Data processing: meaning, definitions and examples

When referring to the broad, general handling and organization of data, often automated or done by computer systems.

• The software is designed for efficient data processing.
• Data processing can help in organizing large volumes of information.

When focusing on examining and interpreting data to uncover patterns, insights, or trends, often used in research or business contexts.

• After data analysis, we discovered new market trends.
• Data analysis revealed that our sales increased by 20% last quarter.

When specifically talking about altering, organizing, or transforming data, either ethically or unethically. It can have a negative connotation if it implies unethical changes.

• Data manipulation techniques are sometimes necessary for cleaning datasets.
• Accusations of improper data manipulation damaged the company's reputation.

When discussing the handling and management of information in a general sense, often including cognitive processes and human activities.

• Information processing is critical in decision-making processes.
• The human brain excels at information processing.

Handling and organizing raw data into an understandable format. Typically involves sorting, cleaning, and converting data into a usable format.

• Data processing is crucial for ensuring the quality of information before analysis.
• The team spent hours on data processing to prepare the datasets for the meeting.

Examining datasets to draw conclusions and insights. Involves techniques like statistical analysis, data mining, and machine learning.

• Data analytics helped the company identify trends and boost sales.
• With advanced data analytics, we can predict customer behavior more accurately.

Making sense of analyzed data, often by explaining its significance, drawing conclusions, and making recommendations. This is the step where findings from data analytics are translated into actionable insights.

• Data interpretation revealed that customer satisfaction has improved significantly.
• The manager focused on data interpretation to provide a clear report to stakeholders.

Changing the format, structure, or values of data to meet the requirements of a particular analysis or system. Typically part of the ETL (Extract, Transform, Load) process.

• Data transformation was necessary to integrate information from different sources.
• The data transformation process included converting text data into numerical values for analysis.

Typically used when referring to the general act of taking raw data and turning it into usable information. It is often used in contexts like data storage, retrieval, and systematic organization.

• The data processing pipeline ensures that raw data is cleaned and structured before analysis.
• Data processing is essential for making sense of the vast amounts of information generated daily.

Refers to the modification, transformation, or moving of data. It involves actions that change the data's structure or content and can sometimes have a negative connotation if it implies unethical alterations.

• Data manipulation techniques are crucial for preparing datasets for machine learning models.
• Careful data manipulation can reveal new insights from existing datasets. Beware of unethical data manipulation to avoid misleading results.

Specifically used for tasks that require mathematical or algorithmic operations on data. It is often found in technical contexts involving extensive calculations or simulations.

• High-performance computers are used for data computation in scientific research.
• The algorithm performs complex data computations to predict future trends.

Generally used to describe the act of taking data and producing meaningful information from it, with a focus on the flow and handling of information rather than the computational aspects.

• Information processing is a critical element in decision-making systems.
• The brain's information processing capabilities are far more advanced than any computer.