What Is Garbage In, Garbage Out (GIGO)?
Garbage In, Garbage Out isn't just a catchy saying. It's the reality of using computers. Junk in, garbage out! So make sure you provide good input and receive the results you expect. Garbage In, Garbage Out isn't just a catchy saying. It's the reality of using computers. Junk in, garbage out! So make sure you provide good input and receive the results you expect. Garbage in, garbage out (GIGO) is a common expression that describes the notion that bad or inaccurate data results in harmful or erroneous information. In information technology, the term is primarily applied to computer programs where the output quality depends entirely on the quality of input. In computer programming, GIGO describes a situation where data is input into a program but is not formatted correctly, is out of range, or is simply incorrect, thus causing the program to produce false results. The concept of "garbage in, garbage out" is applied to most types of decision-making. The term is most common in data science, where inadequate or untested data can lead to wrong or even harmful conclusions. Garbage can take many forms, including inputting false or inaccurate information, collecting data that is irrelevant to the decision-making process, or not considering existing necessary information for a good decision. It can also occur when the decision-making process itself is flawed or biased. Be careful with your input, and make sure it's correct. When you're putting data into a computer program, you don't want to mess it up and cause the program to spit out incorrect results. You can avoid this by testing your data to ensure accuracy and reliability before putting it into the program. For example, let's say you're creating a program for inventory control. You're taking inventory and putting it into the program, and the program is supposed to tell you how much of each item you have on hand. The program will work correctly only if the data you put into it is correct. The program will likely spit out incorrect ones if you're putting in inaccurate inventory numbers. Suppose you must put in correct inventory numbers or correspond to real-world situations. In that case, the program is expected to spit out inventory numbers that don't conform to real-world problems. If your data is insufficient, then no fancy formulas, interpolations, or algorithms will give you anything other than garbage.
Related Terms by Data Management
Join Our Newsletter
Get weekly news, engaging articles, and career tips-all free!
By subscribing to our newsletter, you're cool with our terms and conditions and agree to our Privacy Policy.