ElektraOS for SMB . Pillar 02
02 . Data and Analytics

A real data stack, without a data team.

Warehouse, ingestion pipelines, dashboards and data-quality checks. Not another BI tool bolted onto a spreadsheet; a managed pipeline from source systems to reliable answers, with weekly review of every metric that drives a decision.

What is included

Enterprise data engineering discipline, scoped for SMB surface area. Every layer of the stack is owned by one engineer, not outsourced to a vendor per step.

Layer . Ingestion

Source connectors and ETL

Pull from the systems the team already pays for: Shopify, Stripe, Xero, HubSpot, Klaviyo, Google Analytics, bespoke SQL. Scheduled, idempotent, monitored.

Layer . Storage

Managed warehouse

Postgres or BigQuery depending on volume. One schema per tenant, partitioned for growth. No shared tables across clients.

Layer . Modelling

Tables the team can read

Versioned SQL models for the business entities that matter: orders, customers, products, sessions, cohorts. Column names that match the operator's language.

Layer . Analytics

Dashboards that answer one question each

Metabase or Looker Studio, focused boards per role: founder, head of ops, head of marketing. Every chart carries the SQL and a refresh timestamp.

Layer . Quality

Data-quality checks

Row counts, freshness, referential integrity, outlier detection. Failures go to the operator channel before the dashboard lies.

Cadence

Weekly data review

Thirty minutes to walk the dashboards, flag what is broken, decide what to build next. Not a monthly report; a working session.

First 90 days

From zero visibility to three trustworthy dashboards. Each milestone is a shipped artifact the operator can open.

Day 0 to 30

Inventory and schema v0

  • Map every source of truth the team uses
  • Pick the three highest-value streams
  • Provision warehouse and tenant schema
  • Schema v0 committed to cohort repo
Day 31 to 60

Ingestion and first dashboards

  • Connectors for the top three sources live
  • Modelled tables for orders and customers
  • Two focused dashboards shipped
  • Quality checks wired to alerts
Day 61 to 90

Trust and expand

  • One more data source integrated
  • Third dashboard by role
  • Backfill historical and reconcile totals
  • Weekly review cadence locked in

Proof this is not greenfield

Engineer credentials plus runtime references. The SMB package is the downshift of patterns already running.

Engineer
Zacki Ziane, Kubrick Group, London. Client engagement with AstraZeneca on enterprise data pipelines. University of Nottingham.
Runtime
ElektraOS SQLite core: 76 tables, 19.9MB production database. Verified Apr 10, 2026. Target warehouse upgrade path Postgres and BigQuery.
MealShift signals
Platform activity tracking shipped Apr 22. Live event feed across commerce surfaces feeds the analytics layer.
Quality pattern
Loomgraph compliance scanner: regex-gated validation, hashed audit rows. Same approach applied to data-quality checks.
Z
Zacki Ziane
Pillar lead . Data engineering

Data Management Consultant at Kubrick Group, client engagement with AstraZeneca. London-based. Brings warehouse and ingestion design, data quality and observability, SQL and schema modelling. The enterprise-to-SMB downshift that most SMB tools miss.

LinkedIn

Pillar 02 lead contact

If the team cannot currently answer a basic question like "what did we sell last week, net of refunds" in under a minute, this pillar is the fix.

Apply to the cohort Back to SMB