data processing

What is data processing?

Data processing refers to essential operations executed on raw data to transform the information into a useful format or structure that provides valuable insights to a user or organization. The outcomes of data processing operations flow into various data outputs as designed by a data scientist, including data analytics, business intelligence, machine learning (ML) and artificial intelligence (AI).

With data processing, raw data is collected from various data input sources and means, from real-time data streaming to more static batch processes from which data is collected and processed at specific intervals.

The raw data is transformed or processed in a series of steps that validate, format, sort, aggregate and store data. The term big data is used when particularly large volumes of data are processed. The raw data that is processed can be pulled from a data lake, and the output can land in a data lake, data lakehouse, data warehouse or database.

Data processing, a broad topic, intersects with the related concepts of data preprocessing and data preparation. Both options commonly occur before an actual data processing activity, and both ensure proper data collection to enable the most effective data processing operations.

6 steps of the data processing cycle

The data processing cycle commonly includes these six primary steps in the following order:

• Collection. The first step of the data processing cycle involves gathering raw data from various sources. The collection process seeks complete, accurate data strictly relevant to the objectives of the processing task.
• Preparation. Once collected, data undergoes data cleansing, also called data cleaning or data scrubbing. as well as organization. During preparation, potential errors and duplications are removed to prepare quality datasets.
• Input. The collected and prepared data is entered into a processing system, either manually or through automated methods such as a data import operation.
• Processing. At this stage, the entered data is transformed, analyzed and organized using various techniques to produce meaningful information. This could involve calculations, filtering, sorting and other operations depending on the desired outcome.
• Output and interpretation. The processed data is presented in a readable and interpretable format, such as graphs, tables or documents. This stage might also involve interpreting the data to extract insights and knowledge.
• Storage. The final step involves storing all the processed data and metadata, leaving it accessible and ready to use.

Types of data processing

Data processing can be categorized into several types based on the method and technologies used. Manual data processing, the most basic form of data processing, involves humans collecting, filtering and organizing data without a machine or electronic device. On the other hand, electronic data processing (EDP) means data is collected, organized, processed and stored digitally.

There are different types of EDP-based processing types, including the following:

• Batch processing. With batch processing, data is collected and processed at predetermined times.
• Distributed processing. In this approach, data processing tasks are distributed across multiple interconnected systems to handle large demands, such as the requirements of big data.
• Multiprocessing. In a multiprocessing or parallel processing approach, multiple CPUs and process threads complete data tasks simultaneously, rather than processing on a single CPU or process thread.
• Real-time processing. In a real-time processing approach, data is collected and processed when received.
• Stream processing. Stream processing is sometimes used interchangeably with real-time processing, though there is a difference between the terms. Stream processing includes data streaming a continuous, high-speed data transfer process.

Examples of data processing

Data processing spans myriad industries and applications, reflecting both its versatility and fundamental role in digital operations. The following examples illustrate the critical importance of data processing:

• Digital marketing. A digital marketing organization uses demographic data to strategize and tailor marketing campaigns. By effectively processing the data, the company identifies target audiences, understands their preferences and optimizes marketing efforts for better engagement and conversion rates.
• Financial transactions. Many financial transactions, such as bank transfers, online payments or stock trades, rely on data processing.
• Navigation systems. Real-time processing is crucial for GPS navigation systems, which rely on the immediate processing of satellite data to provide turn-by-turn directions.
• Supply chain optimization. Data processed from various supply-chain points identifies bottlenecks, predicts demand and optimizes production logistics.
• Weather forecasting. Multiprocessing, or parallel processing, is employed in weather forecasting, during which data from sources like satellites or weather stations are processed simultaneously. This approach enables rapid analysis of complex meteorological data to predict weather conditions accurately.

Data processing analytics

While data processing transforms raw data into something usable, data analytics is often the critical technology for interpreting the meaning of data patterns. Data processing combined with analytics leads to fact-based decisions.

Data analytics goes beyond preparing and organizing data. It involves scrutinizing datasets to find trends, draw conclusions and make predictions based on their information. It involves applying statistical analysis and modeling techniques to already-processed data to uncover insights that foster informed decision-making.

The future of data processing

These innovative trends and technologies are shaping the future of data processing:

• Cloud computing. Data processing increasingly occurs in the cloud as organizations adopt cloud computing instead of running all resources on-premises. The emergence of serverless computing and function as a service also simplifies and optimizes data processing tasks in the cloud.
• Edge computing. Another trend impacting data processing is edge computing, driven by internet of things device usage and the deployment of fifth-generation wireless (5G) communications. Edge computing processes data closer to its source, reducing latency and bandwidth and enabling real-time processing capabilities.
• ML and AI integration. The integration of ML and AI with data processing technologies is accelerating. This integration allows the automation of data analysis, predictive modeling and decision-making processes.
• Privacy-preserving data processing. With rising concerns over data privacy and the tightening of regulations, technologies that support privacy-preserving data processing are increasingly important.