Five Main Aspects of a Data Architecture
This is an ongoing series of articles about data architecture. In this series, I will share the major takeaways and complex concepts from the course Data Architect.
Data architecture is a set of rules, policies, and models that govern how an organization's data is collected, stored, and used. It includes the design of data models, the selection of appropriate technologies, and the creation of policies and procedures for data management. The purpose of data architecture is to ensure that data is accurate, accessible, and secure and that it supports the organization's goals and objectives.
Here are five aspects you should consider when designing a data architecture.
Business Needs
Understanding the business needs is an architect's most important step, though most learners ignore it. Creating a system consisting of line after line of code and rack after rack of servers with flashing LEDs has been a lure for young developers who want to be data architects. That’s what you have seen in the movies and what you have read from GitHub and Blogs. However, every data database is created with a need behind it. A person or a company comes to you and asks you to manage some digital files. There is a question coming into the seasoned data architect.
Is there a need for a Data Architecture?
Does the request have a specific data problem behind it? Does your customer have any data now? Does the problem posed by your customer align with their business? Is a digital tool like Excel enough to log simple transaction data for the small business owner? Or are they looking for a more sophisticated system for the growing company to manage inventory and customer data? The architect must understand the business needs and determine if a data architecture is necessary.
Data Integrity
Data integrity refers to the accuracy, consistency, and reliability of data. It ensures that data is not corrupted or lost due to human error or system failures. A well-designed data architecture should include measures to maintain data integrity, such as data validation rules, error detection and correction mechanisms, and backups and redundancy.
To simplify, Data Integrity asks for a solution to the question. How can I ensure no duplicates or conflicts of the same information?
Data Governance
Data governance involves the policies and procedures for managing data within an organization. It includes defining roles and responsibilities for data management, ensuring compliance with regulations and standards, and establishing data quality and security processes. A well-designed data governance framework provides that data is managed effectively and efficiently.
Data Ownership refers to accountability and responsibility for the data. This includes determining who is responsible for maintaining data accuracy and completeness. The data ownership should be clearly defined to avoid potential disputes or misunderstandings. And the data owner is also typical regard as The Single Source of Truth.
Data Management involves the processes, policies, and procedures for managing data throughout its lifecycle. The Database Administrator is the person who makes sure the database functions technically and maintains the data environment clean and tidy.
Data Accessibility refers to the ability to access and use data. It involves ensuring that data is available when needed and that the appropriate people can access it. Different user groups might require different access levels, from read and upload to modify or delete. A good data architecture should provide easy access to data while maintaining the security and integrity of the data.
Scalability & Flexibility
The ability to scale up or down to accommodate data growth or changes in business needs is critical for a successful data architecture. To achieve scalability, data architecture must be designed with a distributed and parallel computing approach that can handle large volumes of data without sacrificing performance. Flexibility, on the other hand, refers to the ability of the data architecture to adapt to changing business requirements, new technologies, and evolving data sources. Flexible data architecture can quickly and easily incorporate new data sources and integrate them with other systems, enabling organizations to gain valuable insights and make informed decisions. Combining scalability and flexibility in data architecture ensures that organizations can efficiently manage and process vast amounts of data while staying agile and responsive to changing business needs.
Retention
Data retention refers to the policies and procedures for storing, backup and archiving data. This aspect is typically set by the company or industrial standards. For example, an LSMV system for aircraft inspection generates Terabytes of data per hour should consider dropping non-feature-related data before reaching the hard disk. Unless the flight safety regulation or the concession report for customers requires keeping complete surface scanning data, designing large storage for millions of images is not worth it.