Warehouse or lakehouse, and which vendor?

This depends on your data volume, the mix of structured versus unstructured data, existing team skills, and cost sensitivity. We evaluate these against your specific situation during the architecture phase rather than defaulting to one vendor; the pipeline and modeling approach is largely portable across the major platforms.

How do you resolve conflicting metric definitions across teams?

We facilitate a working session with the relevant stakeholders to agree on a single canonical definition per key metric, document it in the semantic layer, and make that layer the required source for reporting tools going forward. Disagreements are usually more about lack of a forum to resolve them than genuine irreconcilable needs.

Can this platform support future AI/ML use cases?

Yes, a well-modeled, quality-checked data platform is exactly the foundation AI/ML initiatives need, since model quality is bounded by data quality. We can factor anticipated ML use cases into the architecture (e.g., feature store considerations) if they're on your roadmap.

What happens to our existing scripts and manual processes during migration?

We run new pipelines in parallel with existing processes and validate outputs match before deprecating the old scripts, rather than cutting over in one step. This avoids a period where nobody trusts either the old or new numbers.

Solution

Data Platforms & Analytics Foundations

Different teams report different numbers for the same metric, data pipelines break silently, and building a new report takes weeks of manual data wrangling instead of a query against a trusted source.

Approach

How we tackle it

We treat the data platform as production infrastructure, not a side project: pipelines are orchestrated, version-controlled, monitored for failures, and tested for data quality the same way application code is tested. A documented semantic layer sits between raw ingested data and reporting tools, so 'monthly active users' is defined once and consumed consistently everywhere, rather than recalculated slightly differently in every dashboard.

Current-state pain points

No single source of truth; the same metric is computed differently across teams
Data pipelines fail silently with no monitoring or alerting
Reporting requests take weeks because data has to be manually pulled and reconciled
No data quality checks, so bad data reaches dashboards and decisions
No clear data ownership or governance, especially around sensitive fields
Existing pipelines are undocumented scripts nobody wants to touch

What is in scope

Source system inventory and data flow mapping
Warehouse/lakehouse architecture design and platform selection
ELT/ETL pipeline design with orchestration and monitoring
Data modeling for a consistent, documented semantic layer
Data quality checks and observability for pipeline failures
Access controls and governance for sensitive data

Who it is for

Organizations where different teams get conflicting numbers for the same metric
Companies whose data pipelines are a collection of undocumented scripts and manual exports
Teams wanting to enable self-service analytics without duplicating data logic everywhere
Engineering leaders needing a data foundation to support upcoming AI/ML initiatives

Prerequisites

Access to source systems (application databases, third-party APIs, existing exports)
A stakeholder who can define canonical definitions for key business metrics
Agreement on data retention, sensitivity classification, and access requirements
A designated data or platform team to own the platform post-handover

Deliverables

What you end up owning

Artifacts land in your repositories and cloud accounts, with documentation to match.

Data source inventory and current-state flow diagram
Target warehouse/lakehouse architecture and platform recommendation
Orchestrated ELT/ETL pipelines with monitoring and failure alerting
A documented semantic/data model layer as the single source of truth for key metrics
Data quality test suite integrated into the pipeline
Access control and governance policy for sensitive data domains
Documentation and handover for the data platform and pipelines

Implementation

How the work is sequenced

Discovery & Mapping

Inventory data sources, map current flows, and identify where inconsistencies and manual processes exist.

Platform Architecture

Design the target warehouse/lakehouse architecture and select tooling matched to data volume and team skill set.

Pipeline Build

Build orchestrated, monitored ELT/ETL pipelines replacing manual exports and undocumented scripts.

Modeling & Quality

Build a documented semantic layer and integrate automated data quality checks into the pipeline.

Governance & Handover

Implement access controls for sensitive data and hand over documentation and ownership to the data or platform team.

Technologies

What we typically use

Warehouse/Lakehouse

SnowflakeBigQueryDatabricks / Delta Lake

Orchestration & Pipelines

AirflowdbtFivetran / Airbyte

Data Quality & Observability

Great Expectationsdbt testsMonte Carlo / custom alerting

Governance & Access

Role-based access controlColumn-level maskingData catalog tooling

Outcomes

What changes when this is done

Qualitative outcomes only. Any figures depend entirely on your estate, and we will not quote them before measuring.

A single, documented source of truth for key business metrics
Pipeline failures surfaced through monitoring instead of discovered via bad dashboards
Faster turnaround on new reporting requests through self-service against modeled data
Improved trust in data because quality checks run automatically
A governed access model for sensitive data domains

FAQ

Common questions

Related service

Artificial Intelligence

Talk to the engineers who would do the work

Bring your current architecture, constraints and the problem you are trying to solve. We will tell you what we would change first, what it depends on, and where we would start.

Book a Technical Consultation See how we work