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Data architecture strategy for data quality

IBM Big Data Hub

Several factors determine the quality of your enterprise data like accuracy, completeness, consistency, to name a few. But there’s another factor of data quality that doesn’t get the recognition it deserves: your data architecture. How the right data architecture improves data quality.

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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . But first, let’s define the data mesh design pattern.

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The New Normal for FP&A: Data Analytics

Jedox

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it. Data analytics is not new.

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Power analytics as a service capabilities using Amazon Redshift

AWS Big Data

Analytics as a service (AaaS) is a business model that uses the cloud to deliver analytic capabilities on a subscription basis. This model provides organizations with a cost-effective, scalable, and flexible solution for building analytics. times lower cost per user and up to 7.9 Amazon Redshift delivers up to 4.9

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When Private Cloud is the Right Fit for Public Sector Missions

Cloudera

These systems can pose operational risks, including rising costs and the inability to meet mission requirements. . In the private sector, excluding highly regulated industries like financial services, the migration to the public cloud was the answer to most IT modernization woes, especially those around data, analytics, and storage.

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2023 Predictions: Data Trends That Will Dominate Business Agenda in APAC

Cloudera

In the past year, businesses who doubled down on digital transformation during the pandemic saw their efforts coming to fruition in the form of cost savings and more streamlined data management. However, a significant amount of this spend is wasted as organizations struggle to optimize costs effectively. .

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.