Data Crunching is a method in information science which makes the preparation of automated processing of large amounts of data and information (Big Data) possible. Data Crunching consists of preparing and modelling a system or application that is used: The data is processed, sorted and structured to run algorithms and program sequences on it. The term crunched data therefore refers to data that has already been imported and processed in a system. Similar terms include data munging and data wrangling - these refer more to manual or semi-automatic processing of data, which is why they are significantly different to data crunching.
The ultimate goal of data processing is deeper insight into the matter that should be conveyed with the data, such as in the field of business intelligence, so that informed decisions can be made. Other areas where data crunching applies are medicine, physics, chemistry, biology, finance, criminology, or web analytics. Depending on the context, different programming languages and tools are used: While Excel, Batch and Shell programming were used earlier; languages like Java, Python or Ruby are preferred today.
Data crunching, however, does not refer to exploratory analysis or the visualization of data – that is done by special programs which are tailored to their area of application. Data crunching is more about correct processing, so that a system can do something with the records and the data format. Data crunching is therefore an upstream process of data analysis. This process, as the data analysis itself, can be iterative when the output of the crunching process includes new data or errors. This means that the program sequences may be repeated until the desired result is achieved: an accurate, correct data set that can be further processed directly or imported and does not contain any errors or bugs.
Most data crunching tasks can be simplified into three steps. First, the raw data is read in order to convert it into a selected format as the next step. Finally, the data is output in the correct format, so it can be further processed or analyzed. This trichotomy has the advantage that the individual data (input, output) can also be used for other scenarios.
Some applications of data crunching are:
As a rule, a lot of time can be saved with data crunching because the processes do not need to be performed manually. Therefore, particularly with large data sets and relational databases, data crunching can be a significant advantage. However, appropriate infrastructure is necessary to have the computing power for such operations. A system like Hadoop, for example, distributes the computer load across multiple resources and performs arithmetic processes on computer clusters. It uses the principle of division of labor.
Problems in the areas of online marketing, web design, and web analytics can often be solved with data crunching. Large online shops rely on these effective methods. For example, if 10,000 records from a relational database are supposed to be automatically converted to a different format so that relevant products from the frontend can be displayed, data crunching is the method of choice. Especially in the face of Big Data, crunching of large amounts of data is of central importance. The more data that must be processed, the more time can be saved with data crunching.