Five Essential Data Architecture Principles
By Paramita (Guha) Ghosh on
Data Architecture principlesare a set of policies that govern the enterprise data framework with its operating rules for collecting, integrating, using, and managing data assets. The basic purpose of the Data Architecture principles is to keep the supportive data framework clean, consistent, and auditable. The overall enterprise Data Strategy is built around these principles.
In recent years, DA principles have gone through a major overhaul to accommodate modern Data Management systems, processes, and procedures. The modern-day DA principles help lay the foundation for a Data Architecture that supports highly optimized business processes and advance recent Data Management trends.
Here is a list of Data Management trends that forced global organizations to take a critical look at their existing Data Architecture:
- Shift from on-premise to cloud-based data platforms
- Reduced costs of stream processing, favoring real-time over batch processing
- Pre-made commercial data platform replaced by scalable and customizable modular solutions
- Data reuse and APIs for data access
- Shift from data lakes to domain-based data storage
- Shift from predefined data models to flexible data schemas
Within an enterprise, every user wants clean, easily accessible data that is updated on a routine basis. Effective Data Architecture standardize all Data Management processes for quick delivery of data to people who need it. The existing Data Architecture designs need to change to keep pace with evolving Data Management requirements.
As a McKinsey author observes, “many new and advanced technology platforms have been deployed alongside legacy infrastructure” in global enterprises in the recent years, These novel technology solutions like the data lake, customer analytics platform, or stream processing have put tremendous pressure on the performance capabilities of the underlying Data Architecture. The existing Data Architecture has failed to deliver enhanced support, or have even failed to maintain the existing data infrastructures.
Additionally, with the rising adoption of AI and ML platforms for business analytics and BI activities, the time has come for an overhaul of enterprise Data Architecture. As is true for any technology transformation, the Data Architecture principles“ developed, tried, and tested” for the present-day Data Architecture are quite different from those of legacy Data Architecture.
This post reviews some core principles that define an AI-ready, modern Data Architecture.
