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HyperzenHyperzen

From raw data to decisions

One set of numbers everyone trusts

Pipelines, modelling and dashboards built so definitions are shared, tested and explainable.

The problem

Three answers to the same question

When each team exports its own data, every meeting starts by reconciling figures. Definitions drift, reports are rebuilt by hand each month, and the numbers arrive too late to change anything.

Our approach

Modelled once, defined in the open

Data is ingested reliably, modelled in a warehouse with tested definitions, and served through dashboards that state their assumptions. Metrics have owners and documentation, so agreement replaces reconciliation.

Capabilities

What the engagement includes

  • Ingestion

    Scheduled and streaming pipelines with retries and monitoring.

  • Warehouse modelling

    Layered models with tested, version-controlled definitions.

  • Quality tests

    Freshness, uniqueness and referential checks that fail loudly.

  • Dashboards

    Decision-oriented views rather than walls of charts.

  • Metric catalogue

    Every metric documented, owned and traceable to source.

  • Forecasting

    Statistical baselines and anomaly detection where they add value.

Also covered

  • Warehouse design on Postgres, BigQuery or Snowflake
  • ETL and ELT pipeline engineering
  • Executive and operational dashboards
  • Customer and product analytics
  • Data quality monitoring
  • Reporting automation

Process

How delivery runs

Each phase ends with something you can see, use and judge.

  1. 01

    Inventory

    Sources, definitions in use today and the decisions that need support.

  2. 02

    Model

    Warehouse layers, naming and tests agreed with the business.

  3. 03

    Deliver

    Dashboards built with the teams who will rely on them.

  4. 04

    Operate

    Monitoring, documentation and enablement for self-service.

Technology

What we build with

  • Postgres
  • BigQuery
  • Snowflake
  • dbt
  • Airflow
  • Python
  • Metabase
  • Superset
  • Looker Studio

Outcomes

What you get

  • Meetings that start from agreed numbers
  • Manual reporting effort largely removed
  • Data issues caught before they reach a dashboard
  • Analysts freed for analysis rather than plumbing

Use cases

Where this applies

  • 01

    Executive reporting

    One reliable view of performance across the business.

  • 02

    Product analytics

    Understand activation, retention and feature adoption.

  • 03

    Operational monitoring

    Track throughput and exceptions as they happen.

FAQ

Data Intelligence questions

If reporting spans more than a couple of systems, or history matters, yes. For a single application, well-designed reporting queries are often enough — and we will say so.

Related

Other services

//Start a conversation

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Tell us where you are today and what you need to be true in six months. We will map the route.

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