DATA & CLOUD / SYSTEM NODEData Analysis & BI

The right numbers, in front of the right people.

We build the pipelines, models, and dashboards that give your teams reliable self-serve access to the metrics that drive their decisions.

Delivery evidence
Ownership, deployment boundary, acceptance criteria, and handover are defined per engagement.
SourcesDB / APIs / filesIngestELT pipelineStageRaw warehouseTransformdbt modelsData MartBusiness metricsSemantic LayerLooker / CubeDashboardSelf-serve BIDistributeAlerts & reports
DATA PIPELINE
WHAT WE BUILD

Data Analysis & BI, engineered as an operating system.

Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.

01

Dashboard Development

Custom dashboards in Tableau, Looker, Power BI, or custom-built React — designed around the specific decisions they need to support.

  • Decision-first design
  • Mobile-friendly
  • Drill-through capability
02

dbt Data Modelling

dbt models transforming raw data into reliable, documented, tested metrics — the single source of truth every downstream system depends on.

  • Staging → mart layers
  • Data tests at each layer
  • Auto-documentation
03

Self-Serve Analytics

Semantic layers letting non-technical users answer their own questions without opening engineering tickets.

  • LookML / Cube.js
  • Governed metric definitions
  • Row-level security
04

Reporting Automation

Scheduled report generation and distribution replacing manual export workflows — delivered to email, Slack, or any destination.

  • Scheduled delivery
  • Threshold alerts
  • Distribution management
05

KPI Framework Design

Working with your team to define, agree, and implement the metrics that matter — with a single documented definition for each.

  • Metric glossary
  • Single source of truth
  • Stakeholder sign-off
DELIVERY SEQUENCE

From unknowns to production evidence.

01

Requirements

Map the decisions each dashboard needs to support and the data sources available to answer them — before touching any data.

Business Needs Requirements Doc
02

Data Audit

Assess data quality, freshness, and completeness. Surface issues now — not after building dashboards on bad data.

Source Systems Audit Report
03

dbt Modelling

Build staging, intermediate, and mart models with tests at each layer. Every metric defined once, documented, tested.

Raw Warehouse Trusted Models
04

Dashboard Build

Iterative delivery of dashboard views — reviewed with end users at each milestone, not just at the end.

Trusted Models Dashboard
05

Semantic Layer

Self-serve analytics layer so non-technical users can explore data without engineering support.

Dashboard Self-serve
06

Alerts & Reports

Scheduled reports and threshold-based alerts replacing manual export-and-send workflows.

Self-serve Distributed BI
TECHNOLOGY SURFACE

Tools selected for the system, not the trend.

Technology follows environment, operating constraints, team ownership, and long-term maintainability.

01Transformation

dbt Core · dbt Cloud · SQL · Python

02Warehouses

BigQuery · Snowflake · Redshift · DuckDB

03BI Tools

Looker · Tableau · Power BI · Metabase

04Orchestration

Airflow · dbt Cloud · Prefect

WHERE IT FITS

The same capability changes by domain.

01

Retail

Sales performance, inventory analytics, customer segmentation, promotion effectiveness.

02

Finance

P&L reporting, risk dashboards, regulatory reporting, cost centre analytics.

03

Healthcare

Clinical performance metrics, operational dashboards, population health analytics.

04

Manufacturing

OEE dashboards, quality metrics, supply chain analytics, cost tracking.

TECHNICAL FAQ

Questions worth resolving before delivery.

01Our data is messy — can you still build dashboards?+

Yes, but we do not hide the problem. We surface data quality issues and build the dbt tests and transformations to address them. Dashboards on bad data produce bad decisions.

02What BI tools do you work with?+

Looker, Tableau, Power BI, and Metabase most frequently. For custom requirements we build on React with charting libraries. The right tool depends on your existing stack and user base.

03How do you ensure metric consistency across dashboards?+

By building a semantic layer where metrics are defined once and referenced everywhere. When the definition changes, it changes in one place.

04Do you help define what metrics we should track?+

Yes. We run a metric definition workshop to agree on what matters, how each metric is calculated, and who owns it — before building anything.

OSYSTIC / PROJECT CHANNELOPEN
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Tell us what decisions your team needs to make faster. We will come back with an honest data and BI plan.

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