Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data assets on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture. 

In what follows, we offer a short overview of the overarching capabilities of a modern data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points form our checklist for what we perceive to be an anticipatory analytics ecosystem.  

Defining data architecture

You need to build a data analytics ecosystem that is attuned to your organization’s commercial strategy. It also has to fully align with your specific requirements when it comes to managing large volumes of data. Think of your data architecture as an interface between business goals and technical processes within an organization. You have a set of tools and practices used to manage your data pipeline. Then, you supplement these with processes aiming to transform big data. And you deliver it in an insight-ready form to those who will consume the outcomes.

Data architectures, therefore, have to start with the data consumers and prioritize their perspective. You have to be clear on specific consumer requirements such as speed and availability. You need to think about the order of magnitude — the data volume may be crucial for deciding on the final enterprise data architecture. But you also need to be aware of the scalability options and the level of automation required for your particular scenario.  

A data architecture is not a data warehouse

A data warehouse is an IT-centric formation. Whereas a data warehouse may be part of data architecture, it remains just one constituent of something more complex and expansive than a mere warehousing solution. Today’s data warehouses have become more flexible and may also fit well into the requirements of a contemporary analytics ecosystem. This overarching term, according to Wayne Eckerson, encapsulates a novel understanding of data architectures whereby the “new data environment is a living, breathing organism that detects and responds to changes, continuously learns and adapts, and provides governed, tailored access to every individual”. 

A data architecture is not a data platform

A data platform acts as an enabling entity. It builds on underlying database engines to gather and combine data coming from various sources. A data platform, therefore, is a hub for integrating heterogeneous data. This is where you can perform transformations, analytics, create reports and visualizations. A data platform facilitates the complex movement of data thanks to its built-in functionalities. For example, this includes engines and a toolchain that perform the data processing and prepare the data in an insight-ready form that can be consumed by the decision-makers within the organization. 

From an enterprise architecture perspective, data platforms are part of a continuum. Hereby data-related technical processes are interlinked with business rationale and vice versa. The concept of data architecture additionally incorporates the business goals and stakeholder values building up the data strategy of an organization. 

Data architecture best practices: our checklist

Implementing an end-to-end digital data architecture requires, first and foremost, an assessment of your key use cases and a careful look at future business requirements. In the first step, you need to revise your existing best practices. Look into use cases to determine which processes and values have been conducive to their success. Something to consider is how your use cases work within the broader context of your market strategy and the business needs you are pursuing. It is only after you have reviewed these business-specific realities that you can concentrate on building your data architecture. 

Let us see which are the focus features of a viable, future-ready, and good data architecture:

Focusing on user-centricity

Data architectures need to start with the very business users in mind. The data itself, the underlying technology facilitating ETL processes, data transformations, analytics, reporting, and visualization are all secondary to inherent business requirements and the users behind them. Cultivating user-centricity is just as central to the success of a data architecture as the ability to grow and evolve together with the needs of business users. 

Safeguarding flexibility and elasticity

Data architectures have to remain maximally flexible to adapt to volatile business necessities. As they need to serve a variety of users, data architectures need to provide a versatile catalog of features, capabilities, and integrations that can make them adapt to a breadth of business cases and market conditions. Further still, architectures need to be elastic and scalable. They need to keep current not only with business realities but also with dynamic data processing requirements. 

Ensuring a seamless data flow

Managing and maintaining the constant influx of high volumes of data is one important requirement for data architectures. Your data journey, from the source that is harvested to the business consumers, has to be seamless and maximally streamlined. The data architecture carries and transforms the data via various pipelines. The interconnected pipes are constructed out of data objects that can be re-adapted and re-utilized in a variety of new scenarios. This is how they serve the changing needs within the organization. This guarantees that users get their insight-ready data at the end of the day. 

Automated, with built-in intelligence

A seamless data flow can be achieved via automated processes with built-in real-time anomaly detection and alert triggering mechanisms. So on top of your data architecture, it is best to have machine learning/AI to keep the data movement. AI adds to the elasticity of data architectures as it enhances learning capabilities. This is how you enhance your data architecture’s capacity to adjust and respond to changing conditions. 

Considering security and data governance

A data architecture must be fully compliant with privacy regulations and data protection laws such as GDPR. All data should be encrypted before ingestion and personally identifiable information (PII) should be anonymized. For more information on this, have a look at our article “Data Anonymization Techniques and Best Practices: A Quick Guide”. A data catalog is created for the various data elements to identify unusual activity such as unauthorized usage. It also manages the life cycles of data objects and simply makes sure that all data locations and data-related activities are as intended. Further, each user, depending on their function and data access requirements, is allocated a user-specific point of access to the data architecture.

If you have opted for the cloud, you will find a comprehensive overview of data security strategies for cloud computing in our article “Data Security in the Cloud: Key Concepts and Challenges”.