july 23, 2025
Why a business needs a DWH and how to justify the project
Can you measure the ROI of a data warehouse?
In most companies, data is stored in a fragmented manner across different systems. Access to these systems is typically restricted by department and role. For example, financial and tax data may only be accessible to accounting, while information about marketing campaigns is mainly relevant to the marketing team.
The task of analysing all this data falls to the BI system.
But before visualisation and analysis can happen, the data must be made accessible to analysts: it needs to be integrated, structured, and cleansed of errors. For effective analysis and planning, historical data spanning several years is often essential.
This is where a corporate data warehouse (DWH) comes in — to consolidate and organise data.
DWH implementation projects are complex and resource-intensive. They require the involvement of system analysts, DWH architects, developers, DevOps specialists, and data engineers. In addition to payroll costs, budgets must also cover infrastructure and technology — as there is no ready-made, out-of-the-box DWH solution.
So how can a business justify investing in a DWH given these costs? What business problems can a DWH actually solve? And is it possible to measure its ROI and effectiveness? Read on to find out.
What is a DWH – in simple terms
A Data Warehouse (DWH, or corporate data warehouse) is a centralised repository where business data is collected and processed.
- A DWH integrates data from various sources — such as databases, information systems, spreadsheets, and APIs.
- All data is cleansed, enriched, structured, and organised in a way that makes it easy to analyse.
- The data becomes readily accessible for analysis at any time, enabling teams to quickly generate reports, perform analytics, and make fact-based decisions.
Unlike traditional databases, which are designed for transactional processing and application support, data warehouses are built to provide the performance and depth of storage required for advanced analytics.
A DWH enables the storage of both current and historical data — without compromising data quality.
Why does a business need a data warehouse?
Seamless data integration
Data scattered across CRM, ERP, Excel spreadsheets, and external databases makes aggregation and analysis difficult. The introduction of new systems, business processes, mergers, and acquisitions only adds complexity.
A DWH consolidates data from diverse sources, providing fast and simplified access to accurate and reliable information.
Improved data quality and value
During ETL/ELT processes, data is cleansed of errors and duplicates, reducing manual effort from re-entry or redundant inputs.
The warehouse stores only aggregated values from relevant sources, eliminating unnecessary data that doesn’t contribute to analysis.
High-quality, structured, and up-to-date data ensures more accurate analysis and reporting.
Data security
A DWH operates independently of operational databases and doesn’t burden other systems. Analysts can run complex queries and work with large volumes of data without risking performance or compromising critical business data.
This is especially important for large companies with multiple departments, especially in industries like banking or insurance, where sensitive personal and financial data is handled.
Data democratisation and self-service capabilities
Data democratisation means making data easily accessible to everyone, not just management — eliminating bottlenecks in the analytics process.
Together with modern BI tools, a DWH provides clean, current data and self-service functionality, allowing any end user to access what they need and build reports independently.
Timely data access and decision-making
A DWH stores historical data on sales, production, inventory, logistics, and more — often no longer held in operational systems — allowing for retrospective analysis and strategy review.
Previously, to investigate rising costs, analysts had to download spreadsheets from ERP, and accounting systems and compile reports manually. This delayed response and worsened the issue.
With a DWH, all the necessary data is already aggregated and visualised in a BI system. Executives only need to interpret the dashboards to act.
Discovery of hidden opportunities and innovation
A DWH can reveal new opportunities for business growth and development.
Using historical data enables advanced analytics such as:
- Predictive analytics — forecasting future trends
- Prescriptive analytics — identifying steps to improve efficiency and modelling scenarios using AI and ML
The analytical data marts layer within the corporate DWH serves as a reliable data source for machine learning algorithms.
Furthermore, a DWH scales easily with business growth and data volume, making it suitable for launching Big Data and Data Science initiatives.
Increased trust in data and analytics
A DWH reduces human error and ensures data is timely, complete, and consistent — increasing the reliability of reports.
Accurate and comprehensive reporting builds trust among both internal and external stakeholders.
Leaders turn to analytics to uncover hidden patterns, optimise processes, and make informed decisions based on facts rather than intuition.
Can the effectiveness of DWH implementation be measured?
In times of economic uncertainty, companies are increasingly focused on reducing technology spending and carefully assessing the return on investment (ROI) for business analytics initiatives.
Implementing a DWH project often requires substantial budget approval — which must be justified.
To evaluate the effectiveness of such a project, two key factors must be considered:
- The costs of implementing and maintaining the data warehouse
- The benefits gained from its use
When implemented properly, the benefits of a data warehouse consistently outweigh its costs.
According to IDC research, the ROI from implementing analytics tools based on a data warehouse can reach up to 417% over five years, with the average payback period being less than one year.
DWH implementation costs
Initial costs for implementing a data warehouse vary depending on the specific needs of the business.
It’s also important to consider that the total cost of ownership (TCO) for a data warehouse goes beyond the initial implementation costs.
During implementation, business goals may evolve, and the organisation may decide to scale the project.
Annual support and maintenance expenses typically account for 40% to 60% of the initial implementation cost.
Key cost factors include
- The number and type of data sources
- Diversity of data formats and type
- Specific data processing requirements
- The chosen architecture and technology stack
- Requirements for fault tolerance, scalability, and availability
Additional considerations:
- Complexity of BI and analytics requirements
- Number of users and diversity of roles
- Deployment format (on-premises or cloud)
- Security and compliance requirements
- Salaries of project team members
- Employee training
- Ongoing support and updates
- Expected business value of the implementation
Benefits of implementing a DWH
The challenge in evaluating the benefits of a DWH lies in the fact that it does not generate revenue directly. Instead, the return on investment comes from increased process efficiency.
Intangible benefits
- Competitive advantage
- Improved relationships with stakeholders
- Timely and well-informed managerial decisions
Tangible (measurable) benefits
- Increased profitability
Changes to marketing strategy, portfolio expansion, and attracting new target audiences - Cost control
High-quality data enables loss detection and expense optimisation - Analyst productivity
Reduced time spent on report preparation and faster response times - Reduced IT costs
Centralised data reduces reliance on legacy systems and allows better resource allocation within IT
