Optimizing Data-to-Learning-to-Action: The Modern Approach to Continuous Performance Improvement for Businesses
暫譯: 優化數據到學習再到行動:企業持續績效改進的現代方法
Steven Flinn
- 出版商: Apress
- 出版日期: 2018-04-07
- 售價: $1,270
- 貴賓價: 9.5 折 $1,207
- 語言: 英文
- 頁數: 216
- 裝訂: Paperback
- ISBN: 1484235304
- ISBN-13: 9781484235300
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相關主題
商品描述
Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization’s data-to-learning-to-action processes.
This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today’s business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector.
You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time.
In today’s dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value.
What You’ll Learn
- Understand data-to-learning-to-action processes and their fundamental elements
- Discover the highest leverage data-to-learning-to-action processes in your organization
- Identify the key decisions that are associated with a data-to-learning-to-action process
- Know why it’s NOT all about data, but it IS all about decisions and learning
- Determine the value upside of enhanced learning that can improve decisions
- Work backwards from the decisions to determine the value constraints in data-to-learning-to-action processes
- Evaluate people, process, and technology-based solution options to address the constraints
- Quantify the expected value of each of the solution options and prioritize accordingly
- Implement, measure, and continuously improve by addressing the next constraints on value
Business executives and managers seeking the next level of organizational performance, knowledge workers who want to maximize their impact, technology managers and practitioners who require a more effective means to prioritize technology options and deployments, technology providers who need a way to credibly quantify the value of their offerings, and consultants who are ready to build practices around the next big business performance paradigm
商品描述(中文翻譯)
應用一種強大的新方法,確保您的業務持續改進績效。您將學習如何確定和評估基於人員、流程和技術的解決方案,以優化您組織的數據到學習再到行動的流程。
本書詳細描述了如何全面優化從數據到學習,再到決策和行動的活動鏈,這對於在當今商業環境中實現卓越績效至關重要。本書結合了決策科學、約束理論和流程改進的見解,提供了一種清晰、有效的方法,幾乎可以應用於每一個商業功能和行業。
您將學習如何系統性地從決策回推到數據,估算整個鏈條的價值流,並識別不可避免的價值瓶頸。更重要的是,您將學習量化成功解決瓶頸所能獲得的價值的技術,提供可信的支持,以便在正確的地方和正確的時間做出適當的投資。
在當今動態的環境中,執行者必須考慮不斷湧現的新型顛覆性技術(例如,雲計算、物聯網、人工智慧/機器學習、商業智慧、企業社交等,以及隨之產生的大數據),作者史蒂文·弗林提供了做出有效決策所需的全面方法,並以可信的量化價值為基礎。
您將學到的內容:
- 理解數據到學習再到行動的流程及其基本要素
- 發現您組織中最高杠桿的數據到學習再到行動的流程
- 確定與數據到學習再到行動流程相關的關鍵決策
- 知道為什麼這不是全關於數據,而是全關於決策和學習
- 確定增強學習所帶來的價值上升,這可以改善決策
- 從決策回推以確定數據到學習再到行動流程中的價值約束
- 評估基於人員、流程和技術的解決方案選項以解決約束
- 量化每個解決方案選項的預期價值並相應地進行優先排序
- 實施、測量並通過解決下一個價值約束來持續改進
本書適合於:
尋求組織績效提升的商業高管和經理、希望最大化影響力的知識工作者、需要更有效手段來優先考慮技術選項和部署的技術經理和從業者、需要可信量化其產品價值的技術提供者,以及準備圍繞下一個商業績效範式建立實踐的顧問。