Data analytics that decision-makers can trust

Pipelines, models, and dashboards grounded in clear definitions — so reports stop arguing with each other.

  • Conflicting metrics across teams because source definitions differ
  • Fragile ETL jobs that fail silently until someone notices late
  • Dashboards built without ownership of the underlying tables
Talk about your data stack
Analytics charts and data visualisation on a workstation

What we deliver

Enterprises rarely lack data. They lack agreed definitions, reliable pipelines, and reporting that survives a finance review. We fix the foundations before adding more dashboards.

Data platform & pipelines
Ingest, transform, and store data with lineage and failure visibility.
  • Source-to-warehouse pipeline design
  • Scheduled transforms with monitoring
  • Basic data quality checks on critical fields
Business intelligence
Dashboards and semantic models tied to definitions your stakeholders agree on.
  • Metric definitions documented with owners
  • Power BI or Tableau reports for priority use cases
  • Access patterns aligned to roles
Analytics engineering
Clean, tested transformation layers so analysts stop rebuilding the same joins.
  • Dimensional or wide-table models as appropriate
  • Version-controlled transform code
  • Handover notes for your data team
Operational reporting
Near-real-time views where latency actually matters to the business process.
  • Event or CDC-based feeds where justified
  • Operational dashboards with clear refresh SLAs
  • Alerting on pipeline health

How we work

  1. 1

    Define

    Agree the questions that matter and the metric definitions that answer them.

  2. 2

    Model

    Design sources, transforms, and storage for those questions — nothing more.

  3. 3

    Build

    Implement pipelines and reports with tests on the fields that drive decisions.

  4. 4

    Enable

    Train owners, document refresh and escalation paths, then step back.

Technologies we work with

Stacks and platforms we use on this kind of work — not a partnership claim.

Languages

  • PythonPython
  • RR
  • SQL

Pipelines

  • SparkSpark
  • KafkaKafka

Storage

  • PostgreSQLPostgreSQL

Business intelligence

  • Power BIPower BI
  • TableauTableau
Diagram of a data flow from sources through pipelines into a warehouse and BI layer

Common questions

Do you start with a full data lake project?

Usually no. We start with the highest-value reporting or operational use cases and expand the platform only when those paths are solid.

Can you work with SAP as a source system?

Yes. Extracting and modelling SAP data for analytics is a common request alongside our SAP services work.

Who owns the dashboards after delivery?

Your named business and data owners. We document refresh schedules, metric definitions, and how to request changes.

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