Creating Smart Enterprises

Leveraging Cloud, Big Data, Web, Social Media, Mobile and IoT Technologies

Vivek Kale

• Publisher: Auerbach Publications
• Year: 2017
• ISBN:
  9781351648493
• Edition: 1
• Topic: Business
• Subtopic: Management
• Index: Business

"Vivek Kale's Creating Smart Enterprises goes smack-dab at the heart of harnessing technology for competing in today's chaotic digital era. Actually, for him, it's SMACT-dab: SMACT (Social media, Mobile, Analytics and big data, Cloud computing, and internet of Things) technologies. This book is required reading for those that want to stay relevant and win, and optional for those that don't."

—Peter Fingar, Author of Cognitive Computing and business technology consultant

Creating Smart Enterprises unravels the mystery of social media, mobile, analytics and big data, cloud, and Internet of Things (SMACT) computing and explains how it can transform the operating context of business enterprises. It provides a clear understanding of what SMACT really means, what it can do for smart enterprises, and application areas where it is practical to use them.

All IT professionals who are involved with any aspect of a SMACT computing project will profit by using this book as a roadmap to make a more meaningful contribution to the success of their computing initiatives.

This pragmatic book:

• Introduces the VUCA (volatility, uncertainty, complexity, and ambiguity) business ecosystem confronted by the businesses today.
• Describes the challenges of defining business and IT strategies and of aligning them as well as their impact on enterprise governance.
• Provides a very wide treatment of the various components of SMACT computing, including the Internet of Things (IoT) and its constituting technologies like RFID, wireless networks, sensors, and wireless sensor networks (WSNs).

This book addresses the key differentiator of SMACT computing environments and solutions that combine the power of an elastic infrastructure with analytics. The SMACT environment is cloud-based and inherently mobile. Information management processes can analyze and discern recurring patterns in colossal pools of operational and transactional data. Analytics, big data, and IoT computing leverage and transform these data patterns to help create successful, smart enterprises.

Chapter 1

VUCA Ecosystem

Agility is the ability to respond to (and ideally benefit from) unexpected change. We must distinguish between agility and flexibility: flexibility is scheduled or planned adaptation to unforeseen yet expected external circumstances, while agility is unplanned and unscheduled adaptation to unforeseen and unexpected external circumstances. The enterprises that can best respond to the fast and frequently changing markets will have better competitive advantages than those that fail to sustain the pace dictated by the process of globalization. And this can be realized through enterprises acquiring better control and efficiency in their ability to manage the changes in their enterprise processes.

1.1VUCA Ecosystem

VUCA stands for Volatility, Uncertainty, Complexity, and Ambiguity. It describes a situation confronted in the digital economy:

1.Volatility: The term volatility is commonly used in statistics and financial theory. Volatility can be defined as a statistical measure, describing the amount of uncertainty about the size of changes. In statistics, it can be quantified by the standard deviation or variance. Real-life examples are increasing price fluctuations on global raw material markets or stock markets. You can see high volatility as significant jumps of values over time, which can be seen as an indicator of increasing pace of the environment.

Volatility can be understood as an observable output of a complex system that cannot be easily interpreted any more. While complex systems that are in an equilibrium or are oscillating between two or three different equilibria are easy to interpret, a system that runs in so-called deterministic chaos has no obvious pattern to be easily observed.

2.Uncertainty: With increased volatility of the environment, it is increasingly hard to predict the future. While in the past, statistical regression models were able to predict the future, today it is becoming more and more difficult to extrapolate future developments and link them with a probability distribution. Uncertainty can also be described as a lack of clarity to evaluate a situation properly to identify challenges and opportunities.

Uncertainty is a problem that might arise in decision making if there is incomplete information, inadequate understanding of available information or equally attractive options. Across time, there develops an increasing belief in the ability to cope with uncertainty and even to overcome uncertainty through planning and control. Decisions in business are often made based on the assumption that with enough research, collection of information and preparation for decision making, uncertainty can be avoided completely. But this is not the case, and in highly dynamic environments, the speed of change in the context is higher than the speed of learning. In such a situation, it is customary to build up a certain mutually acceptable perception that is used as a reference by all concerned. This helps in reducing the impact of uncertainty to a certain amount, and helps the people to achieve some sense of assurance, security, and stability. But this doesn’t mean that it reflects the real-world situation—there is an irreducible gap.

3.Complexity: In an interconnected and networked environment, it becomes more and more difficult to connect cause and effect. The idea of linear causality hits the limits. Complexity can be defined as a situation where interconnectedness of parts and variables is so high that the same external conditions and inputs can lead to very different outputs or reactions of the system. Real-life examples are organizations or even more complex inter-organizational alliance networks where the same inputs can cause very different outputs at different points in time.

From a systems perspective, complexity can be understood as a specific property, defined as the result of the amount of system elements, their relationships, and the dynamics between the elements and the relationships. The more states a system can take, the higher the variety of the system. The variety can be then used as a measure of complexity. In computer science, for example, the algorithmic complexity is measured by the size of the shortest possible computer program that can solve the problem or at least completely describe the problem. Generally speaking, complexity has two aspects:

a.Complex structure is given by the high number of elements that are linked to each other in a non-trivial, non-linear way. In contrast, complicated structures are only characterized by a high amount of system elements, and they are missing these intense internal structures of various relationships and dynamics between the elements.

b.Complex behavior is characterized mainly by emergence, which can be described thus: “the action of the whole is more than the sum of the actions of the parts” (Holland). If something contains many interacting objects whose behavior is affected by memory or “feedback,” the interaction becomes non-linear.

Complexity is also closely linked to organization, decomposability, and nestedness of systems.

4.Ambiguity: Ambiguity is characterized by the fact that causal relationships are completely unclear, and the meaning or interpretation of a situation cannot be definitely resolved according to a rule or process consisting of a finite number of steps. In contrast to vagueness, which characterizes a situation by a lack of clarity, in ambiguity, specific and distinct interpretations are permitted. In real life, business decisions become more and more ambiguous, as there is often more than one possible solution to a problem and there is no analytical process to decide which option should be chosen. If one asks different people for an evaluation of a specific situation and plans for action, one would get different answers that would be equally valid.

Organizations cannot reduce the environment’s “degree of VUCA,” but a company’s capability to deal with VUCA can increase. In times of high dynamics and high interconnectedness, traditional simple mind models and decision-making rules and heuristics are no longer effective. The traditional mechanistic worldview worked well in times when the complex environment stayed in “pockets of order”; it was supported by the human need for simple, rational cause-and-effect mind models to be able to make decisions and to easily deal with the environment. Today, the environment is often in a state at the “edge of chaos” or in a “deterministic state of chaos.” In these situations, not only there is a need for different models and approaches in cognition, judgment, and action in management, but performance on all of these aspects can also be enhanced tremendously by augmenting with technology.

1.2Four Industrial Generations

In the earliest stages of industrialization, the focus of the technological revolution was on mechanization and the use of water and steam power. Mechanized production that had previously been organized mainly along craft lines led to larger organizational units and a significantly higher output of goods.

The second industrial revolution was characterized by the assembly line and electrification of factories.

The third industrial revolution brought with it a further automation of production by means of microelectronics and computerization.

The fourth industrial revolution is characterized by the essential digitization of the processes of production and delivery.

1.3Trends

1.3.1Social Trends

1.Sharing and Prosuming: The digital transformation blurs the traditional societal categories of production and c...