Optimizing Data-to-Learning-to-Action: The Modern Approach to Continuous Performance Improvement for Businesses

  • 3h 46m
  • Steven Flinn
  • Apress
  • 2018

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

Who This Book Is For

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

About the Author

Steven Flinn is founder and CEO of ManyWorlds, Inc., which is a pioneer of machine learning-based solutions for enterprises, the market leading provider of visual UX software for collaborative systems, and a provider of related advisory services to leading organizations around the world. Mr. Flinn has extensive consulting experience at the intersection of strategy, decision science, and technology with Global 1000 enterprises, as well as selected high-impact startups. He has been awarded over 40 patents in the field of machine learning and its applications, and is the author of The Learning Layer (Palgrave Macmillan 2010), which predicted, and established the imperative for, applying machine learning-based capabilities in the enterprise, an imperative that is now widely accepted and a reality. Prior to ManyWorlds, he was a Chief Information Officer and Vice President of Strategy at Royal Dutch Shell. His education includes graduate degrees from Northwestern University's Kellogg School of Business and Stanford University's School of Engineering.

In this Book

  • Case for Action
  • Roots of a New Approach
  • Data-to-Learning-to-Action
  • Tech Stuff and Where It Fits
  • Reversing the Flow: Decision-to-Data
  • Quantifying the Value
  • Total Value
  • Optimizing Learning Throughput
  • Patterns of Learning Constraints and Solutions
  • Organizing for Data-to-Learning-to-Action Success
  • Conclusion