Remove Data Quality Remove Metadata Remove Risk Remove Uncertainty
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift.

Strategy 289
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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. You might have millions of short videos , with user ratings and limited metadata about the creators or content. Models within AI products change the same world they try to predict.

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The Role of Data Governance During A Pandemic

Anmut

As a result, concerns of data governance and data quality were ignored. The direct consequence of bad quality data is misinformed decision making based on inaccurate information; the quality of the solutions is driven by the quality of the data. COVID-19 exposes shortcomings in data management.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 176
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Data Science, Past & Future

Domino Data Lab

What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things. You see these drivers involving risk and cost, but also opportunity. One is data quality, cleaning up data, the lack of labelled data.