For many companies, data is growing at a faster rate than ever before, and so is its complexity. Common use cases at many companies range from ML/AI to marketing analytics, and infrastructure varies widely, from spreadsheets to data warehouses. In this complicated environment, data architecture is increasingly essential to managing, securing, and activating data throughout an organization.
Data architecture is the blueprint for achieving your business goals with data. But there’s much more to the topic, and that’s what we will cover in this blog.
Read on for a full overview of data architecture, including why it’s so important for today’s data-saturated companies.
Data Architecture: The Overview
Data architecture refers to a framework that governs how an IT infrastructure upholds an organization’s data strategy.
This framework translates business needs into data assets and manages organizational data flow. Data architecture also offers a blueprint for the database management systems, data warehouses, data lakes, BI tools, and other technical platforms that actuate a data strategy.
Data architecture delineates the organization’s logical and physical data assets and data management resources. It includes the models, standards, policies, and rules to monitor and control various companies’ acquisition, storage, arrangement, integration, and usage of data.
The framework converts business needs into system requirements to manage data flow through the company.
Data architecture is often described by a set of predefined policies and diagrams. These include:
- Data flow diagrams depicting the data flows through systems and applications
- Data models and data definitions
- Documents to map data usage to the processes of an organization
- Standards and policies for data operations
- High-level architectural blueprints
Data architecture documentation also describes business goals, consumer needs, and core concepts for data management functions.
The Technologies Behind Data Architecture
Technologies transform data architectures from documentation to existence, including components as diverse as machine learning, automation, the Internet of Things, and blockchain.
Some of the key technological features of modern data architectures include:
- Cloud-native: Today’s data architectures build and maintain applications in a distributed computing environment hosted in a cloud delivery model. These frameworks are compatible with end-to-end security and high data availability, with the added functionality of cost and performance scalability.
- Scalable data pipelines: The transportation of data from source to destination should be compatible with fast-growing volumes of data. That’s why modern data architectures should support instant data streaming and micro-batch data bursts.
- Seamless data integration: An application’s new module or feature must be integrable without causing any noticeable complications. Usually, modern data architectures integrate with legacy applications using standard API interfaces. Modern data architectures must be capable of sharing data across systems and organizations.
- Decoupled and extensible: The components of the system are not constrained on the same platform, build environment, and operating system. Modern data architectures are loosely coupled to perform minimal tasks regardless of other services.
- Real-time data enablement: Modern data architectures must engage in active data management in compliance with enforced data policies in real-time. These frameworks must build and deploy automated data validation, management, classification, and governance.
How to Develop a Data Architecture
Data management teams need to work in coherence with business executives and consumers to develop a data architecture. It ensures that business strategies, data requirements, and the architecture itself are in sync with each other.
Here are some sample steps for developing a data architecture:
- Meeting with senior executives for their support and requirements.
- Engaging with end-users to understand their data needs.
- Assess the risks and challenges associated with data based on data governance policies.
- Build and track data lineage, data lifecycle, and data flows.
- Evaluate the existing data management technology infrastructure for any discrepancies.
- Develop a roadmap for the data architecture deployment projects.
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Popular Data Architecture Frameworks
Here’s an overview of some of the most popular data architecture frameworks in wide use today:
- DAMA-DMBOK (DAMA International’s Data Management Body of Knowledge) is developed explicitly for data management. It explains guiding principles for data management, also providing definitions for data management functions, deliverables, and roles.
- Zachman Framework for Enterprise Architecture is an enterprise structural framework for organizing information created by John Zachman at IBM during the 1980s. The data column includes several layers. Additionally, it comprises architectural standards, an enterprise data model, a semantic model, a physical data model, and actual databases.
- The Open Group Architecture Framework (TOGAF) is an enterprise architecture ontology that offers a high-level framework to develop enterprise software packages and applications. It follows a systematic approach to organizing the development process. This approach focuses on curtailing errors, managing timelines, ensuring cost-effectiveness, and aligning Information Technology with business units to produce desirable results.
Strong Data Architectures Make Strong Companies
As the volume and complexity of data continue to grow, strong companies need strong data architectures to thrive.
Modern data architectures allow companies to translate vast quantities of disparate data into manageable assets that can be leveraged to achieve business objectives.
That’s why, for a data-driven company, a modern data architecture is no longer just a competitive edge. A modern data architecture is the backbone of a data-first company.