Remove Data Integration Remove Data Quality Remove Insurance
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AWS Glue Data Quality is Generally Available

AWS Big Data

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

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The Need For Personalized Data Journeys for Your Data Consumers

DataKitchen

While this is a technically demanding task, the advent of ‘Payload’ Data Journeys (DJs) offers a targeted approach to meet the increasingly specific demands of Data Consumers. Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap.

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

Cloudera

Perhaps the biggest challenge of all is that AI solutions—with their complex, opaque models, and their appetite for large, diverse, high-quality datasets—tend to complicate the oversight, management, and assurance processes integral to data management and governance. proprietary data, business strategies, methodologies, etc.

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Why data observability is essential to AI governance

erwin

And if it isnt changing, its likely not being used within our organizations, so why would we use stagnant data to facilitate our use of AI? The key is understanding not IF, but HOW, our data fluctuates, and data observability can help us do just that. And lets not forget about the controls.

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7 Benefits of Metadata Management

erwin

It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization. Better data quality. It harvests metadata from various data sources and maps any data element from source to target and harmonize data integration across platforms.

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

Despite soundings on this from leading thinkers such as Andrew Ng , the AI community remains largely oblivious to the important data management capabilities, practices, and – importantly – the tools that ensure the success of AI development and deployment. Further, data management activities don’t end once the AI model has been developed.

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Transforming FSI in ASEAN with Cloud Analytics

CIO Business Intelligence

Having disparate data sources housed in legacy systems can add further layers of complexity, causing issues around data integrity, data quality and data completeness. million in insurance fraud in just 7 months. .