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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Visualize data quality scores and metrics generated by AWS Glue Data Quality

AWS Big Data

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

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Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

The core issue plaguing many organizations is the presence of out-of-control databases or data lakes characterized by: Unrestrained Data Changes: Numerous users and tools incessantly alter data, leading to a tumultuous environment. Monitor freshness, schema changes, volume, and column health are standard.

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Analyzing the business-case approach Perdue Farms takes to derive value from data

CIO Business Intelligence

Mark Booth: We have a growth strategy to improve our business, and to support that, we’re driving a transformation in technology and business processes. But the more challenging work is in making our processes as efficient as possible so we capture the right data in our desire to become a more data-driven business.

Data Lake 135
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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

The research cited a lack of talent and skills to work with the technology (62%), unclear AI and GenAI investment priorities (47%), and the absence of a strategy for responsible AI (41%) as the top three obstacles. Reach consensus on strategy. GenAI requires high-quality data. But how do you get there?

Data Lake 142
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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

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Death by Data Cleansing (and How to Avoid It in 3 Steps)

Dataiku

In helping organizations around the globe set up and implement their data science and AI strategies, we often hear teams say that they’re waiting to figure out their data first before beginning to generate value with advanced analytics and AI — whether they’re referring to data quality, data silos, or centralization in a data lake.