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Data architecture plays a crucial role in shaping an organization’s ability to efficiently use and manage data. Understanding the core principles behind modern data architecture can help organizations make informed decisions and create a robust data infrastructure.
We are using our developers’ insights to outline six essential principles for designing effective data architecture.
In modern data architecture, data is considered a shared asset, meaning that every department should use the same data sources. Eliminating data silos across departments ensures that all stakeholders have a complete view of the data sets and that access is consistent throughout the organization.
An essential principle of good data architecture is providing users with adequate access to the data they need to perform their jobs. From experienced data analysts to salespeople, all stakeholders should have an interface that allows them to access the required data easily and efficiently.
Security is a crucial aspect of modern data architecture. The design must ensure that only authorized individuals can access sensitive data. A robust data architecture supports data governance by establishing rules for data security and interactions between systems.
Once data is shared as a single asset, it is vital to establish a common understanding of the KPIs, definitions, product catalogs, and dimensions within the data sets. This ensures that all stakeholders have a consistent understanding of the information contained within the data assets.
Data should always be curated to ensure it provides value to the organization. This involves investing in core functions such as data cleaning, modeling, and defining key dimensions and measures. Proper data curation leads to more accurate reporting on metrics like sales, discounts, and profits.
The final principle of modern data architecture is optimizing data flows for agility. This involves reducing the number of times data is moved between systems before reaching the end-users. Minimizing data movement reduces costs, improves data freshness, and increases the refresh rate frequency, which is crucial for time-sensitive decisions.
While not everyone is a data architect, understanding these six core principles can help you guide discussions on data architecture within an organization. For data architects, keeping these principles in mind while designing or improving data architecture can significantly enhance an organization’s ability to manage and utilize its data effectively.
But just using core principles is, of course not enough. To start building a comprehensive data architecture, you also need to learn its main components.
Data pipelines refer to the process in which data is collected, moved, and refined. This process includes data collection, refinement, storage, analysis, and delivery. The seamless flow of data from collection to delivery in the business intelligence (BI) and analytics layers is a crucial aspect of data architecture.
While not all data architectures rely on cloud storage, most modern data architectures leverage it for better agility, scaling, and data delivery. Cloud storage provides a flexible, scalable, and cost-effective solution for storing data in today’s data-driven world.
In addition to using cloud storage, many modern data architectures also utilize cloud computing for data analysis and management. By leveraging cloud computing for processing power instead of local machines, organizations can improve their data processing capabilities significantly.
Modern data architectures often incorporate artificial intelligence (AI) and machine learning (ML) models to automate tasks such as data collection and labeling. A robust data architecture enables organizations to unlock the potential of AI and ML for improved efficiency and insights.
Data streaming, which involves transmitting data in real-time or near real-time, is another critical component of modern data architecture. Platforms like YouTube and Facebook rely on data streaming to deliver content directly to consumers, showcasing the importance of this component in today’s digital landscape.
Container orchestration, such as open-source Kubernetes, automates software deployment, scaling, and management. This component helps organizations streamline their data architecture, enabling them to adapt quickly to changing business needs and requirements.
Real-time analytics is a goal for many organizations, as it eliminates delays in data availability for reporting and BI solutions. By ensuring data flows almost instantly, real-time analytics enables organizations to make more informed decisions based on the most current data.
While data architecture can seem like a complex and theoretical subject, understanding its main components is crucial for those involved in data-driven decision-making.
To tackle organizing proper data architecture, you need the right data management tools that will offer the following features:
AINSYS offers these and other tools and syncs data between every tool and platform your IT team employs, helping you get an accurate picture of your software. By implementing AINSYS tools, any business can organize proper data governance to make the right decisions for your organization and keep up with the ever-changing technology landscape.
