Technatic | Data processing is one of the important aspects that must be carried out by every organization that wants to develop and achieve its goals. Without data processing, organizations will find it difficult to access data that can be useful for increasing their competitiveness and providing important insights. That's why it's critical for every organization to understand the need to process all their data, and how to do it.
Through this article, you will learn about data processing in more depth, starting from techniques, methods, cycles, to examples. But before that, you have to know what is meant by data processing first. Let's look at the complete explanation below!
What is Data Processing ?
Data processing is a process that converts raw data into useful and easily accepted information. Raw data is usually in the form of numbers or notes that have no meaning to the user, so they require processing to convert them into useful information using certain techniques and methods.
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Typically, data processing is performed by a data scientist or team of data scientists, and must be done correctly so as not to negatively impact the final product or data output. The data processing process begins with the data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and used by employees throughout the organization.
The terms "Data Processing", or "Data Processing", and "Data Processing" (DP) in English are also often used to refer to departments in organizations that are responsible for the operation of data processing programs.
What are the Purposes of Data Processing
Data processing is one of the important things that every organization must do. The following are some of the purposes of data processing.
1. Turning Raw Data Into Information
The main goal of data processing is to transform raw data into useful information for users. The raw data that has been collected will be useless if it is not processed first; This data will only become numbers or notes that have no meaning if it is not processed.
2. Makes decision making easier
Raw data that has been processed into useful information can help make decision making easier for users. Information that is clear and easy to understand makes the decision-making process faster and more efficient.
3. Provide Accurate and Valid Data
Through the data processing process, all raw data that has been collected will be processed and filtered to ensure that the data provided is accurate and valid. This is very important when you want to make business decisions based on data.
4. Reduce Costs and Time
Data processing using efficient techniques and methods can help reduce the costs and time required to collect and analyze data. This can help make business processes more efficient and effective.
5. Make data easier to manage and reprocess
Data that has been processed into useful information can make the data easier to manage and reprocess for further purposes. This can help ensure that the data can be used well for a long time.
What are the Data Processing Methods
There are at least three data processing methods that are most commonly used, including:
- Manual Data Processing: The entire process of data collection, filtering, sorting, calculations and other logical operations are all carried out with human intervention. This is a low-cost method and requires little or no tools, but can result in high error rates, high labor costs, and a lot of time and repetitive tasks.
- Mechanical Data Processing: In this method, data is processed mechanically through the use of devices and machines. This can include simple devices such as calculators, typewriters, printing presses, etc. This method can be used for simple data processing operations.
- Electronic Data Processing: In this method, data is processed with modern technology using software and data processing programs. This method is the most expensive but can provide the fastest processing speed with the highest output reliability and accuracy.
What are the Data Processing Techniques
There are many techniques you can use to process data. The following are some of the most common data processing methods.
1. Batch Processing
This technique involves processing data periodically or all at once in "batches" or large groups. The data processing process is carried out on the collected data and is not carried out in real-time. Batch processing is often used for large and complex processing tasks such as monthly reports or payroll processing.
2. Real-time processing
This technique involves real-time data processing, so that the data can be processed and the results received at the same time. Real-time processing is often used in applications that require fast response times such as transaction systems, sensory monitoring, and gaming applications.
3. Online Processing
This technique involves continuously processing data as it comes in and processing each individual piece of data separately. Online processing is often used in systems that require real-time interaction between users and systems, such as e-commerce applications, customer information systems, and management information systems.
4. Multiprocessing
This technique involves processing data by multiple processors or machines at the same time, dividing the data processing task into several parts, and managing each part with a different processor. Multiprocessing can increase the efficiency and speed of data processing, especially for tasks that require large resources.
5. Time-sharing
This technique involves processing data by allocating computer resources and data in time slots to several users simultaneously. Time-sharing allows many people, located in different places, to use a particular computer system at the same time.
What are the Data Processing Cycles
There are at least seven processes that must be gone through to process data, including:
- Collection: This cycle involves collecting data from various sources such as surveys, transactions, or databases. The data collected must be accurate, complete, and in accordance with the purposes of data processing.
- Preparation: This cycle involves cleaning data from deficiencies, errors, or duplications. This process is important to ensure that the data to be processed is high quality and reliable.
- Transformation: This cycle involves changing the form of data such as encoding, scaling, or aggregation. This process is useful to help convert data into a format that suits analysis needs.
- Loading: This cycle involves loading data into a database or data storage system for processing and analysis. This process must be done correctly so that the data remains accurate and maintained.
- Analysis: This cycle involves data analysis to extract useful information and fulfill data processing objectives. The analysis carried out can use statistical techniques, machine learning, or other appropriate techniques.
- Visualization: This cycle involves visual representation of the results of data analysis, such as tables, graphs, or interactive visualizations. This process helps to visualize and understand the analysis results easily.
This cycle involves presenting the results of analysis and visualization in the form of a report or presentation. This process can help present analysis and visualization results to the right audience.