Data & analytics
Most analytics problems are engineering problems
When an organization cannot answer a question about itself, the cause is rarely the reporting tool. It is that the same entity is defined differently in four systems, that a critical field is populated by hand, that nobody owns the source of truth, or that the only person who understood the extract has moved on.
We work on that layer. Architecture, integration, pipelines, quality and governance first; dashboards and models after, on a foundation that will support them. It is less immediately satisfying than a new visualization, and it is the reason the visualization keeps being right six months later.
This is also where AI initiatives most often succeed or fail. A model applied to inconsistent data produces confident, inconsistent answers.
Signs the foundation needs work
- Two departments report different numbers for the same measure
- A monthly report depends on one person’s spreadsheet
- Nobody can say who owns a given data set
- Integrations break quietly and are noticed days later
- An AI proposal keeps stalling on “where would the data come from?”
Capabilities
What we deliver
Data strategy and architecture
Target architecture, platform selection, domain and ownership models, and a migration path that delivers value in stages rather than in one high-risk cutover.
Integration and pipelines
ETL and ELT pipelines built to be observable and recoverable, with clear failure handling — because a pipeline that fails silently is worse than no pipeline.
Migration and modernization
Moving data off ageing platforms with validation at each step, reconciliation against the source, and a rollback position maintained throughout.
Data quality and master data
Profiling, cleansing, matching and stewardship, so that “customer”, “resident”, “student” or “case” means one thing across the organization.
Data governance
Ownership, definitions, lineage, retention and access policy — implemented in the platform rather than described in a document nobody opens.
Warehouses, lakes and cloud platforms
Designing and building modern data platforms sized to the organization, with cost and performance considered as first-order design constraints.
Business intelligence and reporting
Dashboards and reporting built around the decisions people actually make, with definitions documented so figures can be trusted and defended.
Predictive and real-time analytics
Forecasting, prioritization and streaming analytics where the timing of an answer changes what can be done about it.
Data preparation for AI
Structuring, securing and pipelining data so that AI initiatives rest on something reliable, with sensitive information handled deliberately.
Platforms we work with
We work across relational, document and cloud-native data platforms, and select according to workload, existing investment and the skills of the team who will run it. Naming a platform describes our working familiarity rather than a formal vendor relationship.
Databases
- Oracle
- Microsoft SQL Server
- PostgreSQL
- MySQL
- MongoDB
Cloud data and analytics platforms
- Snowflake
- Databricks
- Azure data services
- AWS data services
- Google Cloud data services
Business intelligence
- Power BI
- Tableau
Secure data access
Opening data up and protecting it are usually presented as opposing goals. They are not, provided access is designed rather than inherited. We implement role and attribute-based access, row and column-level controls, masking for sensitive fields, and auditable access logging — so that more people can safely use data, not fewer.