Remove Data Integration Remove Publishing Remove Risk
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. Data quality is no longer a back-office concern. The decisions you make, the strategies you implement and the growth of your organizations are all at risk if data quality is not addressed urgently.

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Proposals for model vulnerability and security

O'Reilly on Data

Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data integrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Disparate impact analysis: see section 1.

Modeling 278
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CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

IT 59
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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

Our customers tell us that the fragmented nature of permissions and access controls, managed separately within individual data sources and tools, leads to inconsistent implementation and potential security risks. Having confidence in your data is key.

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Seamless integration of data lake and data warehouse using Amazon Redshift Spectrum and Amazon DataZone

AWS Big Data

Business units can simply share data and collaborate by publishing and subscribing to the data assets. The Central IT team (Spoke N) subscribes the data from individual business units and consumes this data using Redshift Spectrum.

Data Lake 109
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Get Analytics with Reports Your Users Can Understand!

Smarten

Gartner defines Data and Analytics (D&A) as, ‘…the ways organizations manage data to support all its uses, and analyze data to improve decisions, business processes and outcomes, such as discovering new business risks, challenges and opportunities.’

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Nexthink scales to trillions of events per day with Amazon MSK

AWS Big Data

We also lacked a data buffer, risking potential data loss during outages. Such constraints impeded innovation and increased risks. Our services consuming this data inherit the same resilience from Amazon MSK. Secure data access Transitioning to our new architecture, we met our security and data integrity goals.