Data Processing: Meaning, Properties, Input, Processing & Output


The term data refers to raw and unprocessed facts, observations, measurements, or symbols that represent various entities, events, or attributes. Data can take various forms, such as numbers, text, images, sounds, or any other form of representation. It represents the fundamental building blocks that can be input into a processing system for further analysis, manipulation, or interpretation.

A processing system, whether operated by humans or machines, encompasses a range of activities including computations, transformations, comparisons, and other operations performed on the data. These processes are designed to extract meaningful insights, discover patterns, and derive useful knowledge from the raw data. Data processing involves organizing, categorizing, sorting, filtering, aggregating, or performing calculations on the data to convert it into a more useful and structured form.

Information, on the other hand, is the output or end result of a processing system. It represents the processed and refined data that has been transformed into a meaningful and contextually relevant form. The information provides a deeper understanding, interpretation, or explanation of the underlying data and often carries additional value or significance.

The purpose of processing data is to generate meaningful information that can be utilized for decision-making, problem-solving, or gaining insights into various aspects of a system or situation. Management, in particular, relies on information to make informed decisions and formulate strategies based on the analyzed data. The information derived from data serves as a valuable resource for planning, forecasting, monitoring, evaluating, and optimizing processes, operations, and outcomes.

The relationship between data and information can be visualized as a continuous cycle of transformation and refinement. Data is collected, processed, and transformed into information, which then becomes a basis for further data collection and analysis. This iterative process helps in building knowledge, improving understanding, and driving informed actions.

In summary, data represents the raw and unprocessed facts, while information is the processed and meaningful output derived from data through various processing activities. The conversion of data into information forms a critical component of decision-making and knowledge generation in both human-operated and machine-operated processing systems.



Data processing refers to the systematic manipulation and transformation of raw data to generate meaningful information. It involves a series of operations performed on computer data to organize, analyze, validate, and retrieve relevant information for various purposes. Data processing is crucial for converting data into a useful format that can facilitate decision-making, problem-solving, and knowledge generation.

The initial collection of raw data may consist of individual facts or observations that, on their own, hold little value or significance. For example, a collection of weights of individuals by itself may not provide useful information for decision-making. However, through data processing, these individual weights can be analyzed, validated, and compared with other data to derive meaningful results or information.

During the data processing phase, various techniques and operations are applied to the collected data. This may include tasks such as data entry, where the data is input into a computer system or database, ensuring accuracy and completeness. Sorting is another operation that arranges the data in a specific order based on selected criteria, allowing for easier analysis and retrieval.

Updating data involves modifying or adding new information to the existing dataset. This can include adding new observations, updating existing records, or incorporating additional data sources. Data retrieval is the process of accessing and extracting relevant information from the stored dataset based on specific criteria or queries.

The computer plays a crucial role in data processing, as it provides the necessary tools and computational power to handle large volumes of data efficiently. Through software applications and algorithms, the computer can perform complex calculations, comparisons, and transformations on the data to generate meaningful insights and results.

Data processing serves as a foundation for decision-making and problem-solving in various domains. For example, in management, processed data can be used to make informed decisions about assigning specific duties or responsibilities based on certain criteria. It enables organizations to analyze trends, patterns, and relationships within the data, leading to a deeper understanding of their operations, customers, and markets.

The specific use of processed data and the resulting information depends on the context, goals, and requirements of the organization or situation. Different analytical techniques, statistical methods, and data visualization tools can be applied to further explore and interpret the processed data, unlocking valuable insights and knowledge.

Data processing involves the transformation of raw data into meaningful information through a series of operations performed on computer data. It plays a vital role in decision-making, knowledge generation, and problem-solving by organizing, analyzing, validating, and retrieving relevant information from the collected data. The computer serves as a powerful tool in data processing, enabling efficient handling and manipulation of large datasets to derive valuable insights.


  1. Collected/Captured
  2. Prepared
  3. Presented
  4. Precise
  5. Complete
  6. Accurate
  7. Purposeful
  8. Assigned


The data processing cycle describes the stages of data processing. It involves the following stages:

  • Data gathering
  • Data collation
  • Input stage
  • Processing stage
  • Storage stage
  • Output stage


In the realm of data processing, the flow of information can be conceptualized through the stages of input, processing, and output, with storage playing a critical supporting role. This framework illustrates the fundamental steps involved in manipulating data to derive meaningful results and generate valuable output.

1. Input: The input stage involves the collection and acquisition of data from various sources. Data can be obtained through manual data entry, automated systems, sensors, surveys, or external data feeds. During the input phase, data is captured and transformed into a format that can be processed and analyzed. Input processes may include data validation, verification, and formatting to ensure accuracy and consistency.

2. Processing: Once the data is acquired, it undergoes various operations and transformations to extract valuable insights and generate meaningful output. Processing entails applying computational techniques, algorithms, and statistical methods to the collected data. This stage involves sorting, filtering, aggregating, analyzing, and manipulating the data to uncover patterns, relationships, and trends. Complex calculations, data modeling, and machine learning algorithms may be employed during the processing phase to derive valuable information from the data.

3. Output: The output stage involves presenting the processed data in a useful and accessible form. This can take different formats depending on the intended audience and objectives. Output may include reports, visualizations, dashboards, charts, graphs, summaries, or any other format that effectively communicates the insights and findings derived from the processed data. The output should be tailored to meet the specific needs of the users, providing them with actionable information for decision-making, problem-solving, or knowledge generation.

4. Storage: While not explicitly part of the input-processing-output framework, storage is an essential component of data processing. Both the input data and program instructions or algorithms need to be stored for future reference and reuse. Data storage can involve databases, data warehouses, cloud storage, or other structured or unstructured storage systems. The storage stage ensures that data and program instructions are readily available for future processing, analysis, or retrieval.

The input-processing-output framework is a cyclical process, where the output of one cycle becomes the input for the next. This iterative nature allows for continuous refinement, improvement, and learning from the data processing activities. Feedback from the output stage can also influence future data collection strategies and inform adjustments to the processing techniques.

Overall, the input-processing-output model provides a structured approach to handling data, converting it into meaningful insights through processing, and presenting the results in a format that supports decision-making, problem-solving, and knowledge creation. The integration of storage ensures the availability and accessibility of both data and program instructions, enabling seamless and efficient data processing workflows.


Data processing activities involve the following:

  1. INPUT: involves three steps; collection, verification/validation, and coding
  2. PROCESSING: involves classification, sorting, calculating, converting, and storing
  3. OUTPUT: involves retrieving, converting, and communicating.


  1. COLLECTION: involves gathering data from various sources and assembling it at one location.
  2. VERIFICATION/VALIDATION: after data have been gathered, their accuracy and completeness must be checked. This is an important step that helps to eliminate the possibility of Garbage-In – Garbage-out(GIGO)
  3. CODE: data must be converted into machine readable form so that it can be entered into the processing system. Entering data via a computer terminal and keyboard is one example of coding.


  1. a)   CLASSIFICATION: Classification involves categorizing data according to certain characteristics to make it meaningful to the user. For example, sales data can be grouped according to salesperson, product type, customer, or any other classification useful to management.
  2. b)  SORT: This involves arranging the grouped data element into a predetermined sequence to facilitate processing. For example, an employee number can be last. Sorting can be done on numbers, letters, special characters, or a combination of them. After it has been classified, data may be stored.
  3. c) CALCULATION: The arithmetical or logical manipulation of data is referred to as calculation. Examples include the computation of students’ grade–point averages, customers’ bank balances, and employees’ wages.
  4. d) SUMMARIZE: Reducing a large amount of data to a concise, usable form is called summarizing. The logical reduction of data is necessary to provide information that is useful.
  5. e) STORE: this involves the storing of data not immediately needed; data could be stored on a disk, tape or CD-ROM.


This involves retrieving data, printing data, and data communication.


In today’s increasingly competitive and technologically advanced society, the importance of data processing has grown significantly. With the exponential growth of data being generated from various sources, managing and making sense of this vast volume of information has become a critical challenge. The importance of data processing lies in its ability to transform raw data into valuable insights and meaningful information, addressing the need for timely, accurate, and actionable knowledge in our competitive world.

1. Efficient Handling of Large Volumes of Data: Data processing enables organizations to handle and manage large volumes of data effectively. As data continues to accumulate at an unprecedented rate, traditional manual methods of data analysis and management become impractical and time-consuming. Data processing techniques, powered by advanced computing technologies, allow for the speedy and accurate processing of vast datasets, enabling organizations to extract valuable information and insights efficiently.

2. Improved Decision-Making: Data processing plays a crucial role in supporting informed decision-making processes. By processing and analyzing relevant data, organizations can identify patterns, trends, and relationships, leading to insights that aid in strategic planning, risk assessment, resource allocation, and performance evaluation. Data processing allows decision-makers to access accurate and up-to-date information, enabling them to make well-informed choices that can drive business success and competitive advantage.

3. Enhanced Accuracy and Quality: Manual handling of data is prone to human errors and inconsistencies. Data processing techniques, on the other hand, can ensure a higher level of accuracy and data quality. By applying automated algorithms and validation processes, data processing can identify and rectify errors, inconsistencies, and missing values in the dataset. This enhances the reliability of the information generated, reducing the risks associated with decision-making based on flawed or incomplete data.

4. Timely Access to Information: In today’s fast-paced business environment, timely access to information is critical for effective decision-making. Data processing allows for the rapid retrieval, analysis, and dissemination of relevant information. Real-time or near-real-time data processing capabilities enable organizations to respond quickly to changing market conditions, customer demands, and emerging opportunities. Timely information empowers decision-makers to act proactively and seize competitive advantages.

5. Automation and Efficiency: Data processing automates repetitive and time-consuming tasks, enabling organizations to achieve greater operational efficiency. Through automated data entry, sorting, filtering, and analysis processes, valuable human resources can be redirected to more strategic and value-added activities. Data processing reduces manual effort, minimizes human errors, and streamlines workflows, resulting in improved productivity and cost savings.

6. Data-Driven Insights: Data processing facilitates the generation of data-driven insights that help organizations gain a deeper understanding of their operations, customers, and markets. By analyzing patterns, correlations, and anomalies within the data, organizations can uncover valuable insights that can drive innovation, optimize processes, improve customer experiences, and identify new business opportunities. Data processing empowers organizations to harness the power of data to gain a competitive edge and stay ahead in dynamic and evolving markets.

7. Compliance and Regulatory Requirements: Data processing plays a crucial role in ensuring compliance with various regulations and legal requirements. Many industries, such as finance, healthcare, and telecommunications, have strict guidelines regarding data privacy, security, and reporting. Data processing enables organizations to handle sensitive data in a secure and compliant manner, ensuring that information is processed and stored in accordance with applicable regulations.

8. Personalization and Customer Insights: Data processing enables organizations to personalize products, services, and experiences based on customer preferences and behavior. By analyzing customer data, organizations can identify individual needs, preferences, and purchasing patterns, allowing for targeted marketing campaigns, personalized recommendations, and customized offerings. Data processing provides valuable customer insights that help organizations build stronger relationships, enhance customer satisfaction, and drive customer loyalty.

9. Business Intelligence and Analytics: Data processing is a fundamental component of business intelligence and analytics initiatives. By processing and analyzing data, organizations can gain valuable insights into market trends, customer behavior, operational performance, and financial indicators. These insights enable organizations to make data-driven decisions, identify areas for improvement, optimize processes, and uncover new business opportunities.

10. Risk Management and Fraud Detection: Data processing plays a vital role in risk management and fraud detection. By analyzing patterns, anomalies, and historical data, organizations can identify potential risks, fraud patterns, and security breaches. Data processing techniques, such as anomaly detection and predictive analytics, enable organizations to detect and mitigate risks, prevent fraudulent activities, and safeguard their operations and assets.

11. Scientific Research and Innovation: In scientific research and innovation, data processing is essential for analyzing experimental data, conducting statistical analyses, and drawing conclusions. Researchers can use data processing techniques to identify correlations, test hypotheses, and uncover new scientific insights. Data processing also supports innovation by providing researchers with valuable data resources for developing new technologies, products, and solutions.

12. Operational Efficiency and Cost Reduction: Data processing helps organizations streamline their operations, leading to increased efficiency and cost reduction. By automating data-related tasks, organizations can eliminate manual errors, reduce processing time, and optimize resource allocation. Improved operational efficiency not only enhances productivity but also contributes to cost savings and better resource utilization.

13. Performance Measurement and Evaluation: Data processing enables organizations to measure and evaluate their performance against predefined metrics and key performance indicators (KPIs). By analyzing relevant data, organizations can assess their progress, identify areas of improvement, and make data-driven adjustments to achieve strategic objectives. Data processing provides organizations with the insights needed to monitor performance, track milestones, and make informed decisions for continuous improvement.

14. Forecasting and Planning: Data processing supports forecasting and planning activities by providing organizations with historical data, trends, and predictive insights. By analyzing past data, organizations can forecast future demand, anticipate market trends, and make proactive decisions to optimize resource allocation, production, inventory management, and overall business planning.

15. Collaboration and Knowledge Sharing: Data processing facilitates collaboration and knowledge sharing within organizations. By processing and organizing data, employees can access accurate and relevant information, promoting informed discussions, collaboration, and knowledge exchange. Data processing also enables the integration of different data sources, fostering a holistic view of information and facilitating cross-functional collaboration.

Data processing is crucial for compliance, personalization, business intelligence, risk management, research, efficiency, performance measurement, forecasting, collaboration, and overall strategic decision-making. By harnessing the power of data processing, organizations can unlock valuable insights, optimize operations, and gain a competitive advantage in today’s data-driven landscape.

In conclusion, the importance of data processing lies in its ability to handle, analyze, and transform large volumes of data into valuable information and insights. By leveraging advanced computing technologies, data processing enables organizations to make informed decisions, improve accuracy and efficiency, access timely information, and gain a competitive advantage in today’s data-driven world.

Read also:

Data and Information

ICT Application in Everyday Life

Digitalization of Data II

Digitalization of Data

Classification of Computers By Size

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