Understanding Data Processing Methods
Various data processing methods are used to converts raw data to meaningful information through a process. Data is manipulated to produce results that lead to a resolution of a problem or improvement of an existing situation. Similar to a production process, it follows a cycle where inputs (raw data) are fed to a process (computer systems, software, etc.) to produce output (information and insights).
Generally, organizations employ computer systems to carry out a series of operations on the data to present, interpret, or obtain information. The process includes activities like data entry, summary, calculation, storage, etc. A useful and informative output is presented in various appropriate forms such as diagrams, reports, graphics, etc.
Need of different data processing methods
Data processing is important in business and scientific operations. Business data is repeatedly processed, and usually needs large volumes of output. Scientific data requires numerous computations and usually needs fast-generating outputs.
Methods of Data processing and data processing techniques
There are number of methods of data processing. Based on the data processing system and the requirement of the project, suitable data processing methods can be used. Three methods of data processing have been presented below:
- Manual Data Processing: In manual data processing, data is processed manually without using any machine or tool to get the required results. In manual data processing, all the calculations and logical operations are performed manually on the data. Similarly, data is transferred manually from one place to another. This method of data processing is very slow, and errors may occur in the output. Mostly, is processed manually in many small business firms as well as government offices & institutions. In an educational institute, for example, marks sheets, fee receipts, and other financial calculations (or transactions) are performed by hand. This method is avoided as far as possible because of the very high probability of error, labor intensive and very time-consuming. This type of data processing forms the very primitive stage when technology was not available, or it was not affordable. With the advancement of technology, the dependency on manual methods has drastically decreased.
- Mechanical Data Processing: In the mechanical data processing method, data is processed by using different devices like typewriters, mechanical printers or other mechanical devices. This method of data processing is faster and more accurate than manual data processing. These are faster than the manual mode but still forms the early stages of data processing. With invention and evolution of more complex machines with better computing power this type of processing also started fading away. Examination boards and printing press use mechanical data processing devices frequently.
- Electronic Data Processing: Electronic data processing or EDP is the modern technique to process data. The data is processed through a computer; Data and set of instructions are given to the computer as input, and the computer automatically processes the data according to the given set of instructions. The computer is also known as electronic data processing machine. This method of processing data is very fast and accurate. For example, in a computerized education environment results of students are prepared through a computer; in banks, accounts of customers are maintained (or processed) through computers, etc.
Methods of Data Processing by electronic means
1. Batch Processing: Batch Processing is a method where the information to be organized is sorted into groups to allow for efficient and sequential processing. Online Processing is a method that utilizes Internet connections and equipment directly attached to a computer. It is used mainly for information recording and research. Real-Time Processing is a technique that can respond almost immediately to various signals to acquire and process information. Distributed Processing is commonly utilized by remote workstations connected to one big central workstation or server. ATMs are good examples of this data processing method.
2. Online Processing: This is a method that utilizes Internet connections and equipment directly attached to a computer. This allows for the data stored in one place and being used at an altogether different place. Cloud computing can be considered as an example which uses this type of processing. It is used mainly for information recording and research.
3. Real-Time Processing: This technique can respond almost immediately to various signals to acquire and process information. These involve high maintenance and upfront cost attributed to very advanced technology and computing power. Time saved is maximum in this case as the output is seen in real time. For example in banking transactions
4. Distributed Processing: This method is commonly utilized by remote workstations connected to one big central workstation or server. ATMs are good examples of this data processing method. All the end machines run on a fixed software located at a particular place and make use of exactly same information and sets of instruction.
Data Processing Cycle
The Data Processing Cycle is a series of steps carried out to extract information from raw data. Although each step must be taken in order, the order is cyclic. The output and storage stage can lead to the repeat of the data collection stage, resulting in another cycle of data processing. The cycle provides a view on how the data travels and transforms from collection to interpretation, and ultimately, used in effective business decisions. There are 6 stages of data processing cycle:
- Output & Interpretation
Data Processing System
A data processing system is a combination of machines and people that for a set of inputs produces a defined set of outputs. The inputs and outputs are interpreted as data, facts, information, depending on the interpreter’s relation to the system.
A data processing system may involve some combination of:
- Conversion is converting data to another format.
- Validation – Ensuring that supplied data is “clean, correct and useful.”
- Sorting – “arranging items in some sequence and/or in different sets.”
- Summarization – reducing detail data to its main points.
- Aggregation – combining multiple pieces of data.
- Analysis – the “collection, organization, analysis, interpretation and presentation of data.”.
- Reporting – list detail or summary data or computed information.
- Presentation – data presentation is helpful in taking decisions
Commercial Data Processing
Commercial data processing involves a large volume of input data, relatively few computational operations, and a large volume of output. For example, an insurance company needs to keep records on tens or hundreds of thousands of policies, print and mail bills, and receive and post payments.
In a science or engineering field, the terms data processing and information systems are considered too broad, and the more specialized term data analysis is typically used. Data analysis makes use of specialized and highly accurate algorithms and statistical calculations that are less often observed in the typical general business environment.
Almost all fields
It is impossible to think of any area which is untouched by data processing or its use. Let it be agriculture, manufacturing or service industry, meteorological department, urban planning, transportation systems, banking and educational institutions. Data processing is required at all places with varied level of complexity.
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