Data & Analytics
Consolidate fragmented data sources into governed pipelines, surface actionable metrics through real-time dashboards and deploy predictive models that inform operational decisions.
From scattered spreadsheets to a single source of truth
Most operational businesses accumulate data across payment processors, CRM systems, infrastructure logs, partner portals and finance tools — but no unified view. Decision-makers wait days for reports that should be live, and metric definitions drift between departments.
We design and build the data infrastructure that connects these sources: ingestion pipelines with schema validation, a central warehouse with documented transformation logic, and a semantic layer that ensures every team uses the same definitions for revenue, churn, authorization rate or partner performance.
On top of this foundation we deliver dashboards tailored to each audience — executive summaries, operational monitoring, finance reconciliation views — plus anomaly detection and forecasting models where historical data supports them.
Result: a governed data platform that answers questions in seconds rather than days, with metric definitions your entire organization agrees on.
Metrics shown represent a typical mid-market deployment. Source count and refresh cadence vary by integration scope.
What is included
Pipeline engineering
ELT pipelines with schema validation, incremental loading, deduplication and automated backfill — connecting your payment, CRM, infrastructure and finance data.
Dashboards and reporting
Role-specific dashboards for executives, operations and finance — with scheduled exports, drill-down capability and natural-language query support.
Predictive models and anomaly detection
Time-series forecasting for revenue and cash flow, anomaly detection for transaction patterns and infrastructure metrics, with configurable alert thresholds.
Metric governance
A semantic layer with documented metric definitions, ownership assignments and version history — so revenue means the same thing to finance, ops and the board.
Data quality assurance
Automated completeness, accuracy and consistency checks on every pipeline run — with quality scores surfaced on dashboards and alerting on degradation.
Self-serve reporting enablement
Team training sessions, saved-query libraries and role-based dashboard access so operations, finance and leadership can answer their own questions without engineering tickets.
How it works
Data source audit and metric workshop
Week 1–2. We inventory every data source, interview stakeholders on their reporting needs and document current metric definitions (and disagreements). Deliverable: data source catalog, metric dictionary draft and priority dashboard requirements.
Warehouse and pipeline build
Week 2–5. Design the warehouse schema, build ingestion pipelines for each source, implement transformation logic and configure incremental loading. Deliverable: production warehouse with all priority sources flowing and validated.
Dashboard development
Week 4–6. Build role-specific dashboards, configure scheduled reports and set up alerting for anomalies and threshold breaches. Deliverable: live dashboards accessible to all stakeholders with documentation.
Model deployment and validation
Week 6–8. Train and validate predictive models on historical data, deploy to production with monitoring and establish accuracy baselines. Deliverable: model performance report with confidence intervals and drift monitoring.
Handover and ongoing maintenance
Week 8+. Knowledge transfer sessions, documentation handover and optional managed maintenance covering pipeline monitoring, model retraining and new source onboarding.
Technical details
| Parameter | Specification |
|---|---|
| Warehouse platforms | BigQuery, Snowflake, Redshift, PostgreSQL with TimescaleDB — we deploy on your existing platform |
| Orchestration | dbt, Airflow, Dagster, Prefect — workflow tool matched to your team preference |
| BI and dashboards | Looker, Metabase, Grafana, Power BI — with embedded iframe option for internal portals |
| Data source connectors | REST APIs, databases, S3/GCS file drops, webhooks, CSV upload, SaaS integrations (Stripe, Salesforce, HubSpot) |
| Refresh cadence | Configurable: real-time streaming, 5-minute micro-batch, hourly or daily — per source |
| Data quality checks | Great Expectations or dbt tests on every pipeline run; quality score surfaced in dashboard |
| Model types | Prophet, ARIMA, gradient boosting, isolation forest for anomaly detection |
FAQ
Do we need to migrate our existing data warehouse?
No. We build on your existing platform — whether that is BigQuery, Snowflake, Redshift or a well-structured PostgreSQL instance. Migration is only recommended when your current platform cannot meet performance or cost requirements, and we present the business case before recommending a move.
Who owns the data models and pipeline code?
You do. All pipeline code, dbt models, dashboard definitions and documentation live in your repositories. There is no proprietary lock-in. If you transition to internal management, your team inherits fully documented, production-tested code.
How quickly can we add a new data source?
For sources with standard connectors (Stripe, Salesforce, Google Analytics), typically 2–3 business days including validation. Custom API sources take 1–2 weeks depending on documentation quality and authentication complexity.
Your data is already there. Let us connect it.
Share your current reporting pain points — we will propose a data architecture that eliminates them.