Data Processing

-

Data Processing

M.SARULATHA
Assistant Professor,
Dept of CA,JJC

 

What do you mean by Data Processing?

          Data processing is the conversion of data into a useful and desirable form. This conversion or "processing" is done using a defined sequence of manual or automatic operations. Most of the processing is done using computers and is done automatically. The result or "generated" data can be obtained in a variety of ways.

          Examples of these types include a picture, graph, table, vector file, sound, charts or other desired format. The form obtained depends on the software or data usage method used. When done on its own it is called automatic data processing. Data center is a key component as it enables data processing, data storage, data access, data sharing and data analysis etc.

          Many businesses and forums need information to provide quality service. Collecting data and its results is the most important aspect of managing and verifying statistical accuracy. It is very important for services that deal with financial technology. This is so because the purchase and payment details need to be properly maintained so that they can be easily accessed by customers and company officials who need it. Processing is not limited to computers and can be done manually.

          While manual options use brain power and intelligence, electronic data processing techniques can save a lot of time and ensure smooth workflow and ensure deadline adherence. Accuracy is also high through electronic operation. One of the most important aspects of this is to ensure that the input data is stored for future use and sharing to save computational power and time. Data access, network security and data management systems are essential for any business.

Six stages of data processing

1. Data collection

          Data collection is the first step in data processing. Information is drawn from available sources, including data pools and archives. It is important that the available data sources are reliable and well-designed so the data collected (and later used as information) is of the highest quality.

 2. Data processing

          Once the data is collected, it goes into the data preparation phase. Data processing, often referred to as “pre-processing” is the stage at which raw data is processed and scheduled for the next phase of data processing. During preparation, raw data is actively tested for any errors. The purpose of this step is to erase bad data (unwanted, incomplete, or incorrect data) and begin to create the highest quality data for advanced business intelligence.

3. Data input

          Clean data is then entered on the go (perhaps a CRM like Salesforce or a data store like Redhift), and translated into a language you understand. Data entry is the first stage where raw data begins to capture data for usable information.

4. Processing

          At this stage, the data entered into the computer in the previous section is actually processed for definition. Processing is done using machine learning algorithms, but the process itself may vary slightly depending on the source of the data being generated (data pools, social networks, connected devices etc.) and its intended use (advertising patterns, medical diagnoses from connected devices, determining customer needs, etc. ).

5. Data extraction / interpretation

          The output / translation phase is the phase at which data is finally used by non-data scientists. Translated, read, and often in the form of graphs, videos, photographs, plain text, etc.). Members of a company or institution can now begin to generate data for data analysis projects.

6. Data storage

          The final stage of data processing is storage. After all the details have been processed, they are then stored for future use. While some details may be used immediately, many of them will serve a purpose later. Also, well-maintained data is a requirement for compliance with data protection legislation such as the GDPR. When data is properly stored, it can be easily and easily accessible by organization members when needed.

Comments

Post a Comment

Popular posts from this blog

Operating System Structure

-
Image
M.Sarulatha Monolithic System  or Simple System: A monolithic system structure refers to a software architecture where all components are tightly integrated into a single, self-contained unit. This means that the entire system is built as a single executable file, with all functionality and data stored within it. Characteristics of Monolithic Systems: 1. Tight Coupling: Components are heavily interconnected and interdependent. 2. Single Executable: The entire system is built into a single executable file. 3. Centralized Data: Data is stored within the single executable or in a centralized database. 4. No External Dependencies: The system does not rely on external services or components. Advantages of Monolithic Systems: 1. Simple to Develop: Easier to build and test, as everything is self-contained. 2. Fast Deployment: Quicker to deploy, as only a single executable needs to be deployed. 3. Easy Maintenance: Simpler to maintain, as all components are within a single codebase. Disa...
Read more

Asymptotic Notations

-
Image
Asymptotic Notations: Asymptotic Notations are mathematical tools used to analyze the performance of algorithms by understanding how their efficiency changes as the input size grows. These notations provide a concise way to express the behavior of an algorithm’s time or space complexity as the input size approaches infinity. Rather than comparing algorithms directly, asymptotic analysis focuses on understanding the relative growth rates of algorithms’ complexities. It enables comparisons of algorithms’ efficiency by abstracting away machine-specific constants and implementation details, focusing instead on fundamental trends. Asymptotic analysis allows for the comparison of algorithms’ space and time complexities by examining their performance characteristics as the input size varies. By using asymptotic notations, such as Big O, Big Omega, and Big Theta, we can categorize algorithms based on their worst-case, best-case, or average-case time or space complexities, providing valuable in...
Read more

ASP.NET Events Handling

-
Image
  ASP.NET  Events Handling M.SARULATHA Assistant Professor Dept. of CA,JJC.           ASP.NET provides important feature event handling to Web Forms. It let us to implement event-based model for our application. As a simple example, we can add a button to an ASP.NET Web Forms page and then write an event handler for the button's click event. ASP.NET Web Forms allows events on both client and server sides.           In ASP.NET Web Forms pages, however, events associated with server controls originate on the client but are handled on the Web server by the ASP.NET.           ASP.NET Web Forms follow a standard .NET Framework pattern for event-handler methods. All events pass two arguments: an object representing the object that raised the event, and an event object containing any event-specific information. Example: Creating Event Hand...
Read more