10/29/2023 0 Comments Data architectThe Zachman Framework is an enterprise ontology created by John Zachman at IBM in the 1980s. Zachman Framework for Enterprise Architecture.It provides standard definitions for data management functions, deliverables, roles, and other terminology, and presents guiding principles for data management. DAMA International’s Data Management Body of Knowledge is a framework specifically for data management. There are several enterprise architecture frameworks that commonly serve as the foundation for building an organization’s data architecture framework. Data modeling takes a more focused view of specific systems or business cases. While both data architecture and data modeling seek to bridge the gap between business goals and technology, data architecture is about the macro view that seeks to understand and support the relationships between an organization’s functions, technology, and data types. On the other hand, DMBOK 2 defines data modeling as, “the process of discovering, analyzing, representing, and communicating data requirements in a precise form called the data model.” The goal of many modern data architectures is to deliver real-time analytics, the ability to perform analytics on new data as it arrives in the environment.Īccording to Data Management Book of Knowledge (DMBOK 2), data architecture defines the blueprint for managing data assets by aligning with organizational strategy to establish strategic data requirements and designs to meet those requirements. A container orchestration system such as open-source Kubernetes is often used to automate software deployment, scaling, and management. Data streaming is flowing data continuously from a source to a destination for processing and analysis in real-time or near real-time. At the same time, modern data architectures can help organizations unlock the ability to leverage AI and ML at scale. AI and ML are used to automate systems for tasks such as data collection, labeling, etc. Modern data architectures use APIs to make it easy to expose and share data.In addition to using cloud for storage, many modern data architectures make use of cloud computing to analyze and manage data. Not all data architectures leverage cloud storage, but many modern data architectures use public, private, or hybrid clouds to provide agility. It includes data collection, refinement, storage, analysis, and delivery. A data pipeline is the process in which data is collected, moved, and refined. Reduce the number of times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility.Ī modern data architecture consists of the following components, according to IT consulting firm BMC: Data flows should be optimized for agility.Invest in core functions that perform data curation (modeling important relationships, cleansing raw data, and curating key dimensions and measures). Shared data assets, such as product catalogs, fiscal calendar dimensions, and KPI definitions, require a common vocabulary to help avoid disputes during analysis. Common vocabularies ensure common understanding.Modern data architectures must be designed for security and they must support data policies and access controls directly on the raw data. Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs. Users require adequate access to data.A modern data architecture needs to eliminate departmental data silos and give all stakeholders a complete view of the company. Data architecture principlesĪccording to Joshua Klahr, vice president of product management, core products, at Splunk, and former vice president of product management at AtScale, six principles form the foundation of modern data architecture: Consulting firm McKinsey Digital notes that many organizations fall short of their digital and AI transformation goals due to process complexity rather than technical complexity. Many organizations today are looking to modernize their data architecture as a foundation to fully leverage AI and enable digital transformation. The goal of data architecture is to translate business needs into data and system requirements and to manage data and its flow through the enterprise. An organization’s data architecture is the purview of data architects. It is an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. Data architecture describes the structure of an organization’s logical and physical data assets and data management resources, according to The Open Group Architecture Framework (TOGAF).
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