What Is Data Processing? Definition and Stages

Effective data processing is a critical aspect for companies to gain access to valuable insights and maintain a competitive edge. Therefore, understanding the importance of processing data as per best practices can help unlock new opportunities for growth and success.

What is Data Processing?

Data processing involves transforming raw data into valuable information for businesses. Generally, data scientists process data, which includes collecting, organizing, cleaning, verifying, analyzing, and converting it into readable formats such as graphs or documents. Data processing can be done using three methods i.e., manual, mechanical, and electronic.

The aim is to increase the value of information and facilitate decision-making. This enables businesses to improve their operations and make timely strategic decisions. Automated data processing solutions, such as computer software programming, play a significant role in this. It can help turn large amounts of data, including big data, into meaningful insights for quality management and decision-making.

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Six Stages of the Data Processing Cycle

The data processing cycle outlines the steps that one needs to perform on raw data to convert it into valuable and purposeful information. This process involves the following six stages:

Data Collection

Data is gathered from reliable sources, including databases such as data lakes and data warehouses. It is crucial that the data sources are accurate, dependable and well-built to ensure that the data collected, and the information gathered is of superior quality and functionality.

Data Preparation

The data collected in the first stage is then prepared and cleaned. In this stage, also referred to as “pre-processing”, the raw data is organized to assist in implementation of further stages. Data cleaning or data preparation involves eliminating errors, removing noise and eliminating bad data (inaccurate or incorrect data) to sort it out into high-quality data.

Data Input

This is the stage in which raw data starts to take an informational form. During this stage, clean data is entered into a system or destination (such as a data warehouse like Astera data-warehouse Builder or CRM like Salesforce). This is done by translating it into a language, that the system can understand, either manually or through input devices set up to collect structured or unstructured data.

Data Processing

This stage involves processing data for interpretation using machine learning algorithms, and artificial intelligence algorithms. The actual process may differ based on the source of data (data lakes, social networks, connected devices), and its intended use or purpose (deriving patterns and trends, determining solutions or strategies, and optimization).

Data Output

In the data output stage, also referred to as data interpretation stage, the processor translates and presents data in a readable data format such as documents, graphs, images etc. Now the data is usable by all members of the organization, and not only data scientists, to help them in their respective data analytics projects.

Data Storage

This final stage of the cycle involves storing the processed data for future use. This step takes place after using the information required for immediate implementations and insights. In this stage, organizations store data for reference purpose or to allow easy and quick access to members of the organization for future use.

Types

The following types are differentiated based on the source of data and the steps taken by the processor. Each type serves a different purpose, and its implementation is highly dependent on the available raw data.

• Batch processing: The system breaks down a large amount of data into smaller units/batches before collecting and processing it.
• Real-time processing: It typically involves processing and transferring data as soon as the system obtains it, to assist in rapid decision-making.
• Online processing: It involves automatic data processing by automatically entering it through an interface as soon as it becomes available.
• Multiprocessing: Breaking one computer system into smaller processors to distribute data processing among them, while ensuring coherent execution. Data engineers also refer to this as parallel processing.
• Time-sharing: Allowing multiple users to access the computer system simultaneously, to execute the process.

The Future of Processing

The future of this innovation revolves around the concept of cloud. Cloud technology allows electronic data processing methods which accelerates its speed, efficiency and effectiveness. Therefore, it helps results in timely, higher-quality analytics. This means each organization now has more data to utilize and increases the number of valuable insights extracted.

Cloud computing allows for a seamless way for companies to not only implement these stages, but to also upgrade them by incorporating innovative changes and updates. Big data cloud technologies allow for companies to combine all of their platforms into one easily-adaptable system.

Large corporations are not the only ones who benefit from cloud technology. In fact, small companies can reap major benefits of their own. It offers immense scalability to companies without incurring hefty prices.

The exact IT innovations that created big data and its associated challenges have also provided us with the solutions. The cloud can handle huge workloads that are a major characteristic of big data operations.

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Automated data processing is the way forward as its manual counterpart has become redundant due to innovation in technology. It allows for sustainable solutions with reduced chances of errors, execution time and investment.

Businesses are now relying more on quality data. With the passage of time, this need for quality data will keep on increasing. Data automation streamlines your business operations by removing repetitive manual tasks and enables you to focus on business growth. Automated data processing further helps business users make critical business decisions promptly in real-time.

Astera Centerprise utilizes technology that accurately and efficiently prepares, cleanses, validates, and stores data. It enables faster innovation and availability of reliable data at each step. Centerprise allows data automation through job scheduling by creating data maps and automating them on triggers or actions.

With Centerprise, users save time and resources by letting our software automate all their repetitive tasks. It allows you to set up dataflows that transform and migrate data from a source to the desired destination. Learn how this automated solution can help you extract quality insights for business improvement.

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