electronic data processing (EDP)
What is electronic data processing (EDP)?
Electronic data processing (EDP) refers to the gathering of data using electronic devices, such as computers, servers or calculators. It is another term for automatic information processing. It also involves analyzing data and summarizing and recording the output in a (human) usable form.
The concept of EDP has evolved from data processing, or DP. The term emerged in an era when most computing input was physically provided to a computing device, usually in the form of punch cards. With those applications, the output was presented either on punch cards or as a paper report.
Electronic data processing explored
In the 21st century, the volume of data generated daily is growing at an unprecedented pace. Some estimates suggest that the size of the global datasphere will be of the order of several hundred zettabytes -- 1 ZB = 1 trillion gigabytes -- within the next few years. Increasing digitization and the emergence of new technologies are contributing to this data explosion.
Data is widely considered the "new oil" since it creates numerous opportunities for learning, improvement and advancement -- especially for organizations. However, companies need a way to efficiently gather and glean insights from their massive data stacks. Manual methods are obviously inadequate to handle such voluminous data, but EDP can.
EDP provides a rapid and accurate method for data processing, data analytics and the presentation of results. Through the use of technology and automation, EDP systems enable business users to capture useful information and insights about their industry, market, customers and competitors.
Electronic data processing advantages
As noted, the main advantage of EDP systems is that they enable the rapid processing and analysis of large volumes of data. EDP tools also reduce the cost of paper document management and storage as they remove the need for physical storage locations, printing, couriering, etc.
Many EDP tools support user-friendly document search and streamline business workflows. Users can collaborate on projects and track the status of data. They can gain useful insights for their specific requirements in a format that makes the most sense to them.
EDP tools reduce the need for manual effort and also minimize the presence of redundant or bad data, which enables better enterprise decision-making. Finally, some EDP systems can store vast quantities of data and make it readily available for further analysis and presentation.
Elements of electronic data processing
EDP systems comprise four key elements.
Hardware refers to all the physical parts of the EDP system, including devices and peripherals. The most common digital devices used in EDP are the following:
- end-user computing devices, such as laptop computers, desktop PCs and smartphones, that can capture data and enable data entry;
- a central server that is required to support data processing and analysis; and
- audio and video devices that are used when data is to be captured in multimedia format.
In addition, EDP systems may also include the following:
- scanners to convert paper-based data into digital format;
- barcode scanners and point-of-sale systems to capture product pricing data for billing; and
- medical devices and sensors that collect patient data for diagnosis and treatment.
In EDP, software makes the hardware work and ensures that the expected output is produced. Different types of EDP software are available for various applications and business needs, including the following:
- data entry
- accounting and bookkeeping
- scheduling and time management
- inventory management
In addition to hardware and software, EDP involves procedures or steps for data collection, aggregation, conversion, sorting, analysis and reporting (see below).
The final element of EDP is personnel. Although EDP tools are designed to replace manual labor with automation to minimize human intervention, people are still required to use the systems and to apply the generated insights to business decision-making.
Steps or procedures in electronic data processing
The EDP process includes multiple steps or procedures.
In an EDP system, the data may be collected from multiple sources. These sources must be trustworthy and yield high-quality data to maximize the system's usefulness and value. Advanced EDP tools are capable of pulling massive amounts of data from large data lakes and data warehouses.
Data preparation and conversion
Raw data must be cleaned up and organized. An EDP system checks and removes errors in the data and removes redundant or incorrect data that may affect the output quality. If the data is not in an acceptable format for processing, the system converts it into the right format before sending it on to the next stage.
The clean, organized and properly formatted data is fed into the destination application in terms that the application -- e.g., a customer relationship management or enterprise resource planning platform -- can understand. The data is then ready for processing and transformation into usable information.
Advanced EDP systems may use machine learning algorithms to process and interpret data. Older systems may use other, less advanced means of data processing. The actual processing methodology usually varies depending on the data type, source and intended use case.
The processed data is translated and converted into a human-readable form, like graphs, text, images, etc. The conversion enables human users to draw conclusions from the data, without requiring any specialized technical expertise.
Data storage is the final step in EDP. The processed and converted data is stored on media for future use. In many industries, compliance regulations mandate the proper storage -- and, in some cases, encryption -- of data.
Electronic data processing methods
EDP is a fairly broad term that encompasses many different methods, depending on how the data is processed and presented. The following are some of the most common EDP methods.
In this method, many nodes or user terminals are connected to a central computer. In theory, each user can access the central processing unit (CPU) along with all other users at the same time. In practice, they are allocated a "time slice" of the CPU in a round-robin sequence. The EDP system's multiuser operating system controls how much time each user gets.
This method provides accurate and up-to-date information since the data is processed as soon as it becomes available. The computer processes all incoming data in real time and generates instantaneous or near-instantaneous output.
Multiprocessing EDP involves the processing of multiple tasks at the same time using different processors simultaneously on the same computer. Since there can be more than one independent CPU, processing can be fast.
Multitasking -- also known as multiprogramming -- involves different processors working at the same time. Various tasks share the same processing resource to enable parallel processing to produce results in less time.
In this method, data is collected in a batch process over a specified time period, and then all the data is processed at the same time.
This EDP method involves distributing processing tasks to multiple computing devices that are physically distinct but linked electronically for data transmission and exchange. ATMs are an example of distributed processing EDP.
Electronic data processing applications
Two of the most common applications of EDP are in inventory stock monitoring and supply chain logistics systems. Modern-day retail and e-commerce supply chains are extremely long and complex, due to the number of elements in the supply chain and the vast quantities of data generated from start to end.
The data must be efficiently captured to ensure that orders are fulfilled on time and the sales pipeline keeps moving. EDP systems enable the seamless flow of data to streamline supply chain operations and smooth the interactions between the various moving parts of the chain.
Other industries and sectors where EDP has many applications and use cases are the following:
- telecommunications and electronics
- healthcare, pharmaceuticals and clinical research
- hospitality and tourism
- financial services
- law and order
- natural sciences, e.g., paleontology and geology
See also: big data, data management, data scientist, data visualization and data aggregation.