What Is Data Processing in Research Methods?
Data processing in research is the collection and translation of a data set into valuable, usable information. Through this process, a researcher, data engineer or data scientist takes raw data and converts it into a more readable format, such as a graph, report or chart, either manually or through an automated tool. The researcher will then use this information to gain insights, solve problems, make improvements and ultimately generate better results.
What Are the Stages of Data Processing?
The data processing cycle includes several steps. Though each stage has a specific order, the entire process repeats cyclically.
Data collection is the process of extracting data from available sources, such as data warehouses and data lakes. Raw data can come in several forms, from user behavior to monetary figures to profit statements to web cookies.
The type of raw data that you collect will have a significant impact on the output you later produce. Researchers must look to accurate, trustworthy and comprehensive sources for valid, usable findings.
Through data preparation, you will polish, organize, filter and examine raw data for errors. The data preparation stage is meant to eliminate incorrect, redundant or incomplete data and convert it into a suitable form for further processing and analysis. The goal of the preparation stage is to achieve the highest quality data possible.
The input stage is the first stage where raw data begins to resemble usable information. Once the data is clean, you’ll enter it into a corresponding destination, such as a data warehouse or customer relationship management (CRM) software, and translate it into a compatible language for these systems. You can enter this data using numerous input sources, including keyboards, scanners or digitizers.
Next, you’ll begin to process the data stored in your computer during the data input stage. You can conduct data processing using machine learning and artificial intelligence algorithms to generate the desired input, but the processing will vary based on your data sources and intended output use. You can use the data from the processing stage in a variety of ways, from creating medical diagnoses to determining customer needs to drawing connections between advertising patterns.
Through this stage, data becomes usable and can be interpreted by non-data scientists. This translated data is readable and often presented in images, graphs, text, audio and videos. Once interpreted, company members can self-serve the data for their analytics projects.
After processing the data successfully, all remaining information should be stored for later use. When companies properly store their data, they remain compliant with data protection legislation and promote a faster, easier means of accessing information when they need to. They can also use this data as input in the following processing cycle.
What Are the Three Methods of Data Processing?
You can choose from three primary methods of data processing based on your needs:
Manual data processing: Through this method, users process data manually, meaning they carry out every step without using electronics or automation software. Though this method is the least expensive and requires minimal resources, it can be time-consuming and has a higher risk of producing errors.
Mechanical data processing: Mechanical processing involves the use of machines and devices to filter data, such as calculators, printing presses or typewriters. This method is suitable for simple data processing endeavors and produces fewer errors but is more complex than other techniques.
Electronic data processing: Researchers process data using modern data processing software and technologies, where they feed an instruction set to the program to analyze the data and create a yield output. Though this method is the most expensive, it is also the fastest and most reliable for generating accurate output.
The Benefits of Data Processing in Quantitative Research
When you use data processing in quantitative research, your company will experience a range benefits:
- Easier report building
- Higher processing speed
- Cost reduction
- Simple storage
- Greater data accuracy
- Regulatory compliance
- Enhanced security
- Smooth collaboration
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