Data Warehouse & Data Architecture

Thoughtfully designed data warehouse architecture for precise analytics and measurable results

Unified relational database as the foundation for business intelligence
Operational efficiency through integrated data architecture and streamlined data management
Faster analytics and more accurate reporting of real-time data possible
Informed decisions based on reliable data that ensure integrity across all business units
Secure and scalable data warehouse design built on best practices for data governance and data quality

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Why a Data Warehouse is Essential for Your Business

A data warehouse forms the foundation of a modern data strategy and serves as the backbone of an enterprise data warehouse environment. It integrates data from various sources and operational systems, securely stores historical data, and makes it available in a single repository for analytics and business intelligence. This allows you to manage information efficiently, accelerate data analysis, and make decisions based on a solid data management and data integration framework.

Centralized Data Integration

A modern data warehouse connects and integrates data from multiple source systems such as ERP, CRM, finance, and other business applications. Using automated ETL processes and data pipelines, it can also extract data from source systems, cleanse raw data, and load it into a structured database. The result is a unified single source of truth across the enterprise, ensuring consistent reporting and minimizing data redundancy.

Leverage Historical Data

By systematically capturing and storing historical data and huge amount of data, you can identify long-term trends and gain valuable insights through predictive analytics. A well-designed data warehouse architecture makes it easy to analyze data from both – current and past sources, supporting better forecasting and long-term planning.

Secure Data Management

Manage your data in a centralized, controlled, and transparent way using a robust database management system. At the same time, make it securely available for reporting and data analysis for business users, analytics teams, and operational systems. Your operational data store (ODS) ensures consistent data governance, while clear access policies maintain data quality and traceability.

Automated Data Updates

Automated data pipelines keep your data warehouse continuously up to date. Modern ELT workflows help load data efficiently and eliminate data redundancy. Manual tasks and potential errors are reduced, ensuring long-term data quality and consistency across the enterprise.

Scalable Architecture

Build scalable models that adapt to your company’s size and growing data volumes. This flexibility allows you to easily integrate new requirements or data sources as your organization evolves.

Governance & Compliance

Using a flexible schema design your data in a warehouse remains agile and ready for new flow of data sources or business processes. A well-structured data warehouse also enhances data governance and compliance. Clear frameworks and database structures provide transparency and trust in business intelligence reports.

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What We Deliver in the Area of Data Warehousing

We design and implement a centralized enterprise data warehouse (e.g., Azure or SQL Server) tailored to your data systems and requirements
Our experts develop data warehouse architecture with a focus on governing, security, and scalability
We automate ETL processes and data pipelines for real-time reporting and optimized query processing
Analysis-ready data models for Power BI, Excel, and business intelligence tools
We integrate data science and analytics solutions for deeper insights into large amounts of data
Precise data mapping, data transformation, and data cleansing ensure accuracy across the enterprise
Seamless integration with ERP, CRM, HR, and customer relationship management systems for consistent business data flows
Comprehensive documentation and training so your business users can manage and store data independently
Ongoing data engineering support and optimization as your data warehouse evolves

Who Benefits from Modern Data Architecture

Our data warehouses are optimized to help organizations of all sizes to manage and analyze structured data efficiently. We combine strategic consulting with data engineering and technical implementation so business users, data scientists, and data engineers share the same central data repository.

Mid-sized companies building scalable data warehouse architecture to handle large amounts of data efficiently
PE-backed and founder-led organizations modernizing business intelligence and analytics processes
CFOs, controllers, and IT leaders needing a single enterprise data warehouse for planning, reporting, and forecasting
Clients in Germany and the U.S. with bilingual implementation and international experience in data management

The Core Functions of a Data Warehouse

Data Integration

A data warehouse collects and unifies information from different sources such as ERP, CRM, and financial systems. This integration creates a consistent data foundation, breaks down silos, and enables an organization-wide perspective. All information is consolidated in one place and made available for analytical processes.

Data Cleansing and Transformation

Before use, raw data is cleansed, validated, and transformed. This data transformation occurs within structured database layers to ensure data quality. Inaccurate, duplicate, or incomplete records are corrected to ensure that only high-quality information is included in analysis. This guarantees that decisions are based on reliable data.

Data Aggregation

In this step, dimensional data is consolidated into meaningful key metrics and dimensions. The data warehouse condenses complex information, allowing for faster and clearer evaluations. This aggregation supports management and business units in identifying trends and patterns.

Data Storage

A data warehouse provides permanent data storage for consolidated information in one repository. This storage simplifies access, accelerates queries, and ensures data security and traceability. As a result, your entire dataset remains accessible, transparent, and audit-proof at all times.

Our Process and Timeline Schema

The duration of each project depends on its individual scope and requirements and therefore cannot be generalized. The timeline below serves as an example to illustrate our approach and provide a better understanding of the typical phases. Your specific project schedule will be defined together during the initial planning stage.

A high-performing data warehouse is built on careful planning, technical precision, and a resilient database structure. Our transparent workflow ensures smooth implementation from initial analysis to full production use. Here is what a typical project timeline looks like:

Week 0 to 1:

Project initiation, data source inventory, and requirements workshop.

Week 1 to 6:

Architecture design, setup of data pipelines, and test query performance between database management systems.

Week 6 to 10:

Data warehouse rollout, dashboard setup, and results validation.

Week 10 to 12:

Training sessions, documentation, and handover to your internal team.

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Get Started with a Scalable Data Architecture

A modern data warehouse lays the foundation for powerful analytics and sustainable growth. We help you integrate multidimensional data, transform raw data into insights, and build a repository that scales with your business.

Whether you modernize existing operational databases (OLTP) or create a comprehensive new enterprise data warehouse, we design a secure, scalable environment aligned with your business processes and data governance requirements.

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Modern Technologies for Sustainable Data Architecture

Cloud and on-premise options for your data warehouse
Secure integration with Microsoft 365, ERP, CRM, and other data sources
Automated data updates and real-time reporting
Access rights management and logging for compliance
Structured data queries and analytics based on the central database

The Advantages of Data Warehouses

Centralized Data Foundation: Modern data warehouses integrate information from multiple sources, providing a consistent, organization-wide view across all business areas.
Cloud Flexibility: Cloud-based data warehouses offer scalability, security, and access from anywhere ideal for dynamic data environments.
Analytical Power: A well-structured architecture makes it easier to extract, model, and prepare data efficiently for analytics.
Big Data Integration: Even unstructured big data can be seamlessly processed and incorporated into existing data models.
Historical Perspective: Current and historical data can be combined to reveal trends, developments, and forecasts.
Greater Efficiency: Automated data flows reduce manual effort and accelerate reporting and analytical processes.

Why Radial?

Big Four quality combined with the speed and agility of a boutique
Leadership by former Big Four consultants, a bilingual team, and a private-equity mindset
Data-driven delivery: dashboards, models, and governance you fully control
Proactive communication and clearly defined responsibilities that ensure tangible results every single week

Ready to take the next step?

Your data holds the key to better decisions and a modern data warehouse is the first step toward unlocking that potential. If you’re ready to optimize your data structures and use professional data engineering to get more value from your information, let’s talk.

We’ll help you build a stable, scalable data architecture that creates transparency, streamlines processes, and accelerates analytics. This is how your data becomes a true competitive advantage.

Get in touch with us today and tell us what you need.

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Frequently Asked Questions

What is Data Warehousing?

Data warehousing refers to the process of collecting, structuring, and centrally storing data from different sources to make it available for analytics and reporting. Unlike operational systems, a data warehouse serves as an analytical platform where companies can use data mining to extract valuable insights from large data sets. Within a data warehouse, additional data marts can be created specialized segments focused on specific departments or business topics. A data mart allows for faster, more targeted analysis without the need to query the entire database.

What is meant by Data Architecture?

Data architecture defines the structure by which data is organized, stored, and processed within an organization. A well-designed data architecture establishes how information flows between systems, how it is secured, and how it can be leveraged for data warehousing and business intelligence purposes.

Which business models do you support?

We work with clients across manufacturing, mechanical and plant engineering, SaaS, project-based businesses, e-commerce, consumer goods, and industrial products. Our frameworks, driver logics, and KPIs are always adapted to your specific business model.

How do you ensure data governance and security?

All our solutions are built using best practices for governance, secured access, and full traceability of data movements. We implement role-based permissions, encryption standards, and compliance controls tailored to your industry.

Can you really integrate all systems?

Yes. We connect data from ERP, CRM, HR, payroll, and custom systems to create unified reporting and analytics across your organization.

Do you support both cloud and on-premise configurations?

Yes. We design and build data warehouses that are 100% aligned with your IT environment and compliance requirements.

Will we be able to grow and scale in the future?

Absolutely. We provide comprehensive documentation and training so your team can operate and expand the system as your data landscape evolves.

How does this help the finance department and management?

It creates a single source of truth for faster decision-making, automated reporting, and real-time analytics. Finance leaders gain greater transparency, while management benefits from consistent KPIs and improved forecasting accuracy.

Data Warehouse vs. Data Lake – what’s the difference?

A data warehouse stores structured data for reporting and analytics, while a data lake is primarily used for large volumes of unstructured data. Modern approaches such as the data lakehouse combine both concepts offering the analytical power of data warehouses and the flexibility of data lakes to handle multiple data formats on a single platform.

What our customers say

Radial has helped us make great strides in our efforts to digitize financial reporting and increase transparency. I look forward to the next steps, in which we will once again be relying on Radial for support.

Thorsten Preiss, DETAX GmbH

Unlock the Full Potential of Your Data

A well-designed data warehouse is more than just a storage system. It’s the foundation for informed decisions, clear analytics, and long-term success. With the right tools and expertise, we help you turn raw data into structured, actionable insights that drive measurable results.

Whether you want to optimize existing systems or build a new data architecture from the ground up, we’ll guide you from strategy to implementation- efficiently, transparently, and aligned with your business objectives.

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