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|a eng
|2 639-2
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|a Jones, Andrew.
|4 aut.
|e Auteur
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|a Data Quality in the Age of AI :
|b Building a foundation for AI strategy and data culture
|c Andrew Jones.
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|a Birmingham :
|b Packt Publishing.
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|a Paris :
|b Cyberlibris,
|c 2024.
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|a Couverture (https://static2.cyberlibris.com/books_upload/136pix/9781835088562.jpg).
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|a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement
|e Cyberlibris
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|a Unlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric cultureKey FeaturesGain a profound understanding of the interplay between data quality and AIExplore strategies to improve data quality with practical implementation and real-world resultsAcquire the skills to measure and evaluate data quality, empowering data-driven decisionsPurchase of the Kindle book includes a free PDF eBookBook DescriptionAs organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What you will learnDiscover actionable steps to establish data quality as the foundation of your data cultureEnhance data quality directly at its source with effective strategies and best practicesElevate data quality standards and enhance data literacy within your organizationIdentify and measure data quality within the datasetAdopt a product mindset to address data quality challengesExplore emerging architectural patterns like data mesh and data contractsAssign roles, responsibilities, and incentives for data generatorsGain insights from real-world case studiesWho this book is forThis report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements
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