Remove Data Governance Remove Data Quality Remove Publishing
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The state of data quality in 2020

O'Reilly on Data

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

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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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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

Despite decades of investment in data management solutions, many continue to struggle with data quality issues, either through their failure to modernise legacy investments or through the outcomes of acquisitions and business decisions, which in either instance have led to data existing in multiple silos across their organisations.

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Data Quality Is Free

Anmut

They made us realise that building systems, processes and procedures to ensure quality is built in at the outset is far more cost effective than correcting mistakes once made. How about data quality? Redman and David Sammon, propose an interesting (and simple) exercise to measure data quality.

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Automated data governance with AWS Glue Data Quality, sensitive data detection, and AWS Lake Formation

AWS Big Data

Data governance is the process of ensuring the integrity, availability, usability, and security of an organization’s data. Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake.

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HEMA accelerates their data governance journey with Amazon DataZone

AWS Big Data

Data has become an invaluable asset for businesses, offering critical insights to drive strategic decision-making and operational optimization. Initially, the data inventories of different services were siloed within isolated environments, making data discovery and sharing across services manual and time-consuming for all teams involved.

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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

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

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