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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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IKEA’s Data Transformation: Lessons from a Global Giant

Timo Elliott

At IKEA, the global home furnishings leader, data is more than an operational necessity—it’s a strategic asset. In a recent presentation at the SAPSA Impuls event in Stockholm , George Sandu, IKEA’s Master Data Leader, shared the company’s data transformation story, offering valuable lessons for organizations navigating similar challenges.

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

CIO Business Intelligence

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

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Set up alerts and orchestrate data quality rules with AWS Glue Data Quality

AWS Big Data

Alerts and notifications play a crucial role in maintaining data quality because they facilitate prompt and efficient responses to any data quality issues that may arise within a dataset. This proactive approach helps mitigate the risk of making decisions based on inaccurate information.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

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Development Strategies to Prevent Data Quality Issues in Production (Part 1)

Wayne Yaddow

These strategies can prevent delayed discovery of quality issues during data observability monitoring in production. These strategies minimize risks, streamline deployment processes, and future-proof data transformations, allowing businesses to trust their data before it ever reaches production.