Actionable Agile Metrics for Predictability: An Introduction

Vacanti, Daniel S.

ISBN 10: 098643633X / ISBN 13: 9780986436338

Editore: Daniel S. Vacanti, Inc., 2015

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Codice articolo M0098643633X-G

Dati bibliografici

• Titolo: Actionable Agile Metrics for Predictability: An Introduction
• Editore: Daniel S. Vacanti, Inc.
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 098643633X
• ISBN 13: 9780986436338
• Rilegatura: Brossura
• Condizione: good

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Riassunto

“When will it be done?” That is probably the first question your customers ask you once you start working on something for them. Think about how many times you have been asked that question. How many times have you ever actually been right? We can debate all we want whether this is a fair question to ask given the tremendous amount of uncertainty in knowledge work, but the truth of the matter is that our customers are going to inquire about completion time whether we like it or not. Which means we need to come up with an accurate way to answer them. The problem is that the forecasting tools that we currently utilize have made us ill-equipped to provide accurate answers to reasonable customer questions. Until now. Topics Include

• Why managing for flow is the best strategy for predictability—including an introduction to Little’s Law and its implications for flow.
• A definition of the basic metrics of flow and how to properly visualize those metrics in analytics like Cumulative Flow Diagrams and Scatterplots.
• Why your process policies are the potentially the biggest reason that you are unpredictable.

Informazioni sull?autore

Daniel Vacanti has more than 20 years focusing on Lean and Agile practices. In 2007, he helped to develop the Kanban Method for knowledge work. He managed the world’s first project implementation of Kanban that year, and has been conducting Kanban training, coaching, and consulting ever since—including extensive work for several Fortune 100 companies. Most recently, Daniel founded ActionableAgile™ which provides industry leading predictive analytics tools and services to any flow-based process. Daniel holds a MBA and regularly teaches a class on lean principles for software management at the University of California Berkeley.