Data processing science is collecting raw data and translating it into usable information. The procedure is performed by data scientists through specific stages and categories. They work with raw data such as website cookies, user behavior, and monetary figures. Then, they present it in a more readable format, such as documents, graphs, and charts to be interpreted by computers and utilized by employees throughout the company. Keep reading to learn more about the different data processing stages and types.
To transform raw data into usable information, data scientists must follow the six main steps in the data processing cycle.
The first step in the data processing cycle is the collection of raw data. Data is pulled from sources such as the company’s point-of-sale systems and mailing lists, usually stored in the company’s data lakes and warehouse. In addition, the type of raw data collected has a massive impact on the output produced. That’s why scientists aim to gather raw data from accurate sources to ensure that the subsequent findings are valid and usable.
The second step in the data processing cycle includes data preparation. During preparation, data scientists sort and filter the raw data, check for errors, and remove redundant, incomplete, or inaccurate data. This step aims to ensure that only the highest quality data is fed into the processing unit.
Data input is the first stage in which data is converted into usable information. First, data scientists enter clean and high-quality data into their systems, such as their customer relationship management (CRM) software. Next, data is converted into a machine-readable format and fed into the processing unit through input sources such as a keyboard, mouse, scanner, and magnetic ink character reader (MICR).
In the fourth stage, the data inputted to the system is processed for interpretation using machine learning algorithms to generate the desired output. The process may vary depending on the source of data being processed, such as data lakes or connected devices. The intended use of the output, such as determining customer needs or understanding medical diagnoses from connected devices, may also alter the process slightly.
In the output or interpretation stage, data has been translated into usable information and can be utilized by non-data scientists, such as the finance, marketing, and accounting departments. It is displayed in a readable format such as documents, graphs, tables, videos, and images. The output can be stored and further processed in the next stage of the data processing cycle.
The sixth and final data processing stage is storage, where the metadata is stored for future use. When it is appropriately stored, data scientists can quickly and easily access and retrieve the information when necessary and use it as input in the data processing cycle. Nonetheless, company members may use some information immediately for business purposes, such as getting better customer insight, increasing customer satisfaction, and detecting fraudulent activities.
There is no one-size-fits-all method that scientists can use for data processing. Instead, each technique depends on the information that needs to be captured and the time available. With that said, let’s walk through the seven types of data processing techniques.
As the name suggests, manual data processing is handled manually. Collecting, filtering, sorting, and other operations are done with human intervention through the ledger, paper record systems, and any other manual data entry process. Manual data processing is one of the earliest data processing methods. In addition, it is costly, time-consuming, labor-intensive, and prone to human error. Collecting information from paper forms is one of the best examples of manual data processing.
Mechanical data processing processes data through mechanical devices and machines, such as calculators, typewriters, and printers. This method performs simple data processing operations faster than manual data processing. Nonetheless, it is beginning to fade with future evolutions that can process large volumes of data.
In electronic data processing, data is processed with modern technologies using data processing software and programs with pre-defined instructions from data scientists. Even though this method is more accurate and faster than its predecessors, it requires human intervention for manual data entry and calculations. One of the most used examples of electronic data processing is digital spreadsheets to maintain student records in educational institutions.
Batch data processing works by processing data periodically and in bulk. In other words, data is piled up and entered into the system in groups. This type of processing is used in the payroll and billing systems that must be processed bi-weekly or monthly. Batch data processing accelerates the processing time and can complete a series of tasks without human intervention.
In real-time data processing, data is processed immediately. In other words, it captures data in real time and generates automatic reports simultaneously. As a result, it is one of the fastest methods to process data. Examples of real-time data processing include bank ATM transactions, customer service systems, weather forecasts, and GPS tracking systems.
Online data processing is used for the continuous processing of data. It is automated to enter and process data into a computer system in real time. Nonetheless, it shouldn’t be confused with real-time data processing. An excellent example of this type of processing is bar code scanning. When purchasing an item, the bar code gets scanned at the register. As a result, the article is immediately updated in the store’s inventory system as being sold. Since online processing updates continuously, employees can run an inventory report at any time and have accurate, up-to-date information.
Automatic data processing processes data in real-time, error-free, and secure without human intervention. This type of processing uses computers to analyze, organize, store, retrieve, manipulate, and report the results. For example, predictive analytics software can incorporate automatic data processing to mine, analyze historical data patterns, and predict future outcomes by extracting information from data sets to determine patterns and trends.
While the six steps of data processing science have remained constant, data processing has come a long way throughout the decades. It has evolved from manual to automated, delivering fast, advanced, and effective processing methods. As a result, more and more organizations will leverage data processing, specifically automatic data processing, to improve decision-making, reduce errors and operational costs, and use the workforce more effectively. Consider reading how data is reshaping the fintech industry to learn more about the latest developments.