Data & Analytics Platform
KPI and GIS dashboards on a governed warehouse, with data-quality checks and alerting so the numbers your team acts on hold up.
- Timeline
- 3-6 weeks
- Team
- Data Engineer · Frontend Dashboard Developer · Data Analyst · Project Manager
- Typical stack
- Data Warehouses: Snowflake, BigQuery, Redshift, Azure Synapse, Databricks. GIS: ArcGIS Enterprise/Online, QGIS, Mapbox, Google Maps Platform, PostGIS. BI Tools: Tableau, Power BI, Looker, Metabase, custom React dashboards with Recharts/D3. Data Quality: Great Expectations, dbt tests, custom Python validators, Soda. ETL/ELT: Fivetran, Airbyte, dbt, custom Airflow/Prefect DAGs. Orchestration: Airflow, Prefect, dbt Cloud. Integration: REST/GraphQL APIs, CDC (Debezium), message queues (Kafka, RabbitMQ). Observability: Prometheus, Grafana, Monte Carlo, elementary. Export formats: CSV, Excel, PDF, Shapefiles, GeoJSON, ArcGIS Feature Service.
What you get
- Data connectors with retry logic and monitoring (source → warehouse)
- KPI catalog with definitions, owners, refresh schedules, and SLAs
- Data quality pipeline: profiling, validation rules, alerts on critical failures
- GIS-enabled dashboards with zoom, filter, layer controls, and export
- Narrative report generator with automated summaries and anomaly detection
- Runbook: troubleshooting, scaling, adding KPIs, data refresh procedures
Outcomes
- Live KPI dashboards with GIS layers and drill-down capabilities
- Data quality pipeline with automated alerts on critical field failures
- Scheduled narrative reports with anomaly detection and trend analysis
- Measurable reduction in report prep time (typically 75%)
- Self-service analytics with governed data catalog
Selected work
$42M in cargo saved. 250K shipments monitored.
Real-time IoT monitoring across 340 pharma facilities, 3.5M telemetry pings/hour. Excursion discovery went from 6-8 hours to 4.2 minutes with a 15-minute intervention window; 8,400 critical excursions caught in year one. FDA 21 CFR Part 11 compliant.
Rails · MQTT · TimescaleDB · IoT
p95 latency ↓46% in 6 weeks
p95 840ms → 450ms. Re-platformed hot paths, added tracing, tuned indices. No feature freeze. Infra spend down 21% ($42k → $33k/mo); 7 critical CVEs closed before go-live.
Rails · Postgres · Grafana
How we approach it
Cloud-Native (Snowflake/BigQuery)
- When:
- Need scalability, managed services, ML integration, multi-region
- Tradeoffs:
- Best performance and features, cloud costs, requires connectivity
- Best for:
- Enterprises with cloud-first strategy, high data volumes
Hybrid (Cloud + On-Prem)
- When:
- Regulatory constraints, sensitive data on-prem, cloud analytics
- Tradeoffs:
- Balanced compliance and capabilities, moderate complexity
- Best for:
- Healthcare, finance, government with data residency requirements
On-Prem Only
- When:
- Air-gapped environments, full data sovereignty required
- Tradeoffs:
- Complete control, higher ops burden, limited scalability
- Best for:
- Defense, critical infrastructure, strict compliance environments
Where teams use it
Manufacturing
Production KPI dashboards with OEE & yield tracking
Real-time equipment health monitoring with downtime root cause analysis and predictive maintenance alerts integrated with EAM systems
Transportation & Rail
Track health GIS with maintenance priority heat maps
Visual inspection data overlaid on GIS with automated work order generation for high-priority segments based on condition scores
Warehousing & Logistics
Real-time inventory flow with geofence alerts
Package movement tracking across facilities with dwell-time anomalies and capacity utilization dashboards for operational optimization
Energy & Utilities
Asset health dashboards with outage prediction
Grid asset monitoring with failure prediction models, outage impact zones, and crew dispatch optimization via GIS routing
What we need from you
- Data sources and existing workbooks to replace
- KPI definitions and data stewardship owners
- Data quality target tolerances and thresholds
- Security roles and SSO configuration scopes
- Reporting cadence and target audiences
- GIS layer requirements and spatial data sources
Proof points
- KPI catalog with owners, definitions, SLAs, and refresh schedules
- Data quality scorecard: pass rates, failure alerts, remediation time
- Dashboard usage metrics: active users, queries/day, p95 query latency
- Before/after report prep time analysis (manual vs automated)
- GIS layer performance: render time, zoom responsiveness, concurrent users
- Data pipeline SLA tracking: uptime %, late runs, failure recovery time
Built for procurement
- Data quality SLAs: Measurable thresholds for completeness, accuracy, timeliness with automated alerts
- GIS export formats: Shapefiles, GeoJSON, KML, ArcGIS Feature Service, WMS/WFS endpoints
- Dashboard access controls: RBAC, row-level security, SSO/SAML integration, audit logs
- Data lineage tracking: End-to-end visibility from source to dashboard with transformation documentation
- Performance SLAs: Query latency targets (p95 < 3s), dashboard load time, concurrent user capacity
- On-prem/GovCloud deployment: Air-gapped installation support, FedRAMP considerations
- Compliance: SOC 2 Type II, HIPAA-ready architecture, data retention policies, PII handling
Frequently asked questions
At minimum: reliable ETL pipelines that keep data fresh, a governed data warehouse with clear ownership, BI dashboards your team actually uses, and self-service query access for analysts. Allerin builds all four layers with data quality checks, access controls, and documentation so the platform does not become another abandoned tool.
Two budgets, and honesty requires separating them: the build (fixed-scope, quoted after we map your sources and use cases) and the run (warehouse consumption, pipeline tooling, BI licenses). The run side is where surprises live, because consumption pricing scales with queries, not with value. We design against that: incremental models, scheduled refreshes instead of always-on compute, and cost alerts wired in from the first week, with the run budget handed to you as part of the proposal.
If your dashboards run fine off your application database and nobody is asking questions across systems, you do not need a warehouse yet, and we will tell you exactly that. The trigger points are real: report queries slowing production, metrics that need joins across your CRM, ERP, and application data, or the moment two departments show different numbers for the same KPI. A warehouse solves those; buying one before you have them is buying overhead.
A focused build covering one business domain (for example sales analytics or operational metrics) typically takes 3 to 6 weeks. Wider rollouts with more data sources and cross-domain reporting extend from there, scoped on data-source complexity and the number of stakeholder groups.
Yes. We build ETL and ELT pipelines connecting databases, SaaS APIs, flat files, streaming sources, and legacy systems into a unified data warehouse. Common sources include Salesforce, HubSpot, Stripe, PostgreSQL, MongoDB, S3, and internal APIs. Data is cleaned, deduplicated, and modeled before reaching your dashboards.
We implement automated data quality checks at every pipeline stage: schema validation on ingestion, null and duplicate detection during transformation, and freshness monitoring on the warehouse. Governance includes column-level access controls, data lineage tracking, and a business glossary so everyone agrees on what "active customer" means.