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2021 | Buch

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Predictive Intelligence for Data-Driven Managers

Process Model, Assessment-Tool, IT-Blueprint, Competence Model and Case Studies

verfasst von: Dr. Uwe Seebacher

Verlag: Springer International Publishing

Buchreihe : Future of Business and Finance

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book describes how companies can easily and pragmatically set up and realize the path to a data-driven enterprise, especially in the marketing practice, without external support and additional investments. Using a predictive intelligence (PI) ecosystem, the book first introduces and explains the most important concepts and terminology. The PI maturity model then describes the phases in which you can build a PI ecosystem in your company. The book also demonstrates a PI self-test which helps managers identify the initial steps. In addition, a blueprint for a PI tech stack is defined for the first time, showing how IT can best support the topic. Finally, the PI competency model summarizes all elements into an action model for the company. The entire book is underpinned with practical examples, and case studies show how predictive intelligence, in the spirit of data-driven management, can be used profitably in the short, medium, and long terms.

Inhaltsverzeichnis

Frontmatter

1. Predictive Intelligence and the Basic Economic Principles

Abstract

This chapter outlines why the topic of data-driven business management and predictive intelligence is of crucial importance in the coming years. Companies and their managers who do not recognize the signs of the times and are not already now starting to build their own predictive intelligence will inevitably be left behind. Why this is the case will be discussed in this chapter.

Uwe Seebacher

2. Predictive Intelligence at a Glance

Abstract

This chapter provides a compact overview and an introductory presentation of the term Predictive Intelligence. It is described how Predictive Intelligence can be established in an organization step by step. In order to be able to analyze the respective initial situation in their organization, the maturity model to Predictive Intelligence is described just like the analysis instrument. At the end of the section, the advantages of Predictive Intelligence (PI) and, above all, the sustainable effects are discussed in a compact form.

Uwe Seebacher

3. The Predictive Intelligence Ecosystem

Abstract

In this section, the most important terms in the context of Predictive Intelligence are listed and described in a way that is easy to understand for everyone. The content area is subject to enormous dynamics, so this overview must be considered a snapshot. An attempt will be made to focus on already established and widely used terms and not to list those that are used selectively from the perspective of a generally valid overview.

Uwe Seebacher

4. The Predictive Intelligence Maturity Model

Abstract

This chapter describes the development stages of Predictive Intelligence in organizations. This model forms the basis for the implementation process of Predictive Intelligence in any form of organization. The model was developed based on research work in the area of organization etymology in combination with experiences from different projects in the context of Predictive Intelligence. The Predictive Intelligence Assessment (PIA) presented in the following chapter was also developed on the basis of the Predictive Intelligence maturity model. It can be used initially to evaluate the status quo in an organization, but also the current state of development of Predictive Intelligence (PI). In this way, the PI maturity model, the PI assessment, and the PI procedure model provide a coherent set of instruments for every organization to efficiently and effectively develop toward a data-driven enterprise.

Uwe Seebacher

5. The Predictive Intelligence Self-Assessment

Abstract

In this chapter, the test procedure is described and discussed, on the basis which in a short time both the potential regarding data-driven management and/or Predictive Intelligence and the respective initial situation in an organization can be assessed and determined in form of a percentage value. The Predictive Intelligence Self-Assessment (PI-SA) is based on the previously described maturity model for predictive intelligence (PIMM) and maps all required dimensions accordingly.

Uwe Seebacher

6. The Process Model for Predictive Intelligence

Abstract

In this chapter the individual steps are described, how an organization can develop toward a data-based management and lift these in further consequence on the level of the predictive intelligence. The process model for Predictive Intelligence is based on the maturity model for Predictive Intelligence already described in this book as well as the Self-Assessment for Predictive Intelligence, which is why these two instruments can also be used continuously as a frame of reference and orientation during the entire development process.

The process model presented here is based on various projects already realized in practice in various organizations for the conception and implementation of Predictive Intelligence. Against this background, it can be certified that neither a separate budget nor additional resources are generally required for the first stages of implementation or application of this process model. Conscious use of organizational resources deepens the awareness of the importance of Predictive Intelligence and automatically ensures an implementation speed that is appropriate for the organization.

Uwe Seebacher

7. The Predictive Intelligence TechStack (PITechStack)

Abstract

In the long term, data-driven corporate management is dependent on a corresponding Predictive Intelligence TechStack (PITechStack), because only with a well-thought through and integrated IT infrastructure can a multidimensional, interactive PI system work with big data and artificial intelligence with high performance. In this chapter, the current status but also trends with regard to the PITechStack are therefore critically analyzed and discussed in order to give the reader an overview but also an assistance, which things must be considered, and which products and solutions are relevant for a successful PITechStack.

Uwe Seebacher

8. The Predictive Intelligence Team

Abstract

In this chapter, we will take a look at the human resources that are necessary and relevant in the context of predictive intelligence. Different areas of competence are discussed and based on these, different possible roles and their responsibilities are derived and explained. In this way, a comprehensive picture of future, new areas of knowledge, and competence are to be drawn so that managers can also ensure and implement sustainable strategic PI workforce management in the course of PI activities. Given the fact that the entire training sector is currently engaged in developing new curricula and courses specifically for these new requirements, first movers must proactively take the HR scepter into their own hands in order to develop the necessary competencies within their own organization in a forward-looking manner.

Uwe Seebacher

9. The Predictive Intelligence Case Studies

Abstract

In the following section, three different case studies on predictive intelligence are described as examples. These three case studies were selected with the background to be able to represent all temporal dimensions of Predictive Intelligence in the most compact form possible. The first case study describes how Predictive Intelligence can be used to optimize short-term inventories and thus the net working capital of an organization. The second case study shows how precisely business expansion can be designed, planned, and quantified with Predictive Intelligence, and subsequently monitored as part of the continuous process of data-based corporate management. The third case study illustrates the long-term strategic perspective of Predictive Intelligence and concludes by presenting the application forms of modern predictive intelligence.

Uwe Seebacher

10. Why It Remains Exciting…

Abstract

This chapter summarizes the first book on this new field of predictive intelligence and outlines the next few years, but also everything that goes with it. Predictive Intelligence will change and reshape business management practice. With Predictive Intelligence a completely new dimension of management quality can be achieved and realized. But, as is so often the case, far-sighted managers are needed, because the course for predictive management is already being set by the pioneers and thought leaders.

Uwe Seebacher

Backmatter

Titel: Predictive Intelligence for Data-Driven Managers
verfasst von: Dr. Uwe Seebacher
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-69403-6
Print ISBN: 978-3-030-69402-9
DOI: https://doi.org/10.1007/978-3-030-69403-6

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Inhaltsverzeichnis

Frontmatter

1. Predictive Intelligence and the Basic Economic Principles

2. Predictive Intelligence at a Glance

3. The Predictive Intelligence Ecosystem

4. The Predictive Intelligence Maturity Model

5. The Predictive Intelligence Self-Assessment

6. The Process Model for Predictive Intelligence

7. The Predictive Intelligence TechStack (PITechStack)

8. The Predictive Intelligence Team

9. The Predictive Intelligence Case Studies

10. Why It Remains Exciting…

Backmatter

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