With an increase in technological choices, businesses and data teams face challenges in remaining competitive. Data architecture is the starting point for a business's success with data. A data-driven organization has a data-rich centralized platform that can be integrated with open-source solutions for decisions and deep analytics.
The more data assets an organization assembles, the more likely will be the need to consider data architecture and best practices. Competitive advantage, customer focus, and process restructure can all be attributed to companies' ability to build a data architecture ahead of time.
Hence, it would help lay the right foundations before using customer data to plunge into analytics. Implementing the correct data architecture principles will enhance data strategy and help organizations improve data quality.
Before we move on, let’s first understand the need for best practices.
The Need for Data Architecture Best Practices
Over the last few years, organizations have transitioned from legacy systems to real-time product recommendations, customizations, and various channels for customer communication. The emergence of new technologies, platforms, and tools has increased data architecture complexity and slowed its efficiency.
Robust data architecture is becoming more critical as Data-as-a-Service (DaaS) becomes a crucial element of cloud business strategies. A successful data strategy depends on the right data architecture. This is the set of rules, policies, and standards that determine the kind of data collected, usage and storage, data management, and how data will be incorporated within your business. Often, technical and business teams fail to coordinate when data architecture best practices are not implemented. An effective data strategy depends on improving this process. Consider a data-centric, architecture-driven business strategy now. As new data technologies evolve constantly, technical experts must formulate best practices to integrate new technologies into agile and robust data architecture models.
Modern data architecture consists of three main layers:
Physical layer: Involves the hardware components and technologies that prepare data
- Logical layer: Defines how different data types relate to each other in an architectural framework
- Data-sharing layer: Regulates data sharing between processes and users
The following are some best practices that we consider helpful for businesses to keep up with contemporary business trends.
Data architectures must be designed keeping in mind the end-users. Business requirements and their users are more important than the data itself. Decision-makers can now clearly define their business objectives and engineer data sets to meet specific data analytics needs. Hence, creating a data architecture that supports user-centricity is equally as important as its ability to grow and evolve with a business user's needs.
The unpredictable business scenarios require data architectures to be flexible enough to adapt to fluctuating conditions. Data architecture must cater to a broad range of users. They must provide numerous features to accommodate the wide variety of business use cases and strategies. Hence, data architectures must remain abreast with business realities and dynamic data processing requirements.
Data architectures must manage and maintain the constant entry of large amounts of data. Data must be streamlined and seamless from the source that collects the data to the business consumers. A data architecture uses pipelines to move and convert data into valuable insights. Automated processes detect anomalies, trigger alerts in real-time, and achieve seamless data flow. Additionally, machine learning/AI can help you keep the data moving. With AI, data architectures become more flexible as learning capabilities are enhanced.
Modern data architectures must comply with privacy and security requirements. When designing the architecture model, strict adherence to regulations such as General Data Protection Regulation (GDPR) is mandatory. Encrypting all data before analysis and anonymizing any personally identifiable information (PII) is essential. The data catalog is used to identify unusual activity, such as unauthorized access. Moreover, it manages data life cycles and ensures the efficiency of all activities related to data. Users also have their unique points of access to the data architecture based on their functions and data access requirements.
Keeping an eye on all data activities requires organizations to become more data-savvy and implement data architecture best practices. A lack of a solid data architecture means the end-user will likely spend more time gathering and organizing data instead of evaluating it. Now that advanced data storage and data processing technologies are available developing robust, efficient, and agile data architectures is easier.