29 Mar '17

A large number of people who are not technically sound and good at the use of modern technology believe data processing as a process of changing raw data to meaningful information. To some extent this definition is OK. When data processing is understood through data manipulation, it is seen as a way to induce positive outcomes that resolve the problem and improve an existing situation. Moreover, data processing follows a cycle where a process (computer system or software) feed inputs (raw data) for the production of output (information and insights). Just go through these aspects to decide what data processing actually means:

  • It’s an act of data manipulation for having some desired results.
  • It gathers facts and figures and transmits them in a network for the future retention.
  • It is a sequence of activities and actions to change raw data into some meaningful information.
  • It collects, manipulates, and arranges data to achieve some common goals.
  • It uses several data manipulation techniques—Classification, searching, sorting, summarizing, comparison, and calculation.

Data Processing Cycle

Data management is about information extraction from raw data through a process called data mining. Data processing is a great way to churn the best out of existing data for having desired results in an effective manner. It is quite clear that there are three board stages in data processing but it also has some sub stages that deal with data collection, processing method selection, data management practices, and using data for availing desired purpose. Let’s have a look at some prime steps involved in data processing cycle:

  1. Data collection: It is primary a stage of the data processing cycle indicated to data collected which is both defined and accurate for taking valid decisions. It offers baseline to measure and target the level of improvement. Some types of data collection include census, sample survey, and administrative by-product.
  2. Data preparation: This stage ensures data manipulation for further analysis and processing. As raw data is hard to process, you need to check accuracy. Dataset construction from data sources is needed for further exploration and processing. Data analysis is needed for careful screening of problems.
  3. Data input: It is concerned with coding of data verification and converting the same for machine reading. The time-consuming process of data entry needs speed and accuracy along with a formal syntax. This stage is normally outsourced due to the factors of rising costs.
  4. Data processing: It belongs to the methods of data manipulation with multiple threads of execution of instructions as per operating system.
  5. Data output and interpretation: In this stage, information is transmitted to users in different report formats. Here, you need to interpret data for gaining useful information for future decisions.
  6. Data storage: This is the last stage where quick access and data retrieval are allowed for the next stage through the usage of system and application software.

Be it manual, mechanical, or electronic data processing, the conversion of data from any form to a desired form is made possible only with these 6 stages of data processing cycle.