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Data & Analytics

Data modernization with Microsoft Fabric

Data lakehouse, data integration and preparation, and BI dashboards to make decisions on trusted information.

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The challenge

Data lives in isolated systems, quality is uneven, and reports are assembled by hand. Without a trusted, well-governed data foundation, neither analytics nor AI can deliver value.

Our approach

We assess the maturity of your data management, design the target architecture and implement it in stages: design first, then a minimum viable product with ingestion, preparation pipelines and dashboards running in non-production and production environments.

What we do

  • Data lakehouse on Microsoft Fabric

    Target architecture design based on your data sources, and lakehouse implementation on Microsoft Fabric.

  • Data integration and preparation

    Ingestion and preparation pipelines that take data from your sources into an analysis-ready model.

  • Analytics and dashboards

    Analytics processes and Power BI dashboards with KPIs defined together with the business.

  • Data security

    A data security strategy defined as part of the architecture design, not bolted on afterwards.

What you get

  • A maturity diagnosis scored by dimension, with critical gaps identified.
  • A target architecture with the recommended technology stack and implementation sequence.
  • Components deployed and running, with an operations guide and a solution report.
  • KPI dashboards the business can use from day one.

Who it’s for

Organizations that need to consolidate scattered data, business teams that rely on manual reporting, and companies that want their data ready before investing in AI.

Technologies

  • Microsoft Fabric
  • Data Lakehouse
  • Power BI
  • Azure SQL Managed Instance
  • PostgreSQL

Starter packages

Focused offerings with defined duration, scope and deliverables.

Data lakehouse enablement — design

Duration: 5 days

The target architecture for your lakehouse and the roadmap to implement it.

Includes

  • Requirements and needs discovery session.
  • Solution design based on your data sources (target architecture).
  • Data security strategy.
  • Prioritization and implementation sequence.

Deliverables

  • Document with the implementation roadmap and recommended technology stack.
  • Implementation recommendations.

Out of scope

  • Building data sources.
  • Data cleansing.
  • Changes to existing systems to integrate with the solution.
  • Network or hardware configuration for data connectivity.
Assumptions · Prerequisites

Assumptions

  • You have access to the data sources involved.
  • The technology stack is defined or approved.
  • You have a technical point of contact (optional).

Prerequisites

  • Data Management assessment.

Data lakehouse enablement + MVP

Duration: 10 days

Your lakehouse deployed and running, with a first end-to-end data flow.

Includes

  • Deployment of the approved architecture components in two environments (non-production and production).
  • Initial data lakehouse configuration.
  • Up to 1 data ingestion pipeline and 1 preparation process.
  • Up to 2 analytics/BI processes.
  • Up to 2 single-level dashboards, with up to 4 KPIs and 4 visualizations.

Deliverables

  • Components deployed and running.
  • Solution operations guide.
  • Deployed solution report.

Out of scope

  • Building data sources.
  • Data cleansing.
  • Changes to existing systems to integrate with the solution.
  • Network or hardware configuration for data connectivity.
Assumptions · Prerequisites

Assumptions

  • You have access to the data sources involved.
  • The technology stack is defined or approved.
  • You have a technical point of contact (optional).

Prerequisites

  • Completed data lakehouse design.

Data consumption enablement — design

Duration: 3 days

The solution design to put your data to work through analytics and dashboards.

Includes

  • Requirements and needs discovery session.
  • Solution design based on your data sources.
  • Prioritization and implementation sequence.

Deliverables

  • Document with the recommended solution and implementation recommendations.

Out of scope

  • Building data sources.
  • Data cleansing.
  • Changes to existing systems to integrate with the solution.
  • Network or hardware configuration for data connectivity.
Assumptions · Prerequisites

Assumptions

  • You have access to the data sources involved.
  • The technology stack is defined or approved.
  • You have a technical point of contact (optional).

Prerequisites

  • Data Management assessment.

Data consumption + MVP (Small)

Duration: 10 days

A first analytics product in production, with a two-level dashboard.

Includes

  • Deployment of the required components in two environments (non-production and production).
  • Up to 2 data ingestion pipelines and 2 preparation processes.
  • Up to 3 analytics/BI processes.
  • 1 two-level dashboard, with up to 6 KPIs and 8 visualizations.

Deliverables

  • Components deployed and running.
  • Solution operations guide.
  • Deployed solution report.

Out of scope

  • Building data sources.
  • Data cleansing processes.
  • Changes to existing systems to integrate with the solution.
  • Network or hardware configuration for data connectivity.
Assumptions · Prerequisites

Assumptions

  • You have access to the data sources involved.
  • The technology stack is defined or approved.
  • You have a technical point of contact (optional).

Prerequisites

  • Completed data consumption design or existing components.

Data consumption + MVP (Large)

Duration: 30 days

A broader analytics product, with more sources, processes and dashboards.

Includes

  • Deployment of the required components in two environments (non-production and production).
  • Up to 5 data ingestion pipelines and 5 preparation processes.
  • Up to 4 analytics/BI processes.
  • Up to 2 two-level dashboards, each with up to 6 KPIs and 8 visualizations.

Deliverables

  • Components deployed and running.
  • Solution operations guide.
  • Deployed solution report.

Out of scope

  • Building data sources.
  • Data cleansing processes.
  • Changes to existing systems to integrate with the solution.
  • Network or hardware configuration for data connectivity.
Assumptions · Prerequisites

Assumptions

  • You have access to the data sources involved.
  • The technology stack is defined or approved.
  • You have a technical point of contact (optional).

Prerequisites

  • Completed data consumption design or existing components.

Want to know the right first step for your case?

In a no-cost conversation, we’ll review your context and recommend the right assessment or package.

Book a consultation

Recommended assessment to get started

Data management

Measures the maturity of how you manage and use data, identifies critical gaps and delivers an actionable roadmap.

  • 1 day
  • 3 days
  • 5 to 10 days

Frequently asked questions

Do I need an assessment before buying a data package?

Design packages build on the Data Management assessment, and MVP packages require a completed design or existing components. That way, every stage builds on a decision that has already been validated.

What does a data MVP include?

Components deployed in two environments (non-production and production), ingestion and preparation pipelines, analytics/BI processes and KPI dashboards, plus an operations guide and a solution report.

Is data cleansing included?

No. Data cleansing, building new data sources and network or hardware changes are out of scope and can be agreed separately.

What do you need from our side?

Access to the data sources involved, a defined or approved technology stack and, optionally, a technical point of contact.

First step

Let’s talk about your next move in cloud and AI

The first conversation is free. We’ll understand what you need, review your context and recommend the shortest path to a tangible result.

Book a consultation