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Über dieses Buch

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

Inhaltsverzeichnis

Frontmatter

Chapter 1. Case for Action

Abstract
Or more precisely, this chapter is all about the case for data-to-learning-to-action! We touched on “Why Now” in the introduction—here, we will take a much deeper dive into that subject. A fundamental maxim of change management is that organizations will not take a new direction or take on a new approach unless there is genuine dissatisfaction within the organization with the status quo situation. This chapter provides plenty of reasons why there is cause for concern about the current state and should help to get the optimizing data-to-learning-to-action approach off the ground in your organization.
Steven Flinn

Chapter 2. Roots of a New Approach

Abstract
New ideas are invariably rooted in prior concepts—very often combinations of prior concepts. And for synergistic combinations to be developed, the prior concepts often first need to be generalized or extended so that they can be flexibly recombined into something for which the whole is greater than the sum of the parts. So it is with the optimizing data-to-learning-to-action approach. In its case, it is primarily rooted in three fields of management science
Steven Flinn

Chapter 3. Data-to-Learning-to-Action

Abstract
Now that we have defined the basics of the data-to-learning-to-action process, it’s time to dive into the details of its intermediate steps. And while birds do it, we do it, and even educated machines can now do it, our focus, of course, will be on the elements of this universal process that specifically pertain to its execution within organizations.
Steven Flinn

Chapter 4. Tech Stuff and Where It Fits

Abstract
I have emphasized and will continue to emphasize that optimizing data-to-learning-to-action is certainly not just about technology—it’s about people, process, and technology. Nevertheless, technology is a massive investment area for any organization. Its rapid evolution is so highly dynamic that technology necessarily presents continuing decisions for any organization, and it can disrupt entire business models in addition to individual processes. It therefore necessarily demands thorough and continuing attention.
Steven Flinn

Chapter 5. Reversing the Flow: Decision-to-Data

Abstract
We also know that since learning can be thought of as a flow, the application of our theory of constraints-based thinking implies that there will inevitably be bottlenecks that constrain the learning flow. More importantly, there will be constraints on the value of the learning throughput of the data-to-learning-to-action process. So, alleviating the constraints on the throughput of data-to-learning-to-action processes is clearly the prescription for improving business performance.
Steven Flinn

Chapter 6. Quantifying the Value

Abstract
In the last chapter, we reviewed some examples of working backward from decisions to understand the value of addressing bottlenecks on actionable learning. For instance, for the pricing example, we needed to understand how much more profit would be expected to be attained if we could better estimate competitors’ bids. And, more particularly, we wanted to know how much more profit would be expected for various levels of accuracy in predicting competitors’ bids. With that information in hand, we could then work backward in our straightforward way to identify the constraints that contributed to the current state of less-than-perfect-predictability of competitors’ bids and determine what it would be worth to resolve those constraints, starting with the constraint that was identified to be the current most-limiting factor in the data-to-learning-to-action chain.
Steven Flinn

Chapter 7. Total Value

Abstract
In the last chapter, we reviewed how the expected value of the learning that influences a decision in a data-to-learning-to-action process can be quantified. This chapter will introduce the concept of total value, which includes learning value, and describes how optimizing expected total value across an organization’s portfolio of investment opportunities is the path to optimizing the organization’s long-term performance.
Steven Flinn

Chapter 8. Optimizing Learning Throughput

Abstract
We have now covered the basic concepts required to optimize a data-to-learning-to-action process, but we still have a few things to consider before our method is complete. Let’s recap what we have already covered before we turn to those finishing touches
Steven Flinn

Chapter 9. Patterns of Learning Constraints and Solutions

Abstract
We now have all the tools in place to optimize one or more data-to-learning-to-action processes and, more specifically, to maximize the value of learning that is associated with data-to-learning-to-action processes. So, in this chapter, we’ll work through some common patterns of learning constraints and potential solutions that are applicable to a wide variety of organizations and functional areas. The solutions invariably rely on people-, process-, or technology-based capabilities and, most typically, combinations of the three. We’ll work through these examples by traversing backward along the chain from the targeted decision, which is the sequence that is most appropriate to be applied in any real-world setting. And, of course, that targeted decision should be one that has been determined to have significant leverage on a value driver for the organization. We will spend time on each element of the data-to-learning-to-action chain and discuss some of the common constraints that are associated with each element and some typical potential solutions to these constraints. These examples will hopefully resonate with some of the data-to-learning-to-action processes in your own organization and help jumpstart your analysis.
Steven Flinn

Chapter 10. Organizing for Data-to-Learning-to-Action Success

Abstract
We’ve covered a lot of material up to this point, and some of it was perhaps new in concept and maybe even took a couple of passes to fully absorb.
Steven Flinn

Chapter 11. Conclusion

Abstract
We began our optimizing data-to-learning-to-action journey by considering the defining characteristics of this era of business, the unprecedented advances across such a broad front of technologies, and the resulting complexity for business and IT strategies. And, at the same time, we looked at the sobering long-term trends of business-performance metrics such as returns on assets and corporate topple rates.
Steven Flinn

Backmatter

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