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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). Know thy data: understand what it is (formats, types, sampling, who, what, when, where, why), encourage the use of data across the enterprise, and enrich your datasets with searchable (semantic and content-based) metadata (labels, annotations, tags).

Strategy 289
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The state of data quality in 2020

O'Reilly on Data

The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials. Key survey results: The C-suite is engaged with data quality. And that’s just the beginning.

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The importance of governance: What we’re learning from AI advances in 2022

IBM Big Data Hub

IBM AI Governance is designed to help businesses develop a consistent transparent model management process, capturing model development time, metadata, post-deployment model monitoring and customized workflows.

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Get started managing partitions for Amazon S3 tables backed by the AWS Glue Data Catalog

AWS Big Data

If the partition isn’t loaded into a partitioned table, when the application downloads the partition metadata, the application will not be aware of the S3 path that needs to be queried. We also can see the partition metadata on the AWS Glue console. Load the partitions using the command MSCK REPAIR TABLE.

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

Another example of a sentinel is a marked increase in the volatility of stock market prices, indicating that there may be a lot of FUD (fear, uncertainty, and doubt) in the market that could lead to wild swings or downturns. In either case, keeping an eye on the situation is critical for the success of the operation.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. ” For example, these tools may offer metadata-based notifications. Complaints from dissatisfied customers and apathetic data providers only add to the mounting stress.

Testing 176
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BI Reporting Nightmare: Where Did This Error Originate?

Octopai

Anxiety and uncertainty descend. Performing BI metadata management manually is a complex operation requiring many dedicated hours to accomplish. Data Discovery – BI teams can locate metadata instantly, even if it is scattered across many different systems. Suddenly, that “typical,” quiet day looks very different.