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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries. I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. This challenge remains deceptively overlooked despite its profound impact on strategy and execution.

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Improve Business Agility by Hiring a DataOps Engineer

DataKitchen

The DataOps Engineering skillset includes hybrid and cloud platforms, orchestration, data architecture, data integration, data transformation, CI/CD, real-time messaging, and containers. The rise of the DataOps Engineer will completely change what people think of as possible in data analytics.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

However, you might face significant challenges when planning for a large-scale data warehouse migration. Effective planning, thorough risk assessment, and a well-designed migration strategy are crucial to mitigating these challenges and implementing a successful transition to the new data warehouse environment on Amazon Redshift.

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

erwin

With complex data architectures and systems within so many organizations, tracking data in motion and data at rest is daunting to say the least. Harvesting the data through automation seamlessly removes ambiguity and speeds up the processing time-to-market capabilities.

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Automate discovery of data relationships using ML and Amazon Neptune graph technology

AWS Big Data

Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern data architecture is critical in order to become a data-driven organization.

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Breaking down data silos for digital success

CIO Business Intelligence

Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. The aim is to normalize, aggregate, and eventually make available to analysts across the organization data that originates in various pockets of the enterprise.

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Connecting the Data Lifecycle

Cloudera

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. . The company needed a modern data architecture to manage the growing traffic effectively. .