Data & Analytics
A clearer picture starts with connected data.
For organizations with several core systems, reporting that nobody fully trusts, or a data environment that has aged past what the business now needs.
The problem
The data exists. Agreeing on it is the hard part.
A few patterns show up again and again: month-end reporting assembled by hand, spreadsheets that have quietly become the system of record, a warehouse built years ago for questions nobody asks anymore, and one person who knows how the numbers really get produced.
None of that is a tooling failure. It is what happens when systems grow independently. The work is connecting them, deciding what the shared definitions are, and putting the result somewhere durable with proper access controls.
Revenue
Labor hours
Fleet cost
Capabilities
What the work includes
Scope varies by engagement. These are the areas we cover.
01
Platform and architecture
- Enterprise data warehouse design
- AWS cloud infrastructure for data workloads
- Snowflake warehouse architecture
- Migration planning and controlled retirement of legacy environments
02
Getting data in
- Ingestion from databases, files, and exports
- API integration with business systems
- Scheduled and incremental loads
- Handling of source changes and failures
03
Making data usable
- Cleaning and standardization
- Data modeling with agreed definitions
- Reporting foundations and dashboards, scoped per engagement
- Documentation of what each field means
04
Control and trust
- Access controls by role and business unit
- Governance practices for sensitive data
- Validation checks on loads and models
- Clear ownership of data definitions
05
Running it over time
- Monitoring of pipelines and platform health
- Query and workload performance review
- Cloud cost management
- Ongoing platform administration and support
06
Working with your team
- Reviews with the people who use the reports
- Handover documentation
- Support for internal analysts
- Phased delivery so value arrives before the whole platform does
Case study · Transportation & Logistics
13 data sources. One place to work with the information.
A transportation and logistics business operating across 51 locations kept finance, workforce, fleet, marketing, and operations information in 13 separate sources. Answering a question that crossed departments meant logging into each platform and exporting data by hand.
Moving that data into one place was only part of the work. Each source had to be mapped, structured so it lines up with the others, and validated as it loads, so the same customer, location, or period means the same thing everywhere.
The result is a shared warehouse, built on AWS and Snowflake, that gives the business a single foundation for reporting. Business dashboards built on that foundation are a planned next phase, not part of the delivered work.
Business systems
Delivered13 sources across finance, workforce, fleet, marketing, and operations
Data preparation and structure
DeliveredMapping, transformation, and validation
Shared warehouse
DeliveredAWS integrations loading into Snowflake
Reporting access
Planned next phaseBusiness dashboards built on the shared data
Questions
Common questions about data work
Do we need to replace our existing systems first?
No. The usual approach is to leave source systems in place and read from them. Replacing a system is a separate decision, and often easier to make once the data is visible in one place.
What does 'modeled data' actually mean for our reports?
It means a customer, an order, or a shift has one agreed definition that every report uses. Without that, two accurate reports can still disagree, and people stop trusting both.
Will reporting be real time?
That depends on the source systems and the engagement. Many useful platforms load on a schedule. We will tell you what your systems can realistically support rather than promise instant data.
Who runs the platform after it is built?
Either your team, ours, or both. Ongoing administration, monitoring, and cost management can be part of the engagement, and handover documentation is written either way.
How long does this take?
It depends on the number of sources, the state of the data, and how much modeling is needed. We scope it with you and work in phases rather than quoting a fixed period up front.
Tell us where the reporting breaks down.
Bring the report that takes three days to produce. That is usually the fastest way into a useful conversation.
Get in touch