Fragmented data → broken trust
Teams lose hours reconciling spreadsheets, inbox attachments, and point tools. No one agrees on a single number, so automation never takes root.
Phase 1
Phase 1 — Unified Data Operating System
One truth. All signals. Build shared truth from governed logs and consented context.
We make the invisible foundation tangible. Every metric, alert, and workflow is traceable back to governed, consented data.
Snow White Laundry proves the thesis: engineered truth unlocks ethical automation without sacrificing trust.
Why Phase 1 exists
Evidence before empathy
No provenance → unverifiable decisions
Without lineage, leaders can’t prove why a decision was made. Compliance, finance, and frontline staff all operate off different histories.
No consent → ethical risk
Data is collected faster than policies evolve. Customers and staff have no clarity on how their data powers daily operations.
Unified Data OS = governed proof
Phase 1 installs a single operating system for context: ingestion, consent, lineage, and audit anchors. We engineer truth so later empathy and automation stand on verifiable ground.
The unified stack
Sources → Staging → Clean → Gold → Proof
One governed pipeline. Every handoff is verifiable, consent-aware, and ready for audit.
Sources
POS, sheets, finance exports, Gmail invoices, IoT sensors streaming events with signed metadata.
Staging
Raw ingestion with schema inference, consent tagging, anomaly flags, and quarantine queues.
Clean Views
Conformed tables with column-level lineage, PII minimization, and business-ready dimensions.
Gold Views
Finance, operations, and customer lenses with cross-system reconciliation baked in.
Proof Anchors
Temporal ledger storing hashes, consent state, and audit trails. Nothing personal leaves the vault.
Data Health KPIs
Measure trust like a product
Each KPI feeds the composite Data Trust Score — the heartbeat of Phase 1.
92Data Trust Score
Completeness %
96%Required data populated across priority tables.
Freshness (h)
1.4hMedian lag from source event to usable record.
Lineage Depth
4.2 hopsAverage hops source → gold view with proof.
Consent Coverage %
94%Records tagged with valid consent + policy.
Reconciliation Accuracy %
98%POS ↔ bank settlements matching nightly.
Snow White Laundry · Phase 1 Pilot
Ethical data in the real world
St. John’s NL · 32-seat restaurant. Every shift now runs on governed data with provable consent and lineage.
Phase 1 unified POS tickets, supplier invoices, labor shifts, and bank settlements. Consent events are tracked at the record level, so SWL can prove what data powers each report. The Assistant flags anomalies, while Temporal anchors proofs nightly.
Food waste
↓ 15%
Reconciliation accuracy
98%
Consent coverage
94%
12-month roadmap
Proof in quarters, not slogans
Each quarter expands the unified data OS while keeping consent and audit signals first-class.
Architecture Deep-Dive
Data model snapshot
Tables reflect the same schema we run for Snow White Laundry. Consent tags and lineage metadata stay first-class.
create table data_sources (
id uuid primary key,
name text not null,
system text not null,
consent_scope text,
created_at timestamptz default now()
);
create table raw_events (
id bigserial primary key,
source_id uuid references data_sources(id),
payload jsonb not null,
received_at timestamptz default now()
);
create table etl_runs (
id uuid primary key,
pipeline text,
status text,
started_at timestamptz,
finished_at timestamptz,
lineage jsonb
);
create table consent_events (
id uuid primary key,
subject_id uuid,
data_use text,
consent_state text,
recorded_at timestamptz default now()
);
create materialized view mv_data_health as
select
current_timestamp as calculated_at,
completeness_ratio,
freshness_hours,
lineage_depth,
consent_coverage,
reconciliation_accuracy
from data_health_snapshots;