Meaning of the word data processing

From Wikipedia, the free encyclopedia

Data processing is the collection and manipulation of digital data to produce meaningful information.^[1]
Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer.^{[note 1]}

The term «Data Processing», or «DP» has also been used to refer to a department within an organization responsible for the operation of data processing programs.^[2]

Data processing functions[edit]

Data processing may involve various processes, including:

• Validation – Ensuring that supplied data is correct and relevant.
• Sorting – «arranging items in some sequence and/or in different sets.»
• Summarization(statistical) or (automatic) – reducing detailed 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.
• Classification – separation of data into various categories.

History[edit]

The United States Census Bureau history illustrates the evolution of data processing from manual through electronic procedures.

Manual data processing[edit]

Although widespread use of the term data processing dates only from the 1950’s, ^[3] data processing functions have been performed manually for millennia. For example, bookkeeping involves functions such as posting transactions and producing reports like the balance sheet and the cash flow statement. Completely manual methods were augmented by the application of mechanical or electronic calculators. A person whose job was to perform calculations manually or using a calculator was called a «computer.»

The 1890 United States Census schedule was the first to gather data by individual rather than household. A number of questions could be answered by making a check in the appropriate box on the form. From 1850 to 1880 the Census Bureau employed «a system of tallying, which, by reason of the increasing number of combinations of classifications required, became increasingly complex. Only a limited number of combinations could be recorded in one tally, so it was necessary to handle the schedules 5 or 6 times, for as many independent tallies.»^[4] «It took over 7 years to publish the results of the 1880 census»^[5] using manual processing methods.

Automatic data processing[edit]

The term automatic data processing was applied to operations performed by means of unit record equipment, such as Herman Hollerith’s application of punched card equipment for the 1890 United States Census. «Using Hollerith’s punchcard equipment, the Census Office was able to complete tabulating most of the 1890 census data in 2 to 3 years, compared with 7 to 8 years for the 1880 census. It is estimated that using Hollerith’s system saved some $5 million in processing costs»^[5] in 1890 dollars even though there were twice as many questions as in 1880.

Electronic data processing[edit]

Computerized data processing, or Electronic data processing represents a later development, with a computer used instead of several independent pieces of equipment. The Census Bureau first made limited use of electronic computers for the 1950 United States Census, using a UNIVAC I system,^[4] delivered in 1952.

Other developments[edit]

The term data processing has mostly been subsumed by the more general term information technology (IT).^[6] The older term «data processing» is suggestive of older technologies. For example, in 1996 the Data Processing Management Association (DPMA) changed its name to the Association of Information Technology Professionals. Nevertheless, the terms are approximately synonymous.

Applications[edit]

Commercial data processing[edit]

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.

Data analysis[edit]

In science and engineering, the terms data processing and information systems are considered too broad, and the term data processing is typically used for the initial stage followed by a data analysis in the second stage of the overall data handling.

Data analysis uses specialized algorithms and statistical calculations that are less often observed in a typical general business environment. For data analysis, software suites like SPSS or SAS, or their free counterparts such as DAP, gretl or PSPP are often used.

Systems[edit]

A data processing system is a combination of machines, people, and processes that for a set of inputs produces a defined set of outputs. The inputs and outputs are interpreted as data, facts, information etc. depending on the interpreter’s relation to the system.

A term commonly used synonymously with data or storage (codes) processing system is information system.^[7] With regard particularly to electronic data processing, the corresponding concept is referred to as electronic data processing system.

Examples[edit]

Simple example[edit]

A very simple example of a data processing system is the process of maintaining a check register. Transactions— checks and deposits— are recorded as they occur and the transactions are summarized to determine a current balance. Monthly the data recorded in the register is reconciled with a hopefully identical list of transactions processed by the bank.

A more sophisticated record keeping system might further identify the transactions— for example deposits by source or checks by type, such as charitable contributions. This information might be used to obtain information like the total of all contributions for the year.

The important thing about this example is that it is a system, in which, all transactions are recorded consistently, and the same method of bank reconciliation is used each time.

Real-world example[edit]

This is a flowchart of a data processing system combining manual and computerized processing to handle accounts receivable, billing, and general ledger

See also[edit]

• Scientific computing
• Big data
• Computation
• Decision-making software
• Information and communications technology
• Information technology
• Computer science

Notes[edit]

External links[edit]

References[edit]

Further reading[edit]

• Bourque, Linda B.; Clark, Virginia A. (1992) Processing Data: The Survey Example. (Quantitative Applications in the Social Sciences, no. 07-085). Sage Publications. ISBN 0-8039-4741-0
• Levy, Joseph (1967) Punched Card Data Processing. McGraw-Hill Book Company.

Introduction

Whether the internet is used to research a topic, make transactions online, food ordering, data is continuously generating each second. The amount of data has increased due to the increased utilisation of online shopping, social media and streaming services. A study has estimated that 1.7MB of data is generated each second for every single human being on this earth in 2020. To avail and get intuitions from such huge amounts of data – data processing is useful.

So what is Data Processing? To be put in simple words is the collection, manipulation, and processing of collected data for the intended use. It translates huge amounts of collected data into a desirable form used by commoners to analyze and interpret the meaning of data processed. Data processing in computers refers to the manipulation of data by computers. This is inclusive of output formatting or transformation. Data flow through the memory and  CPU to the output device and of course, the reformation of raw data into machine language.

1. What is Data Processing?
2. How is data processed?
3. Different Types Of Output
4. Different Methods
5. Types
6. Why we should use Data Processing

1) What is Data Processing?

Concept of Data processing is collecting and manipulating data into a usable and appropriate form. The automatic processing of data in a predetermined sequence of operations is the manipulation of data. The processing nowadays is automatically done by using computers, which is faster and gives accurate results.

Thereafter, the data collected is processed and then translated into a desirable form as per requirements, useful for performing tasks. The data is acquired from various sources like excel file, database, text file data, and unorganised data such as audio clips, images, GPRS and video clips. The most commonly used tools for data processing are Storm, Hadoop, HPCC, Statwing, Qubole and CouchDB. The output is worthwhile information various file formats like a chart, audio, table, graph, image, vector file depending on software or application necessary.

Therefore the meaning of Data processing is a method of collecting raw data and converting it into useful information. Data Processing is performed in a predetermined procedure by a team of data scientists and data engineers in an organization.

2) How is data processed?

Data processing requires six steps, and those are:

1. Data Collection: The primary stage of data processing is to collect data. Data is acquired from sources like data lakes and data warehouses. The collected data must be trustworthy and of high quality.
2. Data Preparation: Also called “pre-processing”, this stage is where the collected data is cleansed by checking for errors and arranged for the following data processing stage. Elimination of useless data and generating quality data for quality business intelligence is the motive of this stage.
3. Data Input: The prepared data is translated into machine language by using a CRM such as Salesforce and Redshift, a data warehouse. 
4. Processing: The processing of input data is done for interpretation. The processing is accomplished by machine learning algorithms. Their process is variable depending on the data which is processed (connected devices, social networks, data lakes, etc.) and the intended use (medical diagnosis, ascertaining customer wants, examining advertising patterns, etc.).
5. Data Interpretation: The non-data scientists find this data very helpful. The data is converted into videos, graphs, images and plain text. Members of a company can start analysing this data and applying it to their projects.
6. Data Storage: Storage utilisation in future is the final step of processing. Effective Properly storage of data is necessary for compliance with GDPR (data protection legislation). Properly stored data to be accessed easily and quickly by employees of an institution as and when needed is of utmost importance.

3) Different Types Of Output

The different types of output files in data processing are –

• Plain Text File – The text file is the simplest format of a data file will be exported as Notepad or WordPad files. 
• Table/Spreadsheet – the data is represented in columns and rows, that helps in quick analysis and understanding of data. Tables/ Spreadsheet allows numerous operations like sorting & filtering in descending/ascending order and statistical operations.
• Charts and graphs – The most common features in almost all software is the graphs and charts format. This format enables easy analysis of data by just a glance.
• Maps/Vector or Image File – The requirement to store and analyse spatial data and export data can be fulfilled by this image and map formats.
•  Specialised software can process software specific file formats.

4) Different Methods

The three prominent data processing methods are as follows:

• Manual Data Processing: Data is processed manually in this data processing method. The entire procedure of data collecting, filtering, sorting, calculation and alternative logical operations is all carried out with human intervention without using any electronic device or automation software. It’s a low-priced methodology and needs very little to no tools; however, it produces high errors and requires high labour prices and much of your time.
• Mechanical Data Processing: data is processed using machines and simple devices such as typewriters, calculators, printing press, etc. Simple data processing operations can be accomplished by this method. There are fewer errors compared to manual data processing, but the only drawback is that this method cannot be utilized with the increase of data. 
• Electronic Data Processing:  Data processing softwares and programs are used to process data. A series of instructions are given to the software to process the data and produce the desired output. It is more expensive but provides faster processing with the highest reliability and accuracy.

5) Types

The types of data processing are as below:

• Batch Processing: The collection and processing of data is done in batches where there is a huge quantity of data. E.g., the payroll system.
• Real-time processing: For a small quantity of data, real-time processing is done where data can be processed within seconds of data input.

E.g., withdrawing money from ATM

• Online Processing: As and when data is available, it is automatically entered in the CPU. This is useful for processing of data continuously.

E.g., barcode scanning

• Multiprocessing: This also goes by the name parallel processing, where data is fragmented into small frames and processed in two CPUs within a single computer system.

E.g., weather forecasting

• Time-sharing: Allocates computer resources and data in time slots to several users simultaneously.  

6) Why we should use Data Processing

In the modern era, most of the work relies on data, therefore collection of large amounts of data for different purposes like academic, scientific research, institutional use, personal and private use, for commercial purposes and lots more. The processing of this data collected is essential so that the data goes through all the above-stated steps and gets sorted, stored, filtered, presented in the required format and analyzed.

The amount of time consumed and the intricacy of processing will depend on the required results. In situations where large amounts of data are acquired, the necessity of processing to obtain authentic results with the help of data processing in data mining and data processing in data research gets inevitable.

Conclusion

Finally, to define data processing in simple terms, it is the procurement of worthwhile information by conversion of data. The processing of data is done in six stages which are data collection,  sorting of data, storage of data, processing of data,  data presentation and data analysis.

The three prominent methods of processing data are Mechanical, Electronic and Manual. Data processing is crucial for organizations to create better business strategies and increase their competitive edge. By changing the data into a legible format like graphs, charts and documents, workers throughout the organization will be able to perceive and use the data to analyse and interpret according to their requirements.

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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? And what is 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. It plays a major part in the commercial world as this process helps in processing data that is required to run various organizations.

In the thing guide, we’ll explore in detail common questions like- What is data processing? And how is data processed? We’ll walk you through methods of data processing, types of data processing, their associated advantages, and applications. Lastly, we’ll cover the topic of what is data processing in the future. Without further ado, let’s dive right in.

Table of Contents

• What is Data Processing?
• How is Data Processed?
• What are the Types of Data Processing?
• What are the Methods of Data Processing?
  • Manual Data Processing
  • Mechanical Data Processing
  • Digital/Electronic Data Processing
• What are the Advantages of Digital Data Processing?
• What are the Applications of Digital Data Processing?
• What is Mechanical Data Processing?
• What Types of Output get from Data Processing?
• The Future of Data Processing
• Conclusion

Gone are the days when enterprises used Manual Data Processing methods to convert raw information into a machine-readable format. Today every individual and business needs to know 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.

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, and 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. 

How is Data Processed?

After having been briefed on what is data processing, let’s now discuss how data is processed. A Data Processing Cycle consists of 6 stages:

• Collection
• Preparation
• Input
• Processing
• Output
• Storage

1) Collection

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.

2) Preparation

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.

3) Input

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.

4) Processing

Different algorithms, statistical calculations, and 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.

5) Output

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, and video files are used to present information. The presentation of output must be in a format that immediately helps users to extract meaningful statistics. 

6) Storage

After getting valuable information, it is kept for future use. By storing the information properly, authenticated users can access them easily and quickly.

What are the Types of Data Processing?

Many factors such as timeline, software compatibility, hardware complexity, and technology requirements, must be considered when determining the type of technique. There are generally 5 types:

• Batch Processing
• Real-Time/ Stream Processing
• Time-Sharing
• Multiprocessing
• Online Processing

1) Batch Processing

In Batch Processing, a large volume of data is processed all at once. Batch Processing completes work in 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 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 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 intricate technique to process data. Daily updates and backup solutions must be performed regularly to receive continual inputs. It is a slightly more tedious and more difficult technique than the former technique. 

3) Time-Sharing

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.

4) Multiprocessing

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, and 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 the same. Inventory stores can refill their stock and update the website by calculating how many items are remaining.

There is a disadvantage to using this technique. When industries use this technique, they are susceptible to hacking and virus attacks.

What are the Methods of Data Processing?

The 3 prominent methods are mentioned below.

1. Manual Data Processing

In manual Data Processing, the entire series of tasks including Data Collection, Filtering, Calculation, and alternative logical operations are all carried out with human intervention without the use of any digital device or automation software.

This process saves costs and requires little to no tools. However, it requires Data Professionals to spend an extensive amount of time and focus to carry out the Data Processing steps. On top of that, this methodology is very much error-prone and requires high labor prices.

2. Mechanical Data Processing

Machines, such as typewriters, printers, and mechanical devices were used in the Mechanical Data Processing method. The accuracy and reliability of the mechanical mode are better than the manual method as there are fewer errors compared to the manual method. However, this method is not suited while working with huge amounts of data.

3. Digital/Electronic Data Processing

Processing is carried out with the help of advanced software and programs. A series of instructions are given to the software to process the data and produce the desired result. It is faster, reliable, and accurate, but all this comes at a cost.

What are the 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:

• Performance
• High Efficiency
• Cheap
• Accuracy

1) Performance

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. 

3) Cheap

Since EDP contains automatic tools, software, and hardware, it is considered an inexpensive medium to pull out valuable information. In the Manual 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.   

4) Accuracy

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, and inaccuracy is minimal in the EDP approach. The EDP method not only surpasses the challenges of Manual Processing but also removes the Mechanical method as a whole.  The advantages of the EDP technique are given in the figure below:

What are the Applications of Digital Data Processing?

The EDP technique has many applications which makes 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.
• Real-time Data Monitoring and Processing: 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, cleaning, transform, and modeling data by applying logical techniques and statistical calculations, ensuring to extract results, driving conclusions, and excerpting the decision-making processes.
  With Data Processing systems, 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 with security challenges. In 2018, it was estimated that 80,000 cyber-attacks 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.
• Reduced Cyber 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, forensics, metrological, industrial department, and the stock market, needs EDP techniques to evaluate information critically. 

What is Mechanical Data Processing?

Machines, such as typewriters, printers, and mechanical devices were used in the Mechanical Data Processing method. The accuracy and reliability of the 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, the Mechanical method is also labor-intensive and time-consuming. Another important point must be kept in mind that user-defined statements, orders, and commands are necessary for both Manual and Mechanical Processing methods. EDP tools are pre-programmed with such commands. While working with EDP software, minimal labor work is involved as everything is automatic.

What Types of Output get from Data Processing?

The different types of output files are mentioned below.

• Plain Text Files: The text file is the simplest format of an output file. It can be exported as a Notepad or WordPad file.
• Tables/Spreadsheets: Data can also be outputted in a collection of rows and columns, making it easy to analyze and visualize. Tables/ Spreadsheets allow numerous sorting, filtering, and statistical operations.
• Charts and Graphs: Charts and Graphs are the most convenient way of visualizing data and its insights. They enable easy data analysis with just a glance.
• Maps or Image Files: Images and Map formats allow you to analyze spatial data and export data.

What is the Future of Data Processing?

The answer lies in the cloud. With every organization moving most of its business into the cloud, it is essential to have faster, high-quality data for each organization to utilize. Cloud technology implements the current electronic methods and accelerates its speed and effectiveness.

Moving into the Cloud allows companies to combine all of their data and platforms into one easily-adaptable system. Cloud platforms can be inexpensive and are highly flexible to grow and expand as the company scales.

Conclusion

This article gave a comprehensive analysis of what is Data Processing and its importance to various businesses. It described the methods of Data Processing, their advantages, types, applications, and also the Data Processing Cycle 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.

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