Fragmented data sources
Reporting pulls from disconnected systems with no shared data model.
Data platforms
A dashboard no one trusts is worse than no dashboard.
The hard part of analytics isn't the chart — it's getting clean, reconciled, trustworthy data behind it. We build the pipelines and the warehouse, define ownership, and govern access so the numbers are consistent and defensible. Once that foundation is solid, reporting and AI on top actually work.
Discuss Data & Analytics
Architecture
flowchart LR
SRC[Source Systems] --> INGD[Ingestion]
INGD --> LAKE[OneLake / Lakehouse]
LAKE --> TRANS[Transform and Model]
TRANS --> WH[Warehouse]
WH --> BI[Power BI]
WH --> AIRAG[RAG / AI]
GOV[Governance and Access] -. controls .-> LAKE
Business challenges
Data & Analytics challenges we solve.
Every recommendation starts with business pressure, technical risk and the operating model required after launch.
Unreliable pipelines
Data pipelines fail silently or produce inconsistent results.
Unclear data ownership
No one is accountable for the accuracy or governance of a given dataset.
Solution overview
Data & Analytics designed for production readiness.
We build the pipelines and the warehouse, define ownership, and govern access so the numbers are consistent and defensible. Once that foundation is solid, reporting and AI on top actually work.
Data architecture
A data platform designed around your real sources and use cases, not a generic template.
Pipeline design
Clean, reconciled pipelines instead of fragmented, hand-maintained exports.
Analytics models
A shared semantic model so numbers agree across reports.
Governance foundations
Ownership and access control that make the data trustworthy enough for AI.
Architecture model
A practical delivery architecture before implementation begins.
We define the target operating model, controls, integration points and ownership path before building, so the solution can be supported after launch.
CloudevTech
Enterprise delivery model
01
Discover
02
Architect
03
Implement
04
Validate
05
Operate
Our approach
Structured delivery from discovery to operational handover.
Every engagement is shaped around the service goal, current constraints and the operating model your team needs after launch.
Assess current data sources
Map where data lives and how it currently flows into reporting.
Design the data architecture
Define pipelines, storage and access model.
Build the pipelines
Implement pipelines and connect data sources into the governed platform.
Monitor and govern
Add monitoring and ownership so pipelines stay reliable.
Business benefits
Outcomes designed for decision makers and delivery teams.
Benefits are framed around measurable improvement, operating confidence and reduced delivery risk.
Connected data architecture
Data sources feed one governed platform instead of disconnected reports.
Reliable pipelines
Pipeline design includes monitoring so failures are caught, not discovered downstream.
Clear data ownership
Governance foundations assign ownership and accountability per dataset.
Technology stack
Implemented with proven platforms and tools.
Technology choices are confirmed during discovery, with a preference for reliable, maintainable platforms your team can support.
Microsoft Fabric
Power BI
BigQuery
Databricks
Data Factory
FAQ
Common questions before engagement.
Short answers to common planning questions for Data & Analytics.
Why don't our current numbers reconcile?
Usually fragmented sources and no single owner. Fixing the pipeline fixes the trust.
Do we need a warehouse or a lakehouse?
Depends on your data and use cases — Fabric, BigQuery and others each fit different shapes. We'll advise.
Enterprise consultation
Book a consultation