Data architecture is the process by which an organization aligns its data environment with its operational goals. This may require building an entirely new data infrastructure from the ground up, or it might involve restructuring existing data assets.
Many enterprises employ a dedicated data architect to oversee this process. Data architects have a high level of technical knowledge of data warehouses, data lakes, and the complex technologies underlying these structures. Because data architecture is driven by business objectives, architects also have strong communication and change management skills.
How Does Data Architecture Work?
Before any organization can tackle the problem of data architecture, they must first ensure that they have reliable data governance in place. This means having a policy framework in place to ensure that all data meets the primary standards for security, accuracy, and integrity.
The tasks associated with data architecture include:
- Documenting current data structures: The architect compiles a detailed overview of the current state. This includes details of all existing data sources, plus all relationships and dependencies.
- Gathering data requirements: The architect then works with business stakeholders to identify key objectives and decide how these can be supported by data. The architect can help the organization to understand the potential applications of data, which may lead to new data-driven objectives.
- Prioritizing data sources: Some data sources are more mission-critical than others. In some instances, it might be necessary to make infrastructure changes to improve data quality, such as upgrading to a cloud-based CRM.
- Determining data extraction methods: Each data source has a different extraction method, such as an API, a file export, or a direct query. When using an ETL system like Xplenty, the architect can use the built-in integration functionality rather than determining an integration method for each source.
- Designing a data architecture: Data architects work with data engineers to create a solution that delivers business objectives. The architect makes key decisions such as how to implement ETL, which data storage structure to adopt, and how to integrate with other systems. Data engineers build these structures according to the architectural specifications.
- Building a real-time data environment: Business units need access to the new data environment and any applications based on this environment, such as analytics and reporting tools. The data architect will oversee the implementation of these solutions.
- Auditing security: Security is an integral part of data architecture, right from the first step. When the new data infrastructure is online, the organization should conduct a full security audit to ensure that there are no potential breaches or risks.
Successful data architecture is a structure that helps the organization to realize the full potential of its data. This means meeting all data-related business objectives while creating a scalable infrastructure that supports future development.