The same metric is calculated differently across reports and business units.
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From source to trusted metric
Data Platforms & Analytics
We design DWH, lakehouse and streaming environments, connect enterprise sources, govern data quality and lineage, and deliver BI models and management dashboards with consistent definitions.
Discuss a project ↗︎SOLUTION
Value
What your
business gets
Value
Consistent and auditable business metrics
Control
Faster reporting and analysis
Flexibility
Controlled data quality and lineage
Outcome
A foundation for predictive and AI use cases
When it fits
Situations where the service creates value
We start with the operating situation, constraints and the result that must change for the business — not with a technology list.
Management reporting depends on manual exports and spreadsheet consolidation.
The organization wants predictive analytics and AI but does not control data quality, lineage or availability.
Included in the solution
We assemble the required
configuration.
DWH, data lake and lakehouse
We design storage layers and data models for history, analytics, scale and agreed data-location requirements.
Integration, ETL/ELT and streaming
We build governed batch and streaming pipelines and observe loads, errors, latency and lineage for each dataset.
BI, reporting and analytical models
We create semantic models, dashboards and reports with consistent metric definitions, access controls and tested performance.
Data quality, catalog and governance
We establish owners, quality rules, cataloging, classification and remediation processes so data can be trusted and reused.
Delivery in detail
What we agree before work begins
We define data domains, metrics, sources, owners, refresh frequency, storage and processing architecture, BI tools, access models, and quality and lineage requirements.
Inputs include systems and interfaces, sample data and reports, metric definitions, volumes and growth, latency, access and retention requirements, and applicable regulatory constraints.
We reconcile control metrics with source systems and test completeness, quality, latency, permissions, lineage, recovery and critical analytical workflows.
After launch we monitor pipelines and data quality, access, resource cost, model and report versions, incidents and the release of new data products.
Process
Three steps
to launch.
Data map
We define sources, owners, metrics, quality requirements and refresh frequency.
Platform
We build storage and processing layers, integrations, access controls and observability.
Adoption
We release reports and data products, validate metrics and evolve the platform.
Solution: Data Platforms & Analytics