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Data Quality vs Data Condition: The Power of Context

Anmut

Data without context is just meaningless noise, and any effort to improve or extract value from your data without considering the larger business context is doomed to fall short.? Unfortunately, traditional approaches to data remediation often focus on technical data quality in isolation from the broader data and business ecosystem.

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The Rise of Unstructured Data

Cloudera

Since learning with labeled data is known as supervised learning, methods that reduce the need for labels have names such as self-supervision, semi-supervision, weak-supervision, non-supervision, incidental-supervision, few-shot learning, and zero-shot learning. Data curation. months since 2012.

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Why you should care about debugging machine learning models

O'Reilly on Data

Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible.

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Swiss Federal Railways CIO relies more on AI than concrete

CIO Business Intelligence

We can only optimize,” said Jochen Decker at the Hamburg IT Strategy Days in February. The third part of the program, asset management, is intended to reduce material wear and tear, and make better use of the workshops. Artificial Intelligence, CIO, Data Management, Data Quality, IT Leadership, Transportation and Logistics Industry

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10 master data management certifications that will pay off

CIO Business Intelligence

That’s where MDM comes in to help create a single version of truth across all copies of master data to ensure data values are aligned. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers you have the right skillset. Informatica Master Data Management 10.3

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How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

Cloudera

This puts the onus on institutions to implement robust data encryption standards, process sensitive data locally, automate auditing, and negotiate clear ownership clauses in their service agreements. But these measures alone may not be sufficient to protect proprietary information. Train and upskill employees.

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Human-centered design and data-driven insights elevate precision in government IT modernization

IBM Big Data Hub

How to quantify the impact : Quantify, articulate and measure the expected long-term benefit of a capability to justify the investment. This strategy is not based on a “gut feel” or anecdotal knowledge of a few individuals, but on a well-documented, expansive set of findings, business analysis and calculated projections.