The way organisations use data is changing – How do you build a modern data architecture to align with it?

Key Considerations for designing a Modern Data Architecture

There is no one-size-fits-all solution for data architecture. If you have determined that your current architecture is no longer sufficient and want to transition to a modern approach, there are several critical factors to consider. These factors are essential for building an architecture that meets both current and future data needs.

Key considerations include:

1. Data requirements and diversity

Determine what information your organisation needs and from which sources or applications this data must come. What types of data (structured or unstructured) are involved, and how large are the datasets? As organisations rely on an increasing number of applications, it is crucial that the architecture can easily integrate new data sources and grow alongside the organisation.

2. Performance requirements

What performance level does your data integration processes require? For some applications, batch processing is sufficient (e.g., financial reports), while others require real-time data (e.g., demand-supply matching or online sales optimisation). Real-time data is becoming increasingly important for fast and accurate decision-making but requires specific technologies and storage structures.

3. Security, compliance, and governance

Every organisation must comply with regulations like GDPR. The extent to which you offer self-service BI, for example, impacts how access control is structured. Additionally, certain sectors, such as healthcare or government, face stricter regulations. These factors influence how governance and security are implemented within your data architecture and where data is stored.

4. Team composition

The choice between in-house or outsourced solutions and the technologies used depends on the competencies and capacity within your data team. Script-based technologies may be more complex to implement and maintain compared to modern cloud-based tools. Ensure your team has a good balance of data engineers, architects, and analysts.

5. Budget considerations

Finding a balance between performance requirements and available resources is crucial. Real-time data processes offer significant advantages but are generally more expensive than batch processing. It is essential to clearly define organisational priorities and manage expectations accordingly.

Key Components of Modern Data Architecture

Identifying the above factors helps in designing the following architecture components:

• Data storage structure: For raw, standardised, integrated, and consumable data.
• Data storage methods: Choose between cloud, hybrid, or on-premises solutions, depending on performance and storage requirements.
• Governance and security: Access control, encryption, and compliance with regulations.
• Self-service capabilities: With analytics tools like Qlik Sense and Power BI.

Finally, a solution is needed to orchestrate, manage, and automate processes within your modern data architecture. This brings us to data integration tooling.

Data Integration as the core of the Architecture

Data integration is the heart of modern data architecture. It brings data together from various sources, transforms it, ensures quality, and makes it available in the correct format for analysis and AI applications. Modern tools make it possible to automate these integration processes and enable both real-time and batch-oriented data streams to coexist.

Stay tuned for our next blog, where we will dive deeper into data integration, exploring challenges, pain points, and benefits. Interested in practical examples or hands-on experience? Sign up for our webinars coming in March and April!