If only one term had to be used to describe the competitive business environment, it would be cutthroat. No matter what kind of industry or its size, every company is constantly trying hard to get a competitive advantage over its competitor. This is always happened in any small or big scale companies to one-up each other. One way an organization can attain an edge over its competition is by making decisions that have an increased positive impact and reduced risk to attain their goals.
Making the proper decision on any difficult task to selecting the best among alternatives can be hard. This is amplified in business when any decision could lead to a great success or a massive failure. Not having nor being able to understand a key piece of information could easily affect the case for selecting one decision path. Not too long ago, tough business decisions were made by long-time industry experts who had intimate knowledge of the business. These decisions were largely made on past historical or financial situations and rarely took into account data models. This led to high levels of failure, and some successful decisions could be attributed more to luck than effective decision-making techniques.
Processes for making decisions started to involve computers in the ’60s and ’70s. As the computer revolution started making its way from academia and government projects to mainstream businesses, people started leveraging computers to do continuous number crunching. Computers could process more data, and this eliminated some of the human error factors involved with complex statistics. This is where computers have an empirical advantage over humans, as they are tailored for mathematical computations and can be harnessed to run almost 24 hours per day. However, even enterprise-level computers in those days were not even close to the power of what we are used to today. Most of them couldn’t do much more than today’s programmable scientific calculator. The early horsepower of computer systems had to be specifically tailored for basic mathematical computations on data, as anything complex as artificial intelligence (AI) was completely out of the question.
Organizations quickly saw the benefit of having computer systems aid them in their everyday business processes. Even though the early computers weren’t that powerful, they could be used to garner vast amounts of data and perform complex business algorithms on it. The resultant data could then be used in the boardroom to shape corporate strategies via actionable decisions from executive information systems (EISs), group decision support systems (GDSSs), organizational decision support systems (ODSSs), and so on.
Making the proper decision on any difficult task to selecting the best among alternatives can be hard. This is amplified in business when any decision could lead to a great success or a massive failure. Not having nor being able to understand a key piece of information could easily affect the case for selecting one decision path. Not too long ago, tough business decisions were made by long-time industry experts who had intimate knowledge of the business. These decisions were largely made on past historical or financial situations and rarely took into account data models. This led to high levels of failure, and some successful decisions could be attributed more to luck than effective decision-making techniques.
Processes for making decisions started to involve computers in the ’60s and ’70s. As the computer revolution started making its way from academia and government projects to mainstream businesses, people started leveraging computers to do continuous number crunching. Computers could process more data, and this eliminated some of the human error factors involved with complex statistics. This is where computers have an empirical advantage over humans, as they are tailored for mathematical computations and can be harnessed to run almost 24 hours per day. However, even enterprise-level computers in those days were not even close to the power of what we are used to today. Most of them couldn’t do much more than today’s programmable scientific calculator. The early horsepower of computer systems had to be specifically tailored for basic mathematical computations on data, as anything complex as artificial intelligence (AI) was completely out of the question.
Organizations quickly saw the benefit of having computer systems aid them in their everyday business processes. Even though the early computers weren’t that powerful, they could be used to garner vast amounts of data and perform complex business algorithms on it. The resultant data could then be used in the boardroom to shape corporate strategies via actionable decisions from executive information systems (EISs), group decision support systems (GDSSs), organizational decision support systems (ODSSs), and so on.