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6 business risks of shortchanging AI ethics and governance

CIO Business Intelligence

In a 2019 survey by Emerj , an AI research and advisory company, 14% of AI researchers said that AI was an “existential threat” to humanity. Even if the AI apocalypse doesn’t come to pass, shortchanging AI ethics poses big risks to society — and to the enterprises that deploy those AI systems. Public relations disasters.

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Top 7 Data Governance Blog Posts of 2018

erwin

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. Defining Data Governance.

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The Foundations of a Modern Data-Driven Organisation: Change from Within (part 2 of 2)

Cloudera

A 2020 retention report by the Work Institute revealed that over 42 million employees in the US left their jobs voluntarily in 2019, and this trend appeared to be increasing. Risk Management. A 2019 HBR article mentioned how organizational decisions backed by data have instilled more confidence and reduced risk.

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The What & Why of Data Governance

erwin

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value. The What: Data Governance Defined. Data governance has no standard definition.

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AI adoption in the enterprise 2020

O'Reilly on Data

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind? Bottlenecks to AI adoption.

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Pillars of Knowledge, Best Practices for Data Governance

Cloudera

And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. But it’s still not easy.

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Data Mesh Architecture and the Data Catalog

Alation

In contrast to this common, centralized approach, a data mesh architecture calls for responsibilities to be distributed to the people closest to the data. Middlemen — data engineering or IT teams — can’t possibly possess all the expertise needed to serve up quality data to the growing range of data consumers who need it.