Overview of Computational Intelligence in Business Analytics: Concepts, Methods, and Tools for Big Data Applications
- 1.1 Introduction
- 1.2 A Need for Computational Intelligence in Business Analytics
- 1.3. Differentiating Your Business Through Computational Intelligence
1.3. Differentiating Your Business Through Computational Intelligence
This book aims to provide a thorough introduction to the main issues associated with the design and implementation of computational intelligence tools that could add value to an organization.
In general, computer systems are not good at knowing what to do: every action the system performs must be explicitly anticipated, planned for, and coded by the programmers. If a computer system encounters a situation that its programmers did not anticipate, then the situation usually results in a system crash. For the most part, business managers accept that computers satisfy a purely computing (number crunching) role. For many applications (such as payroll processing), it is entirely acceptable. However, for an increasingly large number of business processes, managers require computer systems that could decide for themselves what they need to do in order to satisfy both their design objectives and tackle a given business problem. Such computer systems utilize computational intelligence tools. They must operate robustly in rapidly changing and often unpredictable environments. Thus, to some extent, these computer systems are anthropomorphic and utilize the power of computational intelligence techniques, such as fuzzy sets, artificial neural networks, and genetic algorithms.
The techniques are outlined in the book, and examples for suggested implementations are provided. It should be noted that these approaches are much more adequate for dealing with multiple data (both structured and unstructured) as well as with uncertainty and the complexity of today’s organizations than the statistical analysis and tools most commonly used. Computational intelligence tools provide actionable insights for decision making in addition to their capability to explain the past.
For example, these tools (fuzzy sets, artificial neural networks, and genetic algorithms) could address problems and tackle tasks of product mechanical design within a framework of integrated design. The mechanical design process is usually divided in several sub-problems from engineering and programming points of view. The fuzzy sets approach allows for the comparison of different stake-holders’ points of view to one another and the final solution through a global compromise. This approach allows the mechanical design to be distributed in parallel tasks. The genetic algorithms and artificial neural networks tools could be used in order to encapsulate the data and analysis of each engineering design point of view. So, they allow for multiple constraints (factors of materials selection, reliability, performance, safety, and environmental impacts) to be incorporated into the mechanical design. Genetic algorithms and artificial neural networks also allow for exploration of new mechanical design solutions, thus fostering innovation.
Even more autonomous behavior of computer systems utilizing computational intelligence tools is expected in scientific applications. For example, when a space probe makes its long flight from Earth to outer planets in the solar system and beyond, a large ground crew of scientists is usually required to continually track its progress and decide how to deal with unexpected eventualities. This is not practical and very costly. For these reasons, organizations like the National Aeronautics and Space Administration (NASA) have been experimenting with making space probes more autonomous—giving them richer decision making capabilities and responsibilities, in part by utilizing computational intelligence tools.
Computational intelligence poses both a challenge and an opportunity for many businesses and organizations, and data scientists and researchers are at the forefront of learning how to leverage these changes for business impact.
I hope you enjoy learning about computational intelligence and its tools and how the industry is adapting to today’s environment.