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The Science of Dream Teams /
From the CEO of The Predictive Index, the leader in talent optimization, comes Moneyball for HR and people management How do you find the perfect person for the job in a stack of hundreds of resumes? Why do some teams succeed while others-made up of equally talented people-stumble? If the quality of...
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| Formato: | Electrónico Audiom |
| Idioma: | Inglés |
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McGraw Hill-Ascent Audio,
2021.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 300 | |a 1 online resource (20412 pages) | ||
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| 520 | |a From the CEO of The Predictive Index, the leader in talent optimization, comes Moneyball for HR and people management How do you find the perfect person for the job in a stack of hundreds of resumes? Why do some teams succeed while others-made up of equally talented people-stumble? If the quality of your workforce is your company's most important asset, then why are your managers still basing these critical decisions primarily on interviews and their gut instincts? In The Science of Dream Teams, Mike Zani details a data-driven approach to talent optimization that makes hiring, motivating, and managing people more efficient and effective than ever. It employs sophisticated assessments, tools, and software that enable leaders to: measure the traits and characteristics that predict success in a role or fit on a team; build finely tuned project teams and well-balanced leadership teams; boost employee productivity, engagement, retention-and happiness; and unlock the hidden potential of individual workers and your organization as a whole. Whatever the business or industry, this game-changing approach has the power to unlock an unbeatable competitive advantage. The Science of Dream Teams will show you how. | ||
| 542 | |f Copyright © McGraw Hill-Ascent Audio | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | 0 | |a Online resource; Title from title page (viewed August 10, 2021). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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