As the world is becoming more data-driven day by day, the need to gain valuable insights from data is also growing. Nowadays, Data Analytics has grown in popular demand in major industries such as E-Commerce, Education, Healthcare, Banking, Travel, Retail, etc. So how are these industries able to gain these valuable insights from massive data sources? They do so by a process called Data Processing.
Data Processing is the process by which data is manipulated by many computers. It is the process of converting raw data into a machine-readable format and also transforming and formatting the output data that gets generated according to the business requirements. Simply put, Data Processing is any process that involves using computers to operate on different forms of data. Data Processing plays a major part in the commercial world as this process helps in processing data that is required to run various organizations.
This article provides a comprehensive guide into the process of Data Processing that companies can incorporate in their business processes and also elaborates on the types, methods, applications, and Life Cycle of Data Processing. Read along to find out about Data Processing and what makes it very important in the modern world today.
Table of Contents
- What is Data Processing
- Advantages of Digital Data Processing
- Applications of Digital Data Processing
- Introduction to Mechanical Data Processing
- Types of Data Processing
- Data Processing Life Cycle
What is Data Processing
Data Processing is the process whereby computers are used to convert data into better formats for gaining valuable analysis for companies. Gone are the days when enterprises used Manual Data Processing methods to convert raw information into a machine-readable format. Nowadays, companies use Digital Data Processing methods. In Manual Data Processing, companies don’t use machines, software, or any tools to acquire valuable information; instead, employees perform logical operations and calculations on the data manually.
Furthermore, data is also moved from one step to another by manual means. It takes a lot of time, cost and space to perform Manual Data Processing. Employees need excessive hard work, effort to do Manual Data Processing, but data can get misplaced and lost easily in this approach.
In order to combat these challenges, enterprises have adopted Digital or Electronic Data Processing methods, abbreviated as EDP. Machines like computers, workstations, servers, modems, processing software, and tools are used to perform automatic processing. These tools generate outputs in the form of graphs, charts, images, tables, audio, video extensions, vector files, and other desired formats as per the business requirements.
The given figure below shows the methods of Data Processing.
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Advantages of Digital Data Processing
In this era, every organization wants to compete in the marketing world. It could be done only if they’ve valuable information, helping them to take real-time decisions. EDP is a quick way to acquire outcomes as its processing time is a hundred times faster than the manual approach. Before getting down deeper, let’s discuss some other vital advantages of EDP. There are 4 main advantages of EDP:
Automatic processing of data is handled through databases located on a shared network, allowing all the connected parties to access them. Organizations can access data at any time from anywhere in the world, and thus, can make changes to improve the overall performance of data.
2) High Efficiency
EDP tools generate graphs, charts, and well-organized statistics for structured, unstructured, and semi-structured datasets without human intervention. The procedure saves time and energy for employees, boosting the efficiency of a workplace environment.
Since EDP contains automatic tools, software, and hardware, it is considered an inexpensive medium to pull out valuable information. In the Manual Data Processing method, an enterprise needs time, accuracy, the effort of employees, and bundles of a document to store every line, facts, and raw materials. Nonetheless, EDP tools remove the pressure from employees’ shoulders and do everything by themselves. Once the setup is installed, tools display results in front of users automatically.
The typical data entry error ranges from 0.55% to 3.6% in the manual approach. Although this is acceptable when enterprises work on small-sized projects, it becomes easy to highlight such errors. On the flip side of the coin, it becomes daunting to identify errors when companies use large size of datasets. The EDP cycle is an accurate system to reduce human errors, duplication mistakes, and high probability error rates than Manual Data Processing.
Therefore, manpower effort, data entry error rates, inaccuracy is minimal in the EDP approach. The EDP method not only surpasses the challenges of Manual Data Processing but also removes the Mechanical Data Processing method. The advantages of the EDP technique is given in the figure below:
Applications of Digital Data Processing
The EDP technique, has many applications which make it a preferred technique over the manual one. Some of the applications of the EDP technique are given below:
- Commercial Data Processing: Commercial Data Processing, abbreviated as CDP, is used by commercial and international organizations to transform intricate datasets into information in a fast yet precise way. Airports, for example, want to keep track of thousands of passengers, hundreds of planes, dozens of tickets, food and fuel information, and much more than that. Airline companies use fast processing computers to handle, monitor, process data, convert it into information, and take real-time decisions. Without an EDP cycle, it becomes impossible to organize a massive amount of data. That’s why Airport Operating System (AOS), an intelligent Data Processing software, is designed to ease the life of airline staff and passengers.
- Data Analysis: In the business world, Data Analysis is a process to scrutinize, clean, transform and model data by applying logical techniques and statistical calculations, ensuring to extract results, driving conclusions, and excerpt the decision-making processes. With Data Processing methods, enterprises design a Data Analytics platform that helps them to mitigate risks, increase operational efficiency and unveil information describing ways to improve profits and revenues.
- Security: The EDP method is a promising cycle to cope up with security challenges. In 2018, it was estimated that 80,000 cyber-attacks were occurred every day, summing up to over 30 million attacks annually. The pervasive nature of data breaches and cyber-attacks can’t be ignored; it’s putting personal information, files, documents, billing data, and confidential data at risk. Companies face cyber incidents because they don’t have proper strategies, technologies, and protective measures to tackle them. Data Processing methods enable them to gather data from different resources, prior incidents, and malicious events. By having a proper examination of the company’s profile, we can determine which technique is best to overcome cyber challenges in an interconnected world.
To cut the long story short, every field, such as education, E-Commerce , banking, agriculture, forensic, metrological, industrial department, and the stock market, needs EDP techniques to evaluate information critically.
Introduction to Mechanical Data Processing
Machines, such as typewriters, printers, and mechanical devices were used in the Mechanical Data Processing method. The accuracy and reliability of mechanical mode are better than the manual method. The outcomes from mechanical devices can be attained in either reports or documents format, which requires time to interpret and understand.
Likewise, Mechanical Data Processing is also labor-intensive and time-consuming. Another important point must be kept in mind that user-defined statements, orders, commands are necessary for both Manual and Mechanical Data Processing methods. EDP tools are pre-programmed with such commands. While working with EDP software, minimal labor work is involved as everything is automatic.
Types of Data Processing
Now that you understand the methods used in Data Processing, the next step involves choosing the correct type of Data Processing procedure. Many factors such as timeline, software compatibility, hardware complexity, technology requirements, must be considered when determining the type of Data Processing technique. There are generally 4 types of Data Processing:
1) Batch Processing
In Batch Processing, a large volume of data is processed all at once. The Batch Processing completes work in a non-stop and sequential order. It is an efficient and reliable way to process a large volume of data simultaneously as it reduces operational costs. The Batch Processing procedure contains distinct programs to perform input, process, and output functionalities. Hadoop is an example of a Batch Processing technique in which data is first collected, processed, and then batch outcomes are produced over an extensive period.
Payroll systems, invoices, supply chain, and billing systems use the Batch Processing method. Moreover, beverage processing, dairy farm processing, soap manufacturing, pharmaceutical manufacturing, and biotech products also practice Batch Processing techniques.
Batch Processing methods come up with debugging issues and errors. IT professionals and experts are needed to solve these glitches. Although Batch Processing Techniques limit the operational costs, it is still an expensive method as a large amount of investment is required to hire experts and technical personnel.
2) Real-Time/ Stream Data Processing
As the name indicates, this type of processing enables public and commercial enterprises to achieve real-time analysis of data. In Real-Time Data Processing (RTC), continuous input is essential to process data and acquire valuable outcomes. The period is minimal to process data, meaning businesses receive up-to-date information to explore opportunities, reduce threats and intercept challenging situations like cyber-attacks.
For example, radar systems, traffic control systems, airline monitoring, command control systems, ATM transactions, and customer service operations use Real-Time Data Processing techniques to obtain valuable insights instantly.
Amazon Kinesis, Apache Flink, Apache Storm, Apache Spark, Apache Samza, Apache Kafka, Apache Flume, Azure Stream Analytics, IBM Streaming Analytics, Google Cloud DataFlow, Striim, and StreamSQL are Real-Time Data Processing tools.
This type of technique is an intricated technique to process data. Daily updates, backup solutions must be performed regularly to receive continual inputs. It is a slightly tedious and difficult technique than the former technique.
In the time-sharing technique, a single CPU is accessed by multiple users. Different time slots are allocated to each user to perform individual tasks and operations. Particularly, a reference or terminal link of the main CPU is given to each user, wherein the time slot is determined by dividing CPU time by the total number of users present at that time.
It is the most widespread and substantial technique to process data. High efficiency, throughput, and on-time delivery are the basic advantages of the Multiprocessing technique. It uses multiple CPUs to perform tasks or operations. However, each CPU has a separate responsibility. CPUs are arranged in parallel order, concluding that breakage or damage to any one of the CPUs doesn’t affect the performance of the other CPUs.
5) Online Processing
When a user performs face-to-face communication with the computer and exploits internet connectivity, then the processing of data is called Online Processing. For instance, if the user makes any change in the existing data of the computer, the machine will automatically update the data across the entire network. In this way, everyone receives up-to-date information.
Booking of tickets at airports, railway stations, cinemas, music concerts, hotel reservations, are all common examples of Online Data Processing. Buying goods & services from E-Commerce websites through an Internet connection is also an example of Online Data Processing. Inventory stores can refill their stock and update the website by calculating how many items are remaining.
There is a disadvantage using this technique. When industries use this technique, they are susceptible to hacking and virus attacks.
Data Processing Life Cycle
Now that you understand the methods and types of Data Processing, the next step is to understand the process that governs it. A Data Processing Life Cycle consists of 6 stages:
It involves the collection of resource types, the quality of data being used, and the raw information that is needed to process data. The mediums adopted by the company to gather the data are highly critical and must be checked before moving forward. The collection step is the root of the entire cycle; it tells companies what they want to interpret and what they want to improve. It is essential to use reliable and trustworthy Data Lakes or Data Warehouses to generate desirable outcomes.
The Data Preparation or pre-processing stage is the second stage of the cycle in which raw data is polished or prepared for the next stages. Data collected from reliable sources is checked for errors, redundant or repetitive entries, and duplicate copies to make it clean and unique datasets.
In this stage, clean data is converted into a machine-readable format or valuable information. It is the first step to achieving usable results and outcomes. It is a complicated step as fast processing power, speed, accuracy, and time are needed to convert the data into machine-readable syntax.
Different algorithms, statistical calculations, AI/ML/DL (Artificial Intelligence/ Machine Learning/ Deep Learning) tools are used at this stage to process data. Processing of data under the bridge of these algorithms and tools enables enterprises to generate information for interpretation and explanation purposes.
After processing data from the previous four stages, it becomes ready for presenting it in front of users. Reports, documents, charts, tables, graphs, images, multimedia, audio, video files are used to present information. The presentation of output must be in a format that immediately helps users to extract meaningful statistics.
After getting valuable information, it is kept for future use. By storing the information properly, authenticated users can access them easily and quickly.
The Data Processing Life Cycle is represented in the figure below.
This article gave a comprehensive analysis of Data Processing and its importance to various businesses. It described the methods of Data Processing, their advantages, types, applications, and also the Life Cycle of Data Processing in detail. Overall, having a systematic EDP procedure is crucial for many businesses as it helps process data in a smooth and efficient manner and also helps to gain valuable insight from it.Visit our Website to Explore Hevo
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