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Building Your Human Benchmark with Ontotext Metadata Studio

Ontotext

In text analytics, the human benchmark is a set of documents manually annotated by human experts. This measures the consistency of annotations when more than one person is involved in the process. What Are The Benefits Of Using Ontotext Metadata Studio? What Is A Human Benchmark?

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Do I Need a Data Catalog?

erwin

Data catalogs combine physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals in certain circumstances. You also can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

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You Cannot Get to the Moon on a Bike!

Ontotext

Any type of metadata or universal data model is likely to slow down development and increase costs, which will affect the time to market and profit. In both cases, semantic metadata is the glue that turns knowledge graphs into hubs of data, metadata, and content. The diagram below illustrates this in a simplified form.

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Operationalizing responsible AI principles for defense

IBM Big Data Hub

But it’s equally important that they have a deep understanding of the risks and limitations of AI and how to implement the appropriate security measures and ethics guardrails. Note: These measures of responsibility must be interpretable by AI non-experts (without “mathsplaining”).

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The Digital Charter Implementation Act & Metadata Management

Octopai

The DCIA replaces the Personal Information Protection and Electronic Documents Act from 20 years ago. The Digital Charter covers aspects of digital policy ranging from increased digital access for Canadians to measures that protect democracy and accurately identify hate speech. What is the Digital Charter Implementation Act?

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Measure Twice, Cut Once: How the Right Data Modeling Tool Drives Business Value

erwin

There’s an expression: measure twice, cut once. Data modeling is the upfront “measuring tool” that helps organizations reduce time and avoid guesswork in a low-cost environment. Design-layer metadata can also be connected from conceptual through logical to physical data models.

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Deploy Amazon QuickSight dashboards to monitor AWS Glue ETL job metrics and set alarms

AWS Big Data

The metadata is extracted from each job run, including information like runtime, start time, end time, auto scaling, number of workers, and worker type, and is written to an Amazon DynamoDB table with TTL (time to live) enabled to ensure the table doesn’t grow too large. If the tables don’t exist, Athena creates them.

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