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Service 05 — Data Warehousing

One place for the numbers that run the business.

We design data warehouses, lakehouses and semantic layers that turn operational data into a single, trusted foundation for reporting and analytics.
Discuss your projectSee the architecture
A team meeting around a conference table with laptops open in a bright modern office

The business problem

Dashboards are easy. Trustworthy numbers are not.

Many organizations connect BI tools directly to operational systems or spreadsheets. It works at first, then slows down, breaks on schema changes and produces metrics that different teams calculate differently.

A well-modelled warehouse separates analytics from operations, defines metrics once and makes historical analysis possible — so that decisions rest on consistent information.

Signals this might be you

  • 01Dashboards are slow or time out on large queries
  • 02Key metrics are defined differently by each department
  • 03Historical trends are lost when source data changes
  • 04Analysts spend most of their time preparing data

What we provide

What LT Lab provides

Warehouse and analytics infrastructure sized for your organization today, with a clear path to grow.

  1. 01

    Warehouse architecture

    Platform selection and design — warehouse, lakehouse or PostgreSQL-based — based on volume, cost and skills.

  2. 02

    Dimensional modelling

    Facts, dimensions and data marts that reflect how the business actually measures performance.

  3. 03

    Semantic layer

    Shared metric definitions so revenue, margin or utilization mean the same thing in every report.

  4. 04

    BI enablement

    Connecting Power BI, Tableau, Looker or Metabase with governed datasets, access controls and documentation.

Capabilities

What this service covers

  • Data warehouses
  • Data marts
  • Dimensional modelling
  • Lakehouse architecture
  • Reporting infrastructure
  • Analytics engineering
  • Semantic layers
  • BI integrations

Typical architecture

Layered warehouse design

Raw, cleaned and business-ready layers keep data traceable while giving analysts simple, fast tables.

  1. 01

    Raw

    Data as it arrived

    • Source replicas
    • API extracts
    • Files
  2. 02

    Staging

    Cleaned and standardized

    • Type casting
    • Deduplication
    • Naming
  3. 03

    Core model

    Business entities and history

    • Facts
    • Dimensions
    • Slowly changing history
  4. 04

    Marts and semantics

    Ready for decisions

    • Finance mart
    • Sales mart
    • Metric definitions
  5. 05

    Consumption

    Tools people use

    • Power BI
    • Tableau
    • Looker
    • Metabase
    • AI
Data flows through Raw, then Staging, then Core model, then Marts and semantics, then Consumption.

Example use cases

Where this creates value

01

Executive reporting platform

Board and management dashboards built on a governed warehouse rather than monthly spreadsheet packs.

Outcome: Consistent KPIs across the leadership team

02

Multi-entity consolidation

Financial and operational data from subsidiaries or country offices modelled into a single consolidated view.

Outcome: Group-level visibility without manual consolidation

03

Self-service analytics

Curated datasets and a semantic layer that let business users build reports without writing SQL.

Outcome: Less dependence on a small analytics team

04

Donor and programme reporting

Programme, finance and monitoring data modelled for recurring reporting obligations.

Outcome: Faster, more reliable reporting cycles

Technologies

Chosen for your environment, not our preferences.

Warehouses
PostgreSQL / BigQuery / Snowflake / Azure Synapse / Redshift
Modelling
dbt / SQL / Semantic layers
BI
Power BI / Tableau / Looker / Metabase / Zoho Analytics
Cloud
AWS / Azure / Google Cloud

Engagement process

How an engagement runs.

  1. 01 —

    Understand

    We map the workflows, systems, data and business objectives.

    • + Stakeholder workshops
    • + Process and system mapping
    • + Data assessment

    Problem definition and priorities

  2. 02 —

    Architect

    We design a technical approach around the organization's existing environment and future requirements.

    • + Solution architecture
    • + Technology selection
    • + Delivery plan

    Architecture and roadmap

  3. 03 —

    Build

    Our engineers develop, integrate and test the solution iteratively.

    • + Iterative sprints
    • + Integration and testing
    • + Regular demos

    Working, tested software

  4. 04 —

    Improve

    We deploy, monitor and continue optimizing the system as the organization grows.

    • + Deployment
    • + Monitoring and support
    • + Continuous improvement

    A system that keeps getting better

Related services

Start a conversation

Let's talk about Data Warehousing.

Tell us about the problem, the systems involved and where you want to be. We'll help you work out the right first step.

Tell us about your projectconsulting@ltlabsolutions.io