Data Architecture

Data Architecture is a framework of models, policies, rules and standards that an organization uses to manage data and its flow through the organization. Within a company, everyone wants data to be easily accessible, to be cleaned up well, and to be updated regularly. Successful data architecture standardizes the processes to capture, store, transform and deliver usable data to people who need it. It identifies the business users who will consume the data and their varying requirements.

A good approach to data architecture is to make it flow from data consumers to data sources, not the other way. The goal is to transform business requirements into data and system requirements. Companies need to have a centralized data architecture that aligns with business processes and provides clarity about all aspects of data. The individual components of data architecture are the outcomes, activities, and behaviors.

Data architecture is the purview of data architects. A data architect builds, optimizes, and maintains conceptual and logical database models. They determine how to source data that can propel the business forward and how that can be distributed to provide valuable insights to decision-makers.

Data Architecture Principles

Data architecture principles include the set of rules that pertain to data collection, usage, management, and integration. These principles form the foundation of the data architecture framework and help build effective data strategies and data-driven decisions.

• Validate All Data at Point of Entry

It's important to improve the overall health of organizational data by eliminating bad data and common data errors. Design your data architecture to flag and correct errors as soon as possible. A Data Integration Platform can help do that – validate data automatically at the point of entry. This will also help minimize the time taken to cleanse and prep data.

• Strive for Consistency

Using a Common Vocabulary for data architecture will help users on the same project to collaborate. Shared data assets like product catalogs, fiscal calendar dimensions, etc. must use common vocabulary regardless of the application or business function. Users of such shared data must work from the same core definitions to maintain control of data architecture and data governance.

• Everything Should be Documented

Get into the habit of documenting all parts of your data process so that data visibility and data remain standardized across the organization. Documentation should help you keep a tab on how much data is collected, which datasets are aligned, and which applications need to be updated. Consistent documentation should work seamlessly with data integration.

• Avoid Data Duplication and Movement

Every time data is moved, it impacts cost, accuracy, and time. Modern data architectures should reduce the need for additional data movement to reduce cost, improve data freshness and optimize data agility.? Modern data architecture views data as a shared asset and does not allow departmental data silos. This makes it simpler to universally update data, and everyone can operate from a single version of the data.

• Users Need Adequate Access to Data

Data architecture books state that users must be provided the right interfaces to consume data using designated tools.

• Security and Access Controls Are Essential

The emergence of data security projects has made it easier to ensure unified data security. Data architectures must be designed for security without compromising access controls on the raw data.

Data Architecture Framework

There are multiple enterprise architecture frameworks that are used as the foundation for building the data architecture framework of an organization.

• DAMA-DMBOK 2

This refers to DAMA International's Data Management Body of Knowledge – a framework designed specifically for data management. It includes standard definitions of data management terminology, functions, deliverables, roles, and also presents guidelines on data management principles.

• Zachman Framework for Enterprise Architecture

John Zachman created this enterprise ontology at IBM during the 1980s. The 'data' column of this framework includes multiple layers like key architectural standards for the business, a semantic model or conceptual/enterprise data model, an enterprise or logical data model, a physical data model, and actual databases.

• The Open Group Architecture Framework (TOGAF)

TOGAF is the most used enterprise architecture methodology that offers a framework for designing, planning, implementing, and managing data architecture best practices. It helps define business goals and align them with architecture objectives.