Data Processing & Data Processing Stages

What is Data processing?

Data processing is a process of processing raw data and converting it into meaningful information. This process takes the data as input from users; processes it and gives desired results as meaningful information.

Data

It’s a raw or unprocessed material which includes facts, figures, numbers, special symbols etc.

Process

It's a run time entity which takes inputted data as input and processes it; in processing it purifies data by removing erroneous records, incomplete or partial entries from data, inconsistent entries, removing unnecessary records which are not required in the final data set etc. It's a data preparation phase where a user processes data and makes it for use.

Information

It's processed data or an outcome or results of Processed data.

Data Processing Stages

Data processing consists of the following stages−

• Data Collection − The collection of data refers to the gathering of data from different available resources. The gathered data should be defined and accurate. The main goal of data collection is to analyse it to find hidden patterns and data insights which are very useful instrategic decision-making. Hence, processed data can be analyzed and used in decision-making, drive improvements, or generate insights for business, science, healthcare, education, and more. All the results are further stored for future reference. Data collection methods include surveys, interviews, observations, experiments, sensor readings, and others. In today’s modern digital age; generally, a user uses sensors, cameras, and software to collect data automatically. This data can include demographics, customer preferences, environmental measurements, and commercial transactions.
• Data Preparation − Data preparation also known as data pre-processing is a process of constructing a final dataset from different sources for future use. Data preparation encompasses data cleaning, data transforming, and organizing it in such a way that it can be used for further analysis. It ensures data accuracy, and data consistency, and makes it to generate insights by uncovering hidden patterns. Generally, Data preparation includes −
  • Data Cleaning − this process involves identifying missing values, duplicate records, or outliers from data. After identifying erroneous records; experts can correct them by filling a standard average value or data into missing values, de-duplication, and outlier treatment by framing strategies like removal, transformation, and imputation.
  • Data Transformation − Data Transformation can be done by converting data types, adding or deleting suitable columns; and encoding categorical variables into a numerical for machine learning models.
  • Feature Engineering − Identifying new features or modifying existing ones from data to make it fit for machine learning models.
  • Data Integration − It includes merging or joining datasets to create a single, unified dataset for analysis.
  • Data Reduction − By reducing large amounts of data in size or dimensionality reduction to simplify the operations and enhance computational efficiency.
  • Data Formatting − The prepared data can be formatted for analysis or modelling.
• Input − Input refers to inputting data to the system for processing. It can be fed into a computer through standard input devices like a keyboard, scanner, mouse, etc. Sometimes data inputting can be manual, automated, or a combination of both.
• Data Processing − Data Processing refers to the process of processing a prepared data set. In this stage, raw facts or data are converted to meaningful information. Data processing includes computation, logical operations on data, sorting & filtering, conditional statements, and applying algorithms and models, tools and techniques to process the data.
• Output and Interpretation − In this process, output can be obtained in terms of text, audio, video, etc. Once data is processed, the resulting information can be presented as output on output devices such as monitors, printers, speakers, and other display devices used to convey the processed data to users. Interpretation of output provides meaningful information to the user.
• Storage − In this process, we can store data, instructions and information in permanent memory for future reference. Once a user gets output from processed data; it stores it permanently for future reference. It can be stored in databases, files, or other organised formats as needed. Effective data storage ensures data integrity, security, and accessibility.

Types of Data Processing

Some common data processing systems are as −