What is a Data Warehouse?
Find out what a data warehouse is, the advantages it offers, the best design practices to adopt and the essential tools to integrate into its development.
What is a Data Warehouse?
A data warehouse is a centralised system that brings together data from all the company's sources (finance, sales, marketing, operations, CRM, etc.). It organises this information in a structured way to facilitate analysis, report generation and decision-making.
In other words: it's the organisation's "single source of truth", where data is clean, consistent and ready to be analysed.
Difference between a transactional database and a data warehouse
A transactional database (such as accounting software or ERP) is used on a daily basis: sales, invoicing, payroll, orders, inventories. It is optimised to record operations quickly.
A data warehouse is designed to :
Analyse trends;
Produce advanced reports;
Compare results from one period to another;
Consolidate data from multiple systems.
In summary:
The transactional base = act.
The data warehouse = analyse.
How does a data warehouse work?
A data warehouse is based on a structured process for collecting, transforming, organising and analysing information from all the company's systems.
This multi-stage process ensures that the data is reliable, consistent and ready to be used for decision-making.
Data collection and integration (ETL / ELT)
The first step is to collect data from different sources: accounting software, CRM, production systems, marketing, Excel files, etc.
This is achieved using ETL (Extract - Transform - Load) or ELT (Extract - Load - Transform) processes.
These processes enable :
Extracting data;
Eliminating errors and duplicates;
Harmonising formats;
Loading information into the warehouse.
This ensures that all our teams are working with consistent, high-quality information.
Data modelling
Once the data has been integrated, the warehouse organises it according to a logical structure suited to the analysis, often based on :
A star model (the most common);
A snowflake model;
Fact tables (sales, transactions, operations);
And dimension tables (customers, products, dates, locations).
This data modelling facilitates rapid analysis, cross-referencing and the creation of performance indicators (KPIs).
Structured storage
The data warehouse stores information in an architecture optimised for analytical queries.
Unlike operational systems designed to record transactions on a day-to-day basis, the warehouse enables :
Complex analyses;
Massive groupings;
Comparisons between periods;
Financial or operational performance calculations.
Structured storage guarantees fast results, even with large amounts of data.
Operation: BI, reports, advanced analyses
Once the data is ready, it can be used by Business Intelligence tools such as Power BI, Tableau or Looker.
These tools enable :
The creation of automated dashboards;
Real-time visualisation of indicators;
Trend analysis;
Advanced forecasting and modelling.
This is the final stage, where the data is transformed into strategic decisions for the company.
Types of data warehouse
There are several types of data warehouse, each adapted to different technological, budgetary and operational needs.
On-premise data warehouse
An on-site data warehouse is installed directly into the company's IT infrastructure. It offers total control over data, servers and security.
Benefits:
Ideal where strict confidentiality or sovereignty rules apply;
Allows full management of infrastructure and access;
Suitable for organisations that already have a strong in-house IT team.
Limits:
High costs (servers, maintenance, hardware);
Limited scalability;
Slower deployments.
This type of solution is still used in certain sensitive or highly regulated sectors.
Cloud data warehouse (Azure, AWS, GCP)
More and more businesses are opting for a data warehouse in the cloud, offered by platforms such as Azure Synapse, AWS Redshift or Google BigQuery.
Benefits:
Virtually infinite flexibility and scalability;
Costs based on actual usage;
Easy integration with modern tools (Power BI, AI, automation);
High performance and processing speed.
Limits:
Dependence on a cloud provider;
Need for good governance to manage access and compliance (e.g. Act 25).
For SMEs, this is often the most efficient and cost-effective solution.
Hybrid data warehouse
The hybrid approach combines the best of both worlds: some data remains on site, while others are stored in the cloud.
Advantages :
Ideal for companies undergoing digital transformation;
Allows sensitive data to be held locally;
Offers the analytical power of the cloud for complex requirements.
This approach is particularly relevant for organisations whose operational systems remain local (ERP, in-house software).
Sector warehouses (health, finance, manufacturing)
Some data warehouses are designed specifically for an industry sector, with pre-configured models:
Health: clinical data, patient records, quality indicators, strict compliance.
Finance:portfolio management, risk, regulatory compliance, audits.
Manufacturing: production, inventories, costs, logistics, equipment performance.
These sector-specific solutions enable faster deployment thanks to ready-to-use structures aligned with the needs of the sector.
How do you set up a data warehouse?
Setting up a data warehouse is a structuring project that supports decision-making, improves the quality of analyses and reduces dependency on Excel files.
To be successful, implementation must follow a methodical approach, tailored to the business needs and digital maturity of the organisation.
1. Diagnosis and identification of data sources
The first step is to draw up a clear picture of the organisation's information environment.
This includes:
Inventory of systems (accounting, CRM, ERP, production, Excel, NPO, marketing);
Data quality analysis;
Mapping of existing flows.
This diagnosis helps to identify the issues, priorities and data that are really useful for decision-making.
2. Definition of business needs
Successful implementation must be guided by the needs of managers and teams.
We'll clarify here:
Key indicators (KPIs) to be tracked;
Recurring reports to be automated;
Strategic questions to be answered by the warehouse.
This stage avoids building a solution that is too complex or not aligned with business objectives.
3. Architecture and modelling
Based on the requirements defined, the structure of the data warehouse is designed:
Star or snowflake model;
Fact tables (sales, costs, production);
Dimension tables (customers, products, employees, dates).
The aim is to organise the data in a logical way to ensure speed, consistency and analytical efficiency.
4. Development of ETL/ELT pipelines
ETL (Extract-Transform-Load) or ELT (Extract-Load-Transform) pipelines are used to :
Extract data from source systems;
Clean and structure it;
Automatically load it into the data warehouse.
This is the heart of the solution: it guarantees that the information is reliable, uniform and updated according to a defined frequency (daily, hourly, real-time).
5. Integration with a BI tool (e.g. Power BI)
Once the data is available, it is connected to a Business Intelligence tool for visualisation:
Financial reports;
Performance analyses;
Real-time visibility of operations.
Power BI is particularly well suited to SMEs and organisations in Quebec, thanks to its value for money and flexibility.
6. Testing, validation and training
Before the official roll-out :
Data are validated;
Results are compared with source systems;
Users are trained.
This stage guarantees the accuracy of the analyses and ensures that the solution is adopted by the teams.
7. Continuous improvement and addition of new sources
A data warehouse is never static. Over time, you can :
Add new systems (e.g. payroll, HR, marketing);
Integrate new indicators;
Improve models;
Optimise performance.
Continuous improvement enables the data warehouse to evolve with the organisation and support decision-making over the long term.
Turn your data into strategic leverage for your organisation
Implementing a data warehouse means giving your organisation a solid foundation for making better decisions, automating your reporting, improving your performance and accelerating your digital transformation.
At Mallette, our experts will help you structure your data, make your analyses more reliable and build a solution tailored to the reality of your organisation.
Need help launching or improving your data warehouse? Our experts are here to guide you.