Remove Data Quality Remove Experimentation Remove Measurement
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

encouraging and rewarding) a culture of experimentation across the organization. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired! This can be overcome with small victories (MVP minimum viable products, or MLP minimum lovable products) and with instilling (i.e., Test early and often.

Strategy 290
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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Measurement, tracking, and logging is less of a priority in enterprise software.

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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

Testing 304
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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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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

The data science and AI teams are able to explore and use new data sources as they become available through Amazon DataZone. Because Amazon DataZone integrates the data quality results, by subscribing to the data from Amazon DataZone, the teams can make sure that the data product meets consistent quality standards.

IoT 111
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Is the gen AI bubble due to burst? CIOs face rethink ahead

CIO Business Intelligence

Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts. CIOs should first launch internal projects with low public-facing exposure , which can mitigate risk and provide a controlled environment for experimentation.

ROI 143
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10 digital transformation roadblocks — and 5 tips for overcoming them

CIO Business Intelligence

Inadequate data management and governance Data is at the heart of digital transformation, and companies that don’t have adequate data management processes in place are likely to struggle. Ensuring data quality, privacy, and security is essential.