AEVO

Data governance & lakehouse foundations

Build the data foundation for enterprise decisions. AEVO connects governance, data lakes and Medallion pipelines to analytics, AI and decision optimization.

AI-generated editorial image of enterprise data specialists discussing a shared data-platform architecture.

Decision summary

The management question
Can teams reconcile orders, stock and capacity across systems, trust their freshness and explain which records informed a decision?
What we deliver
A scoped governance model, source inventory, Bronze/Silver/Gold pipelines, documented data products and a reviewed interface to the Decision Engine.
How value is measured
  • Freshness against agreed limits
  • reconciliation exceptions
  • lineage coverage
  • pipeline recovery
  • decision-input readiness

Start with the operating evidence.

Begin with the decision that needs better evidence. Map ERP, CRM, MES, WMS, IoT and financial sources; agree ownership, access, retention and refresh expectations. Reconcile identifiers, time zones, units and source-of-truth rules before designing a shared data lake or lakehouse.

Compare feasible alternatives.

Retain source history in Bronze, validate and integrate entities in Silver, then publish domain datasets in Gold. Establish quality checks and recovery paths at each handoff. Connect approved data products to BI, ML and the Decision Engine, where objectives, operating constraints and human approval determine the permitted action.

Explore the data-to-decision architecture

Every layer has a purpose. Every decision has an owner.

Follow enterprise data from source records to a governed recommendation. Select a stage to see its role, handoff and business relevance.

Enterprise data sources

ERP · CRM · MES · WMS · IoT · finance · external data

ML & detailed analysis

Governed Silver datasets can also support ML and detailed analysis. Consumers do not all need the same Gold product.

Data products meet decision logic

Gold supports business data products and BI. Approved datasets enter the Decision Engine together with objectives, constraints and approval rules; analytics alone does not authorize an action.

Bronze — preserve the source evidence

Role in the architecture
Capture source records with timestamps and ingestion metadata, retaining history for the agreed period.
What moves forward
Replayable source snapshots with provenance, ready for validation.
Why it matters to the decision
When a number changes, inspect the original evidence instead of guessing which upstream update caused it.

Silver — establish a consistent business view

Role in the architecture
Validate, deduplicate and reconcile shared entities; standardize identifiers and units, and isolate exceptions.
What moves forward
Defined, validated datasets for downstream business products, ML or detailed analysis.
Why it matters to the decision
Orders, stock and suppliers can share a common meaning before competing plans are compared.

Gold — publish business data products

Role in the architecture
Apply agreed business rules and create purpose-specific datasets, KPIs or features, at the grain each consumer needs.
What moves forward
Documented data products with owners, definitions, refresh times and versions.
Why it matters to the decision
A planning team can see which availability, cost or demand dataset its recommendation relies on.

Decision Engine — compare feasible actions

Role in the architecture
Combine approved data with business objectives, forecasts and operating constraints; evaluate alternatives with AI and optimization.
What moves forward
Reviewable recommendations showing assumptions, trade-offs, binding constraints and input versions.
Why it matters to the decision
A forecast informs the choice; objectives and constraints determine which choices remain feasible.

Review, act & learn

Role in the architecture
Assign an authorized reviewer, apply approval limits and connect execution only within the agreed scope.
What moves forward
Approval and execution records, measured outcomes and feedback for the next reviewed cycle.
Why it matters to the decision
Keep a clear boundary between a suggested action, an approved plan and what actually happened.

Governance across every layer

Ownership · access · quality · lineage · retention · monitoring

Conceptual architecture with illustrative flows. No customer data is processed. Layer design, controls and integrations are agreed for the operating context.

A practical path from governance to decisions.

Begin with a business workflow and extend the platform as its value and operating requirements become clear.

  1. Agree the decision & governance scope

    Identify the business owner, source systems and existing definitions. Agree access, retention, quality thresholds and the first decision to evaluate.

  2. Build the layered data foundation

    Design ingestion and Bronze/Silver/Gold responsibilities. Reconcile business entities, isolate exceptions and document refresh and recovery paths.

  3. Connect reusable data products

    Publish datasets with definitions, owners and versions. Map them to BI, ML and decision inputs; agree objectives and operating constraints with the responsible teams.

  4. Validate, review & operate

    Run a shadow comparison, exercise stale-data and recovery cases, and retain human approval. Monitor actual outcomes before expanding permitted automation.

Governance runs through every layer.

Access permissions, ownership, quality rules, lineage, retention and monitoring span Bronze, Silver and Gold. Link data and model versions to the review record so a recommendation can be traced back to its source evidence.

Agree how value will be measured.

Freshness against agreed limits · reconciliation exceptions · lineage coverage · pipeline recovery · decision-input readiness

Discuss your data-to-decision workflow

Choose the architecture around the business.

Medallion is a design pattern, not a required vendor purchase. AEVO assesses data lake or lakehouse choices against existing platforms, security, volume, latency, operating skills and budget. Databricks is a reference implementation; technology, licensing, infrastructure costs and delivery scope are agreed for each engagement.

Read the data-to-decision perspective

See the decision with synthetic data.

Industries

Related perspectives

Data governance & lakehouse foundations

From signals to accountable action.

A scoped governance model, source inventory, Bronze/Silver/Gold pipelines, documented data products and a reviewed interface to the Decision Engine.

  1. 01 Business signals

    Start with the operating reality.

    Orderbook · capacity · stock

  2. 02 ML forecast

    Understand what may change.

    Expected demand · uncertainty range

  3. 03 OR & constraints

    Compare what is feasible.

    Feasible plans · explicit trade-offs

  4. 04 Explanation

    Show the reasoning behind the plan.

    Rationale · assumptions · alternatives

  5. 05 Human review

    Keep responsibility with people.

    Decision owner · approval · follow-up

  6. Illustrative workflow · synthetic inputs · Conceptual illustration of the workflow. No customer results or live AI inference are shown.

Start with a decision that matters.

Tell us where cost, capacity, risk or cash is limiting performance. We will help define a focused evaluation before committing to a wider transformation.

Discuss your challenge Decision Engine