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Define the goals and requirements of the data-centric architecture.
02
Identify and map the data sources and their relationships.
03
Design a data model that represents the structure and organization of the data.
04
Choose appropriate data storage technologies such as databases or data warehouses.
05
Implement data integration processes to gather data from different sources and consolidate it.
06
Develop data access mechanisms and interfaces for users and applications to interact with the data.
07
Implement data security measures to protect the confidentiality, integrity, and availability of the data.
08
Continuously monitor and optimize the data-centric architecture to ensure its effectiveness and performance.

Who needs data-centric architecture to realize?

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Businesses that deal with large volumes of data and need efficient ways to manage and analyze it, such as data-driven companies or enterprises.
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Organizations that require real-time access to accurate and reliable data for decision-making purposes, such as financial institutions or government agencies.
03
Companies that want to improve data governance and data quality by centralizing and standardizing their data management processes.
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Industries that rely heavily on data-driven technologies, such as e-commerce, healthcare, or telecommunications.
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IT professionals and data architects who are responsible for designing and implementing data-centric solutions.
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Data-centric architecture to realize refers to a design approach that prioritizes data management and utilization as the core of application development and system architecture, emphasizing the importance of data flow, storage, and processing.
Entities that manage significant datasets, including businesses, organizations, and government agencies, are typically required to file data-centric architecture to realize, particularly if they must comply with regulatory standards.
To fill out data-centric architecture to realize, one must outline the data structures, describe data flows, specify storage solutions, and outline data governance practices, ensuring all aspects of data handling are documented.
The purpose of data-centric architecture to realize is to enhance data management, streamline data processes, ensure compliance with regulatory requirements, and improve overall data quality and accessibility.
Information that must be reported includes data inventories, data flow diagrams, governance frameworks, compliance statuses, and risk assessments related to data handling.
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