Data Architecture
Data Architecture

Data Architecture Best Practices: A Guide for Modern Data Management

Buckle up for an exciting data-driven revolution! With the global big data market projected to skyrocket to $473.6 billion by 2030, riding a robust Compound Annual Growth Rate (CAGR) of 12.7% from the year 2022 onwards, making it evident that we’re entering into a new era of endless opportunities. As volume, velocity, and variety of data continue to grow, fueled by the widespread embrace of advanced analytics and artificial intelligence (AI), the possibilities are limitless.

Our goal in this article is to thorough exploration of modern data architecture. We have covered the needs and the big data architecture best practices for you.

Why Data Architecture Best Practices are Necessary?

As data-as-a-service (DaaS) takes centre stage in cloud business strategies, the need for robust data architecture has become a necessity. This architecture not only guarantees data governance but also maintains data security and fulfils demands like on-demand data access for business users.

Modern data architecture comprises 3 major layers: the physical layer is a combination of hardware components and data preparation technologies, the logical layer weaves the connection between the various data types within the architecture framework, and then the data sharing layer, as the name signifies it defines how the data is shared between the users and processes.

In the past few years, businesses have undergone a huge transition i.e. from legacy systems to real-time product recommendations, personalized offers and multi-customer-communication channels. However, this transition acts as a perfect moment to shift towards an architecture-driven, data-centric approach in business.

Best Practices for Modern Data Architecture

Best Practices for Modern Data Architecture

Looking to ensure a positive ROI for your modernization project? The following data architecture best practices can help you achieve your target.

  1. Eliminate Internal Data:

    To modernize data architecture, it is an important factor to break down the internal data barriers. A large number of companies suffer from information silos, causing the inefficiency and poor data management. When data is scattered across various repositories, it often leads to duplication and confusion about what information is accurate. Modernization often works with an aim to remove internal data barriers, ensuring clean, validated, and beneficial data to the organization.

  2. Ensure All Your Data Is Reliable:

    Modernizing your data architecture is essential to ensure that your data is accessible to all those who need it. While, in the beginning, it is important to integrate, clean and validate the data from internal sources, later the companies need to rely on data coming from external sources as well. Modernizing your data architecture includes ensuring you have data to ingest, cleanse, re-duplicate and validate.

  3. Consider Diverse Data Structures and Formats:

    In the past time, data analysis majorly focused on the structured data that could be analyzed using the standard tools. However, the rise of big data and cloud computing leads to a significant increase in both structured and unstructured data volumes. Hence, it’s imperative that your data architecture is designed to handle data from various sources in different formats, involving both structured and unstructured data. Failing to do so means that you are missing out on the valuable information that is necessary for making a well-informed business decision.

  4. Establish Effective Data Governance Practices:

    Maintaining data quality is a continuous effort, and it is essential that your data architecture processes at every stage. Therefore, as part of your data modernization best practices, it is crucial to implement a robust data governance policy within your organization. This approach helps to gain confidence in your data, enabling you to leverage it for strategic decision-making that provides a competitive advantage.

  5. Prepare for the Future:

    While modernizing your data architecture, it is crucial to have a forward-looking perspective. In this process, you’ll require a solution that scales quickly, and effortlessly manage your current data volume, while accommodating your future business growth. Construct for the future by adopting an architecture that is agile, adaptable, and enables real-time analysis and reporting.

Conclusion

Effective data management relies on essential data architecture practices. This guide offers strategies to navigate the data-driven landscape. With the global big data, the market is projected to grow, while robust architecture is essential for governance and accessibility. Modern architecture includes 3 layers- physical, logical and data-sharing. Shifting to real-time operations presents opportunities for data-centric approaches. By following the best practices for data architecture, businesses can leverage data assets for strategic decisions and a competitive edge.

AAHENT is an expert in data architecture, with extensive knowledge and hands-on experience in crafting robust data frameworks. AAHENT, having the finest experience in this field, excels in designing and implementing data architectures customized to meet diverse business needs and requirements. With a keen eye for detail and strategic data modernization approach, AAHENT navigates complex data environments seamlessly. AAHENT is well-known for its innovative data architecture solution deliveries.