Book a data teardown DATA ENGINEERING + BUSINESS INTELLIGENCE FOR ECOMMERCE
Not another dashboard. The system underneath the answer.
Tribal Media builds decision-grade data infrastructure for growing ecommerce brands: collection, warehouse, governed models, business intelligence, and responsible AI enablement. Each engagement starts with a consequential decision, then builds only the layers required to answer it well.
01 / QUESTIONS
The answer should survive the meeting.
Which products and offers create profit after returns, discounts, and fulfillment?
Can every important metric show its definition, freshness, owner, and source?
02 / FAULT LINES
Find where trust breaks before choosing the tool.
Fragmented sources
Commerce, paid media, lifecycle, support, subscription, inventory, fulfillment, and finance systems each describe a different piece of the business.
Disputed definitions
CAC, revenue, active customer, contribution, and retention change between tools or teams because business logic is scattered and undocumented.
Fragile delivery
Recurring answers still depend on one person, manual exports, or dashboards that cannot explain why a number changed.
03 / SCOPE
A right-sized data capability, sequenced around the decision.
Data engineering
Build a reliable path from operational sources to an analytical environment the company controls.
- Collection and source audits
- Warehouse and ingestion architecture
- Orchestration, backfills, and freshness checks
Business intelligence
Turn raw records into governed business concepts and usable decision products.
- Analytics and semantic modeling
- Metric definitions, tests, and lineage
- Scorecards, analysis, alerts, and planning inputs
AI readiness
Prepare approved data and tools for AI only after the meaning, access, and evaluation rules are explicit.
- Use-case and readiness assessment
- Permissions, provenance, and guardrails
- Evaluation, monitoring, and human escalation
04 / SOURCE TO DECISION
One governed path from source to decision.
We do not force every client through all five layers at once. A readiness audit identifies the expensive gaps; a focused sprint can take one priority question into production; an embedded engagement can evolve the system over time.
Instrument
Define the events, entities, consent, and identity behavior the decision requires.
Warehouse
Bring useful source data into an owned analytical environment with freshness controls.
Model
Create tested definitions for customers, orders, contribution, cohorts, and other business concepts.
Decide
Deliver governed answers through scorecards, analysis, alerts, briefs, or custom interfaces.
Enable AI
Expose approved data to evaluated AI workflows with permissions and human escalation.
START WITH THE EXPENSIVE UNKNOWN
Bring us the answer you don’t trust.
We’ll review the question and follow up to determine whether a focused teardown is the right next step. The first job is locating the break: collection, modeling, governance, or delivery.