Data & Analytics
See what is happeningas it happens.
A weekly report describes a business that no longer exists. We build the data infrastructure — pipelines, warehouse, dashboards — so the people responsible for outcomes see the current state and can act on it today.
What we provide
Real-time visibility into the metrics that drive your business, with data you can trust.
- Data warehouse design and build — Azure Synapse Analytics and Microsoft Fabric
- ETL and ELT pipeline development with Azure Data Factory and dbt
- Real-time streaming pipelines — Azure Event Hubs and Stream Analytics
- Power BI report and dashboard development
- Custom interactive dashboards — React, D3 and Recharts
- Data modelling, schema design and semantic layer definition
- Data quality monitoring, freshness alerts and reconciliation
- Data governance — lineage tracking, access control, audit logging
Why Theerrv
Data quality before dashboards
A fast dashboard on bad data is worse than no dashboard. We model and validate the data layer before building anything a decision-maker will see.
Azure-native data platform
Azure Synapse Analytics, Azure Data Factory, Event Hubs and Power BI are integrated by default — not patched together from separate vendors.
Built for the operator, not the analyst
We start with the decisions your operational team makes daily, then trace back to the data required. Dashboards are for the person making the call, not for the person who built the warehouse.
Our approach
Discovery → Architecture → Development → Testing → Deployment → Support
Data discovery
Identify all data sources — operational databases, flat files, APIs, third-party tools. Assess data quality, agree on the metrics that matter and the questions the dashboard needs to answer.
Data model design
Dimensional model or OBT depending on query patterns. Schema designed, lineage documented, transformation logic reviewed with the business.
Pipeline development
ETL/ELT pipelines built, scheduled and tested — including failure handling, idempotent loads and data quality assertions at each stage.
Dashboard development
Iterative build with operational stakeholders. Each sprint starts from the decision they are trying to make, not from the data available.
Data quality monitoring
Automated checks on data freshness, row counts, null rates and business rule violations — alerting to the right person when something goes wrong.
Operate and extend
Pipeline monitoring, SLA on data freshness, scheduled cost review for Synapse capacity, and a backlog for new metrics as the business changes.
Technologies
The stack we use to deliver data & analytics — chosen for longevity and maintainability, not trend.
- Azure Synapse Analytics
- Microsoft Fabric
- Azure Data Factory
- dbt
- Azure Event Hubs
- Azure Stream Analytics
- Power BI
- PostgreSQL
- Python (Pandas / PySpark)
- Delta Lake / Parquet
Industries & use cases
Retail and E-commerce
- Live sales, margin and inventory dashboards
- Customer cohort analysis and retention metrics
- Promotional performance and markdown analytics
Logistics and Distribution
- On-time delivery and SLA performance dashboards
- Fleet utilisation and cost-per-kilometre analytics
- Supplier and carrier performance scorecards
Financial Services
- P&L and cash-flow dashboards for leadership
- Branch and product performance comparison
- Regulatory reporting data pipelines
Manufacturing
- OEE (Overall Equipment Effectiveness) dashboards
- Defect rate and quality trend analysis
- Production vs target variance reporting
Common questions
Do we need a data warehouse, or can we just query our operational database?
For simple reporting on a single system with a small user base, querying the operational database directly is often fine. It becomes a problem when reports run slowly (blocking operational transactions), when data needs to come from more than one system, or when the business needs historical data the operational database does not retain. A data warehouse solves all three — but adds complexity, so we recommend it only when you are genuinely feeling the pain.
How often can dashboards be updated — can we have real-time data?
Most operational dashboards refresh every 1–15 minutes, which is effectively real-time for business purposes. Genuine sub-second streaming requires Azure Event Hubs and Stream Analytics and is appropriate for monitoring systems and financial trading — not for most operational dashboards. We recommend the refresh frequency that matches how often decisions are actually made, not the fastest possible cadence.
We use Power BI already. Can you build on top of what we have?
Yes. We often work with existing Power BI estates — auditing the existing models, improving data quality at the pipeline level, restructuring semantic models for performance, and adding new reports on top of a cleaned-up foundation. We are not attached to replacing what you have if what you have is worth keeping.
How do you ensure the data in the dashboard is accurate?
Data accuracy is a pipeline problem, not a dashboard problem. We add data quality assertions at every transformation stage — checking row counts match source systems, nullability rules hold, and business-logic validations pass. Failed assertions alert before the bad data reaches the dashboard. We also build a reconciliation report that compares warehouse figures to source systems so you can verify trust independently.
Related services
Start here
Tell us what theoperation is costing you.
A first conversation is a conversation, not a pitch. Describe how the work runs today and we will tell you plainly whether software is the right answer — and what it would take.
Or reach us directly at info@theerrv.com