6 | Multiplication | Make data serve multiple purposes |
Most companies have Layer 3. Maybe some Layer 1. They skip 2, 4, 5, and 6 entirely. Then they wonder why their data doesn't work.
Fix Perspective
If your integrations keep breaking or AI gives wrong answers, count how many layers you actually built. It's probably just Layer 3.
Enhance Perspective
This is the blueprint. Build all 6 layers before you deploy AI that needs current, scored, flowing data.
Data comes from everywhere. APIs, webhooks, file uploads, manual entry, third-party systems. Each source has its own format, its own conventions, its own quirks. Nothing speaks the same language.
A normalization layer that standardizes everything at the point of entry. Format conversion. Schema mapping. Validation rules. A unified ingestion pipeline that turns chaos into consistency. By the time data enters your system, it speaks one language.
Garbage in, garbage out. Downstream systems inherit format inconsistencies. Reports don't match because the same field means different things from different sources. Every downstream layer has to handle the chaos that should have been normalized at entry.
Before AI can work with your data, data needs to speak one language. If you're planning AI that pulls from multiple sources, build the ingestion layer first. Otherwise, your AI will inherit the chaos.
Data enters but doesn't flow. Systems are silos. When something updates in one place, other places don't know. Changes propagate manually, if they propagate at all. The same update gets entered in three systems by three people.
A rules engine that knows where data should go. Event-driven routing that reacts to changes. Multi-destination publishing that sends updates everywhere they're needed. When data changes in one place, affected systems know immediately.
Manual data entry between systems. Copy-paste workflows. Delays between when something happens and when systems reflect it. "Which system has the right data?" becomes a daily question.
If you're planning AI that needs current data, not yesterday's snapshot, you need routing. This is how data stays current across systems. Without it, your AI will work with stale information and you won't know until it gives wrong answers.
Data gets stored but isn't organized for use. One schema serves all purposes. The structure optimized for transactions doesn't work for analytics. The format good for reporting doesn't work for real-time access.
Multi-modal storage designed for different access patterns. Purpose-driven schemas that serve different use cases. Query-optimized structures for the access patterns that matter. The same data, organized multiple ways for different consumers.
Usually not skipped entirely. Everyone has databases. But poorly designed storage means slow queries, rigid schemas, and every new use case requiring a migration. The database becomes a bottleneck.
If you're planning AI that will access your data in new ways, design storage for those access patterns now. AI queries are different from transaction processing. Plan for both, or rebuild later.
All data is treated equally. There's no way to know which data is reliable and which is questionable. No way to prioritize important data over noise. No way to answer "how confident should I be in this?"
Confidence scoring based on source reliability and validation history. Quality scoring based on completeness and consistency. Importance weighting based on business rules. Every piece of data carries context about how much you should trust it.
All data weighted equally. Decisions made on unreliable data with no warning. No way to prioritize. Bad data poisons decisions just as much as good data informs them. The system can't tell you what to trust.
If you're planning AI that needs to know what to trust, build scoring. AI without data confidence is AI that presents garbage with the same authority as gold. Your users won't know the difference until something goes wrong.
You know data exists, but you don't know if it's current. That customer record might be from yesterday or last year. That inventory count might be real-time or a week old. Decisions made on stale data are decisions made on fiction.
TTL (time-to-live) policies that define how long data stays valid. Freshness scoring that decays over time. Staleness detection that flags data past its useful life. Update triggers that refresh data proactively. The system knows what's current and warns you about what isn't.
Reports that don't match reality. Decisions made on stale data with no warning. "Which number is right?" becomes impossible to answer. Trust in data erodes across the organization.
If you're planning AI that needs current information, not historical snapshots, build freshness awareness. This is the difference between AI that reflects reality and AI that reflects last week.
Data serves one purpose. Customer data lives in the CRM. Inventory data lives in the ERP. Financial data lives in accounting. Each dataset exists in isolation, serving its original purpose and nothing else. The potential for data to compound across uses is completely unrealized.
Cross-system enrichment that combines data from multiple sources. Derived data that creates new insights from existing information. Compound effects where data in one system automatically enhances data in others. One input, many outputs. Data that multiplies in value as it flows through your organization.
The same data captured multiple times in multiple places. No compound effects. Each system operates on its own incomplete picture. Massive wasted potential.
If you're planning AI that should compound in value, you need data that compounds in value. This is where the Compound Value philosophy becomes concrete. One input, many outputs. Build this, and every new data source makes every existing use case smarter.
This is the layer most companies never reach. They stop at storage. Maybe routing. But multiplication is where data becomes infrastructure. It's the difference between data that exists and data that works.
Data Systems aren't just one of four systems. They're the connective tissue that makes all the others work.
Store knowledge as data artifacts. When those artifacts go stale, knowledge becomes unreliable. Data freshness directly affects knowledge accuracy.
Need current, scored data to make good decisions. A decision framework is only as good as the data feeding it. Data Systems provide the foundation for informed choices.
Triggered by data events. A new order creates data that triggers fulfillment. A status change creates data that triggers notifications. Without data routing, processes don't know when to start.
AI Assistants need current data to answer accurately. Intelligent Workflows need triggers and routing. Data Infrastructure is literally Data Systems productized.
Fix Perspective
Build Data Systems right, and your existing AI investments start working. The AI was fine. The data underneath wasn't.
Enhance Perspective
Build Data Systems first, and every AI capability you add later works from day one. No stale answers. No 'I don't know.' No conflicting information.
A conversation to understand your current data state, identify what's missing, and see what getting this right would enable.
Questions from founders whose integrations keep breaking and whose AI gives stale answers.
Gartner estimates poor data quality costs enterprises $12.9 to $15 million annually. That's not hypothetical. It's productivity drops, duplicate work, missed opportunities, and decisions made on wrong information. About 68% of organizations now rank data silos as their biggest challenge, up 7% from last year. The cost is distributed across departments in ways that make it invisible until you add it up. You're paying for bad data whether you measure it or not.