How to set up a data warehouse: Step-by-Step plan for companies

Setting up a data warehouse typically follows the ETL (Extract, Transform, Load) or ELT methodology. When designing this process, it’s important to create a scalable architecture so your infrastructure can grow along with increasing data volumes and new data points. Below is a concise explanation of the essential steps.

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Step 1: 1 Identify your data needs

A clear understanding of your data needs forms the foundation for a successful data warehouse. Determine which key questions and KPIs you want to answer (e.g., revenue growth, churn, inventory optimization, or customer satisfaction), including both current and future needs. A practical five-step approach:

Map out your goals and KPIs

Define the main business objectives (e.g., revenue growth or ROAS) and link KPIs to them.

Identify stakeholders

Determine which departments and individuals need to be involved (e.g., marketing, sales, procurement, customer service) and ensure they can provide input and help make decisions.

Distinguish between ‘must-haves’ and ‘nice-to-haves’

Define which data and reporting needs are critical to start with (MVP), and which can be addressed later.

Develop a Minimum Viable Product (MVP)

Focus initially on the most important questions and KPIs. This MVP is your starting point and can later be expanded with additional data sources and functionalities.

Iterate and expand

Use early insights and business feedback to continue expanding. By breaking down silos and linking various data sources (from marketing to returns), you create added value and improve efficiency.

This way, you build a solid foundation that makes your data warehouse future-proof and ensures it grows seamlessly with your organization.

Stap 2: Design a data model

The purpose of a data model is to connect business questions with available data. The model also defines relationships between different data sources. For example, linking Google Analytics 4 to back-end data via the transaction ID.

Design a data model in 4 steps:

Step 1: Translate data needs into clear business questions

Example: What is the revenue and profit per product category and ad channel for period X?

Step 2: Convert business questions into dimensions and metrics

Metrics represent values, such as revenue and profit.
Dimensions represent how you break down those metrics, such as product category, ad channel, and date.

Tip: Set standards for these values. For example, use YYYY-MM-DD as the standard date format and express values in Euros.

Step 3: Identify data sources

Determine which data sources need to be connected — for example, webshop data, CRM, Google Analytics 4, email marketing, and other systems. Define the relevant data points for each system.

Pro tip: Read the API documentation thoroughly and run tests to retrieve data samples. Not every tool or service exposes all data points. To avoid late-stage disappointment, research the technical limitations in advance.

Step 4: Visualize the data model

After mapping the data sources and defining dimensions and metrics, visualize your data model to show how fact tables (e.g., revenue, profit) and dimension tables (e.g., product category, ad channel, date) relate. Think in terms of star schemas, snowflake schemas, or ER diagrams (Entity Relationship Diagrams) to clarify structure and highlight MVP essentials.

Stwp 3: Extraction (E)

• Connect your data sources

Use APIs, export functions, or tools like Stitch, Fivetran, or Airbyte.

• Automate wherever possible

Ensure the data is fetched periodically or in real-time and made available in your data warehouse environment.

Step 4: Transformation (T)

• Clean your data

Remove incomplete or incorrect records and ensure consistent column naming.

• Enrich your data

Combine data from different sources and create dimensional tables (e.g., product, customer, sales, marketing spend).

• Apply business logic

Implement calculations for margins, conversion rates, or product categories.

Step 5: Loading (L)

• Store the data in your data warehouse

Preferably in a well-structured schema so BI tools can easily access and process it.

Step 6: Data governance and quality assurance

• Define ownership

Who is responsible for the quality of each dataset?

• Monitor quality

Continuously check if processes are running smoothly and if data remains consistent.

Stap 7: 7 Reporting & visualization

• Connect BI tools

Consider Looker Studio (formerly Google Data Studio), Power BI, Tableau, or Metabase.

• Build dashboards

Design reports to highlight key insights quickly.

These steps form the blueprint for a successful data warehouse. The more complex your organization and data landscape, the more important it is to roll out the implementation step by step — and with a clear plan.

Frequently Asked Questions

What is a data warehouse?

A data warehouse is a central repository where you bring together and organize data from various systems. It provides a single, reliable source of truth for your entire organization.
Want to learn more? Read the full article ‘what is a data warehouse?’ for more information.

What are the benefits of a data warehouse?

A data warehouse brings all your data together in one place, allowing you to report faster and more clearly. It gives your organization more control, deeper insights, and the freedom to store data without limitations.
Curious about all the benefits? Read the full article ‘what are the benefits of a data warehouse?’ here.

What are the trends in data warehousing?

Key trends in data warehousing include the decline of third-party data, stricter privacy regulations, and the rise of predictive analytics. Automation is also becoming more important due to a shortage of skilled personnel, and data is increasingly becoming a competitive advantage for businesses.
Want to learn more? Read the full article ‘what are the trends in data warehousing?’ here.

What are data warehouse hosting options?

The most well-known hosting options for data warehouses are Google Cloud Platform, Amazon Web Services, and Microsoft Azure — each offering strong integrations and scalable solutions. Google Cloud is especially popular due to its cost flexibility, ease of use, and strong connections with Google tools.
Want to learn more? Read the full article ‘what are data warehouse hosting options?’ here.

What are alternatives to a data warehouse?

Plug-and-play tools like Supermetrics offer a quick and easy way to collect data from marketing platforms, but they are less scalable and can become expensive as your business grows. For comprehensive and integrated data analysis, a robust data warehouse is often still the best choice.
Want to learn more? Read the full article ‘what are the alternatives to a data warehouse?’ here.

How do you set up a data warehouse?

You set up a data warehouse by first identifying your data needs and designing a data model. Next, you connect data sources, transform the data, load everything into the system, and ensure quality control and reporting.
Curious about the full approach? Check out the full article ‘how do you set up a data warehouse?’ here.

What are data warehouse use cases?

Data warehouses make it possible to gain insights into sales funnels, advertising effectiveness, inventory management, and margin optimization. They also support analyses of returns, employee performance, and customer value over time.
Want to know more? Check out the full article ‘what are data warehouse use cases?’ here.

What are the costs and ROI of a data warehouse?

The costs of a data warehouse consist of both direct and indirect components such as hosting (cloud or on-premises), development (implementation and further development), maintenance and support, tool licensing, and staff training. There are also indirect costs like change management and missed opportunities due to delays.
The ROI comes from time savings through automation, better marketing decisions thanks to integrated data, increased revenue through upsell and cross-sell opportunities, and cost savings through more efficient use of staff and inventory management.
For a full explanation, read the complete article on ‘the costs and ROI of a data warehouse’ here.

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