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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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How BayCare Health System excels in raising data literacy

CIO Business Intelligence

If we understand the volume of patients in the hospital and the level of care they need, and can predict future staffing needs, we provide better care for less cost. So if we can see the data behind low appointment times, we can create incentive programs to book those slow times. We’re using data to reduce that wait time.

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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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How the right data and AI foundation can empower a successful ESG strategy

IBM Big Data Hub

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

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Top 6 Benefits of Automating End-to-End Data Lineage

erwin

Replace manual and recurring tasks for fast, reliable data lineage and overall data governance. It’s paramount that organizations understand the benefits of automating end-to-end data lineage. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.

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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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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. The solution should be scalable, cost-efficient, and straightforward to adopt and operate.