The term “data architecture” refers to a collection of implementable standards and protocols that govern the collection, storage, preparation, sharing, and distribution of data. This predefined set of standards and protocols is designed to enhance the scope and purpose of data analysis in the busy business environment.

One major problem with traditional data management systems was that while many generic features were sold by technology vendors, the custom components were often developed in bits and pieces later, as ad hoc solutions. Thus, the data architectures of these “Band-Aided” systems lacked holistic controls, which could be prescribed at the very beginning of an enterprise data management project. Massive amounts of data without solid data architecture best practices can be like a data swamp – ready to drown the clueless business users.

In modern data architecture, this problem has been well addressed, and currently the technology teams within organizations follow a standardized blueprint to develop and implement the enterprise data architecture.

In the pre-cloud era when storage costs were high, it was hard to implement many of the data management best practices. Now, with lightning-fast servers, increased storage, and reduced costs, organizations are finding it easier to implement those difficult best practices.

One example is data quality, which is best tackled as a holistic architectural approach – integrated as a core component of data architecture.

Why Are Effective Data Architecture Best Practices Necessary? 

With the rise of data-as-a-service (DaaS) as a core component of cloud business strategy, effective data architecture is becoming imperative. This data architecture ensures data governance, data accuracy, and on-demand data availability for business users.

The main layers of modern data architecture consist of a physical layer, which is the hardware components and data preparation technologies; the logical layer defining the relationships between the different data types within the architecture framework; and the data-sharing layer, which defines how data is shared between users and processes.

In recent times, businesses have completely moved away from their legacy systems to make way for real-time product recommendations, custom offers, and multiple customer communication channels.

The emergence of smart technologies and newer data platforms has made data architectures very complex, very often hindering efficiency. The traditional data architecture model of three-to-five-years’ lifespan is increasingly at risk.

Now is a good time to think of an architecture-driven, data-centric business. As newer and better data technologies continue to evolve, technical experts will have to explore and devise best practices to adapt these new technologies into a new data architecture model that is not only agile and resilient, but also future-proof.

What Makes Data Architecture Best Practices Effective?

The goal of modern data architecture is to provide a framework for managing data across multiple data platforms.  

To take the discussion further, review some fundamental features of a modern data architecture:

• Precise Business Objectives: Historically, the availability of data-influenced business decisions determined what decision-makers needed. With modern data architecture, the rules of the game have changed. Now, the decision-makers can define precise business objectives and manipulate data sets to meet the exact requirements of data analytics.
  
• Data Sharing: Good data architecture removes data silos and promotes collaboration. In such architecture, data from diverse business units and multiple sources are collected, collated, and stored in a single location to eliminate duplication. Data sharing is the key element in this environment.
  
• Automation: Data pipelines between diverse business processes and systems have become streamlined due to automation. Cloud-based data architectures facilitate agile data integration and data sharing across diverse systems.
  
• Smart Architecture: With the easy availability of sophisticated AI and machine learning (smart) tools, modern data architecture enjoys the capability of making on-demand adjustments to remove data quality errors and data identification errors, and discovering fresh insights.
  
• Elasticity: Over the past decade, data architecture has moved from data warehouses to data lakes and then back again to data warehouses – completely unsure of how to meet the growing needs of a business. Recently, business owners and operators realized that a data architecture that scales with the changing demands of a business is the answer to data architecture sustainability. Now, thanks to the cloud and AI technologies, businesses can opt for on-demand services related to scalability.
  
• Security: All modern data architectures are required to be compliant with data privacy and security. Data regulations like the General Data Protection Regulation (GDPR) need to be strictly followed while developing the architecture model. The Best Practices in Metadata Management discusses how the majority of business operators are expressing the importance of deriving “value from data” in a highly data-regulated world.

The Future of Data Management

Forbes has recorded what customers are saying about the future of data. Most industry leaders agree that acquiring a massive amount of data and using that data effectively are two entirely different activities.

To win with data in a business, the business operators need effective data architecture as the starting point. One of the primary characteristics of a data-centric organization is a centralized, data-rich platform that can be combined with open-source solutions for deep analytics and timely decisions. An online retailer, Wayfair, with a cloud-based, central data repository is a case in point.

In data-centric organizations, the pace of innovation has been a game-changer. New data technologies and tools are becoming available in the market every day, and it is becoming almost impossible for businesses to keep pace with the technological choices offered to data teams today.

While at one end, data quality and metadata management have continued to remain major challenges, the limitless technological solutions available in the market further adds to that challenge. A recent webinar provides a discussion about integrating the available technologies with data architecture best practices to help a business derive immediate value from data analytics while keeping a firm eye on long-term sustainability of the data architecture.

Conclusion

When a solid data architecture is not in place, it is inevitable for a data user to spend time on extracting and organizing data rather than on analyzing that data. Global organizations need to become more data-aware and build data architecture best practices to maintain a firm grip on all data activities.

Now, with advanced data storage and data processing technologies available, it is possible to make the initial time and cost investments for developing more agile, more robust, and more efficient data architecture. With these efficient data management processes replacing the archaic systems of data management, the highly paid data professionals can concentrate on extracting value from their data.