Data Architect

What is a data architecture?

A data architecture describes how data is managed--from collection through to transformation, distribution, and consumption. It sets the blueprint for data and the way it flows through data storage systems. It is foundational to data processing operations and artificial intelligence (AI) applications.

The design of a data architecture should be driven by business requirements, which data architects and data engineers use to define the respective data model and underlying data structures, which support it. These designs typically facilitate a business need, such as a reporting or data science initiative.

As new data sources emerge through emerging technologies, such as the Internet of Things (IoT), a good data architecture ensures that data is manageable and useful, supporting data lifecycle management. More specifically, it can avoid redundant data storage, improve data quality through cleansing and deduplication, and enable new applications. Modern data architectures also provide mechanisms to integrate data across domains, such as between departments or geographies, breaking down data silos without the huge complexity that comes with storing everything in one place.

Modern data architectures often leverage cloud platforms to manage and process data. While it can be more costly, its compute scalability enables important data processing tasks to be completed rapidly. The storage scalability also helps to cope with rising data volumes, and to ensure all relevant data is available to improve the quality of training AI applications.