What managers really want from data are clear, foolproof, no-work solutions and the simple ability to predict the future. They want to know exactly what the demand for their current and proposed products will be; how and when prices for goods, services, stocks, and securities will rise and fall; what dangers and opportunities exist within their grasp; and how their decisions will affect their costs of doing business in the present, the near future, and the long term. As business operations become more complex and competitive, and as data volumes grow, companies are increasingly turning to tools like simulation and machine learning to provide these predictions. Machine learning, in particular, has become very popular, largely due to the continued development and impressive performance of deep-learning algorithms for neural networks. In addition, simulations of various sorts have been used for many years to test and develop operational system designs and operational policy decisions. Briefly describe the steps and information needed to create (1) a business simulation and (2) a deep-learning application. Compare and contrast these two creation processes, identifying the key similarities and differences in the way these different tools are built. On the basis of the differences and similarities you have identified, identify and analyze the most important benefits and risks associated with each of these tools and evaluate the relative usefulness of these tools if they were applied to your doctoral research or another business problem familiar to you

Understanding Business

12th Edition

ISBN:9781259929434

Author:William Nickels

Publisher:William Nickels

Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment

Section: Chapter Questions

Problem 1CE

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What managers really want from data are clear, foolproof, no-work solutions and the simple ability to predict the future. They want to know exactly what the demand for their current and proposed products will be; how and when prices for goods, services, stocks, and securities will rise and fall; what dangers and opportunities exist within their grasp; and how their decisions will affect their costs of doing business in the present, the near future, and the long term.

As business operations become more complex and competitive, and as data volumes grow, companies are increasingly turning to tools like simulation and machine learning to provide these predictions.

Machine learning, in particular, has become very popular, largely due to the continued development and impressive performance of deep-learning algorithms for neural networks. In addition, simulations of various sorts have been used for many years to test and develop operational system designs and operational policy decisions.

Briefly describe the steps and information needed to create (1) a business simulation and (2) a deep-learning application.
Compare and contrast these two creation processes, identifying the key similarities and differences in the way these different tools are built.
On the basis of the differences and similarities you have identified, identify and analyze the most important benefits and risks associated with each of these tools and evaluate the relative usefulness of these tools if they were applied to your doctoral research or another business problem familiar to you.

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