Data & Analytics

Warehousing, dashboards, ops telemetry, cost/route KPIs, forecasting.
Turn scattered data into decisions your team can act on every day.
Logistics use-case? See Supply Chain & Logistics Software.

When this makes sense

1

You can’t trust numbers across tools (CRM, WMS/TMS, finance, driver apps).

2

Ops wants live KPIs (on-time %, dwell, cost per mile/stop) instead of static reports.

3

Leadership needs forecasting (volume, capacity, revenue) to plan crews and spend.

4

You want alerts when something drifts: data quality, ETA accuracy, route variance.

What you get

Data Ingestion & Pipelines (ELT/ETL)

  • Connect CRM, WMS/TMS, telematics, driver apps, spreadsheets, accounting.

  • Batch + streaming where useful; backfill history without downtime.

Warehouse / Lakehouse

  • Snowflake / BigQuery / Postgres (fit to scale & budget).

  • dbt models for clean, versioned, testable transformations.

Metrics Layer & KPI Dictionary

  • One definition per metric: cost/stop, on-time %, first-attempt success, dwell, route vs. plan.

  • SLOs/targets baked in for red/amber/green states.

Dashboards & Self-Serve

  • Executive, Ops, Finance views (Metabase / Looker / Power BI / Superset).

  • Drill-through from summary → lane/site/driver/job.

Ops Telemetry

  • Event streams for job status, retries, API latency, device health.

  • Incident panels + runbooks to shorten MTTR.

Forecasting & Optimization

  • Demand by lane/day, capacity & staffing, ETA calibration, inventory turns.

  • Proven methods (Prophet/XGBoost) with feature store + backtests.

Alerts & Automations

  • Threshold/anomaly alerts to Slack/Email/SMS.

  • Trigger workflows (e.g., dwell > X → ping dispatcher with context).

 Governance & Security

  • Trigger workflows (e.g., dwell > X → ping dispatcher with context).

  • Data tests (Great Expectations) and audit trails by default.

Logistics KPIs we commonly implement

Cost per mile/stop, on-time %, first-attempt success, dwell time, route vs. plan variance

Driver/vehicle productivity, utilization, exceptions by cause/site

Customer SLA adherence, refund/chargeback rate, claims per 1,000 deliveries

Tooling we use

Deliverables

Source inventory & integration plan

Warehouse schema + dbt repo (tests + docs)

KPI dictionary (clear, versioned definitions)

Dashboard set (exec/ops/finance) + alert playbooks

Forecasting PoC with backtests & accuracy report

Runbook for ops + knowledge transfer

Process & timeline

1

KPI workshop & source audit (3–5 days)

2

ELT + warehouse + first dashboards (1–2 weeks)

3

Telemetry & alerts (3–5 days)

4

Forecasting PoC (2–4 weeks)

5

Hardening & handover (ongoing support optional)

FAQ

Not always—many SMBs run great on Postgres.

Not always—many SMBs run great on Postgres.

dbt tests, data contracts, and a shared KPI dictionary.

You do-code, models, dashboards, and infra scripts.

Ready to see your KPIs in one pane of glass?

Book a discovery call and we’ll map your first dashboards and data plan.

Book a discovery call and we’ll map your first dashboards and data plan.