Remove Data Quality Remove Measurement Remove Uncertainty
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Sustainability: Real progress but also thorny challenges ahead

CIO Business Intelligence

Dealing with uncertain economic environments, which can distract from sustainability issues: Energy prices, price inflation, and geopolitical tensions continue to fluctuate, and that uncertainty can impact focus on environmental sustainability. The key is good data quality.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. Fragility occurs when a built system is easily “broken” when some component is changed.

Strategy 290
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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. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

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Why HR professionals struggle with big data

CIO Business Intelligence

Most use master data to make daily processes more efficient and to optimize the use of existing resources. This is due, on the one hand, to the uncertainty associated with handling confidential, sensitive data and, on the other hand, to a number of structural problems.

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AI Product Management After Deployment

O'Reilly on Data

It is entirely possible for an AI product’s output to be absolutely correct from the perspective of accuracy and data quality, but too slow to be even remotely useful. For AI products, these same concepts must be expanded to cover not just infrastructure, but also data and the system’s overall performance at a given task.

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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 173
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Systems Thinking and Data Science: a partnership or a competition?

Jen Stirrup

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Systems thinking helps the organization frame the problems in a way that provides actionable insights by considering the overall design, not just the data on its own.