Data processing, in general, is "the harnessing of computer processing power to make the processing of things of particular interest more effective." Processing power refers to the ability of a computer to handle large amounts of data. It is the ability to process this large amount of data quickly and efficiently. Data processing, therefore, is the process by which information is transformed into useful information. It is also the procedure whereby this transformation is captured, maintained, analyzed and passed on to someone else who will use it, see this site for more info.
Data processing involves a wide range of activities. One of these activities is transaction processing. Transaction processing occurs when a user requests a service or product from a company. The request is an electronic transaction that is then transmitted from the requesting party (the seller) to the buying party (the buyer).
A number of elements are involved in the transfer of data through a transaction processing system. First, the requestor's computer system must be able to generate a request. Next, the computer system must be able to analyze the request in a way that satisfies certain criteria. Request criteria are constraints on what the data processing system believes the data to be. They include such things as whether or not the data satisfy certain criteria associated with an order or even what time limit the buyer has for responding.
Another type of data processing involves what is called stream processing. In stream processing, a program is divided into a series of smaller jobs that are executed in order. Each of these jobs may be entirely separate from each other or they may be part of a series of jobs that together make up a single transaction. For example, some systems divide a job into an "iteration" and store the results in memory so that the user can retrieve results for that particular iteration. The results of one iteration are then used to produce the next iteration.
Most companies that employ data processing technologies are in need of methods that can collect usable information from a distributed environment. Traditional computers used to process this sort of data are very susceptible to errors and so programmers often prefer to use computers that process raw data as opposed to processed information. Computer scientists who specialize in this field are constantly working on ways to make the various stages of processing faster and less error prone. They are also constantly trying to figure out new ways to make the different stages of processing more efficient, see page for more insights.
There are many developments in the area of machine learning and data processing that are currently being utilized by businesses all over the world. Machine learning makes it possible to evaluate an entire process from the initial input data without having to actually observe the entire process. A data scientist can write a complex algorithm that will give the computer enough information to decide whether or not the algorithm is correct. This type of machine learning can be used to help a business to cut costs by avoiding unnecessary trips to the customer service desk or the possibility of losing sales because the computer incorrectly interpreted the raw data and gave the wrong results.