Beyond Gold: Introducing the Platinum Layer for AI-Ready Data
- Dave Findlay
- Aug 14
- 3 min read
For decades, enterprise data architectures have been built for human consumption.
We’ve refined and matured them into recognizable layers:
Bronze: Raw, un-modeled data in its original form.
Silver: Cleaned, conformed data, joined, standardized, and deduplicated.
Gold: Curated, business-ready models (fact/dimension schemas, KPIs, and standardized metrics) for analytics and BI tools.
This Bronze → Silver → Gold progression works because the end consumer has always been a person: an analyst, an executive, a business user.
They log into dashboards, slice and dice, and use the Gold layer to inform decisions.
But what happens when your first “user” isn’t a human?
Enter the Platinum Layer
We’re entering an era where LLMs and AI agents will query your data directly and act before a person is even aware a decision needs to be made.
That requires a new layer above Gold. A layer optimized not for human eyes, but for machine actions.
Platinum is:
Agent-ready: Structured for direct programmatic use, without a manual interpretation step.
Decision-first: Designed around triggers, actions, and outcomes rather than exploratory analysis.
Context-rich: Includes metadata, definitions, and business rules that a human might know instinctively but an AI would miss.
Guardrailed: Embedded quality checks, anomaly detection, and clear error handling so agents don’t confidently act on bad data.
How Platinum Differs from OLAP
This is where many teams get confused. OLAP and aggregate tables also sit “above” Gold, but OLAP is for human acceleration, not machine autonomy.
OLAP (Human) | Platinum (Agent) |
Accelerates slice-and-dice exploration | Delivers one-row-per-decision, action-oriented data |
Optimized for visual queries in dashboards | Optimized for API or direct SQL/Parquet access |
Designed around human mental models | Designed around explicit, machine-readable semantics |
Tolerates some ambiguity (analyst can spot issues) | Eliminates ambiguity. Machines can’t spot “this looks wrong” |
In other words:
OLAP is a magnifying glass for people. Platinum is a flight plan for machines.
What Goes Into Platinum
Decision-Oriented Views
Tables that directly answer “Should we act?” questions.
Example: customers_at_risk instead of customer_activity_facts.
Features & Signals
Denormalized, machine-friendly tables that feed ML models or trigger rules.
Example: purchase_frequency_last_30_days, basket_size_trend.
Health & Quality Indicators
Data freshness, anomaly scores, and pipeline health status as first-class columns.
Example: data_freshness_minutes, price_anomaly_flag.
Semantics & Metadata
Clear metric definitions, business logic, and lineage exposed in a way that agents can parse.
Guardrails & Policies
Rules that prevent actions on incomplete or stale data.
Example: “Do not trigger if data_freshness_minutes > 60.”
A Grocery Retailer Example
Scenario:
A large grocery chain wants AI agents to autonomously adjust promotional discounts based on sales performance.
Traditional Gold Layer Output:
A set of curated fact/dimension tables:
sales_fact (with product, store, date, units sold, revenue)
product_dim, store_dim
BI dashboard for category managers to explore trends.
A human would open a dashboard, see that “Organic Avocados” are underperforming in the West region, and manually adjust promotions.
Platinum Layer Output:
A decision-ready, agent-readable table:
promotion_adjustment_candidates
--------------------------------
product_id
store_region
units_sold_last_week
expected_units_sold
sales_variance_pct
action_recommendation
data_freshness_minutes
quality_scoreWhat Happens Next:
The AI agent queries promotion_adjustment_candidates.
It finds products where sales_variance_pct < -15% and quality_score > 0.9.
It sends an action request to the promotion system: “Increase discount by 5% for Organic Avocados in West region stores.”
All of this happens without a human logging into a dashboard, though humans still receive alerts for oversight.
The takeaway
When AI is your first consumer, data modeling becomes product design.
You’re no longer designing for exploratory analysis. You’re designing for safe, reliable, automated action.
That means the Platinum layer is not optional. It’s the contract between your data and your AI.
At Fuse, we believe a great data strategy only matters if it leads to action.
If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.
