What does a data warehouse cost? Costs and ROI explained

The decision to implement a data warehouse is not just a technical one but also a business decision. It’s an investment that you’ll want to justify properly—internally (to management and stakeholders) and externally (to customers or investors). In this section, we’ll dive into the cost structure of a data warehouse and how to calculate its return on investment (ROI). We’ll also cover how to get started on a limited budget and scale up over time.

Get in touch

Request a quote

Direct and indirect costs

A data warehouse involves both direct and indirect costs. Gaining clarity on these prevents unpleasant financial surprises and allows for realistic planning.

1. Hosting

• Cloud costs

Think of variable costs with Google Cloud (BigQuery), AWS (Redshift), or Azure (Synapse Analytics). These services usually charge by storage (GB/terabyte) and by query/processing.

• On-premises

If you opt for an on-premises solution, you’ll face hardware investments, cooling, energy consumption, and security.

2. Development

• Initial Implementation

Setting up infrastructure, connecting data sources, and building the basic ETL/ELT processes.

• Further Development

Once the foundation is in place, new questions typically arise within the organization. This leads to extra development, such as adding new data sources or expanding the data model.

3. Maintenance & support

• Regular maintenance

Technical components (ETL scripts, database schemas) and workflows must be kept up to date.

• Data quality

Ongoing checks, error handling, and updates to data structures as systems evolve.

• Monitoring & notifications

It’s often wise to set up automation that detects issues and alerts you when problems occur.

4. Licenses and tools

• ETL- or ELT-software

Many organizations use tools like Fivetran, Stitch, Airbyte, dbt, or Talend. These licenses can scale up in cost as you add more data sources or traffic.

• BI and reporting tools

Tools like Looker Studio, Tableau, Power BI, or others. Some require (premium) license fees.

• APIs and plug-ins

Some data sources charge extra for data packages or premium features.

5. Training and knowledge development

• Internal staff

Your team needs to understand how the data warehouse works. Think of workshops, internal documentation, or online courses.

• External specialists

If you don’t have an in-house data engineer or BI specialist, hiring a consultant or agency may be part of your cost.

6. Indirect costs

• Change management

A data-driven culture sometimes requires reorganizing processes and workflows, which takes time and resources.

• Missed opportunities from delay

Waiting too long to professionalize your data may cause you to miss out on revenue or efficiency gains that competitors are already realizing.

How to calculate the ROI of a data warehouse

A data warehouse improves process efficiency, provides a better foundation for marketing decisions, and can directly contribute to revenue. But how do you quantify that return?

1. Time savings through automation

• Manual reporting

Without a data warehouse, teams may manually compile data from various sources into Excel, which can take hours or even days. A data warehouse and BI tool can automate much of this process.

• Faster troubleshooting

With good monitoring and alerts, problems are identified and resolved more quickly, minimizing operational disruptions (e.g., in your webshop).

2. Better marketing decisions

• Targeted campaigns

With integrated data, you instantly see which campaigns truly deliver ROI, rather than relying on fragmented figures from silos. This increases ROAS (Return on Ad Spend).

• Customer segmentation

By combining CRM, website, and purchase data, you can identify customer behavior patterns and allocate marketing budgets more strategically.

3. Increased revenue

• Upsell and cross-sell opportunities

A data warehouse can uncover insights into customer preferences, allowing for proactive recommendations.

• Growth in customer loyalty

With deeper customer knowledge (e.g., CLV analyses, segmentation), you can better focus on retention, which is often more profitable in the long run than acquiring new customers.

4. Cost savings

• More efficient staff allocation

With automated reporting, your team can focus on strategic work instead of repetitive data tasks.

• Procurement and inventory

Data insights help better align inventory with demand, saving on storage costs and spoilage.

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.

Don’t See Your Question?
Feel free to reach out.

Maximize your data impact with us

Optimal data centralization is the foundation of every data strategy. Want to take your data to the next level with our BigQuery consultants? Request a quote!

Or would you like to discuss other data challenges? We are happy to have a no-obligation conversation about how we can help.

Schedule a meeting