What Is Data Processing? (With Types, Stages And Uses)

Updated 30 September 2022

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Data is often available in a form that makes its analysis complicated. With the help of data processing, professionals can give raw data a functional format, making it easier to derive insights. Learning about data processing can help you understand the process of transforming extensive data sets into well-presented information. In this article, we answer the question "What is data processing?", outline types of data processing, explain its stages, describe its methods and discuss its possible uses.

What Is Data Processing?

The answer to "What is data processing?" is that it is a method that helps data scientists to take raw data as input and produce information as output. In the context of data processing, data refers to numbers or characters that constitute measurements from the real world. Once professionals have measured the information present in multiple data, they can derive useful information algorithmically and deduce it statistically. This information may serve as a solution to different organisational problems like high data management costs or inefficient supply chain processes.

For instance, if a business has gathered data relative to its operations, the next step is to develop this data into meaningful and easy-to-access presentations for the management. Executive managers can use this structured information to make decisions that may increase revenue and decrease the loss. To convert data into organised information, organisations typically use data processing applications. This data may include operational or transactional details and sales, inventory or payroll information.

Stages Of The Data Processing Cycle

Here are the stages of the data processing cycle:

Collection

The first step to processing data is to collect it. The collection of data typically involves accessing existing databases or utilising a new database. Once you have collected the data, you can convert it into a functional format to process it.

Preparation

In the preparation phase, you clean the data and verify it for accuracy. This typically involves evaluating the data and recognising potential errors, duplicate entries and inconsistent details. By resolving redundancy, you may enhance the integrity of the data before you process it, generating unambiguous results.

Input

After recognising and eliminating the data points that notably differ from meaningful information, you can enter the clean data into a data processing tool. This may be a spreadsheet software tool or a processing system. Putting the data into processing tools enables you to begin data processing.

Processing

Data processing typically requires manually manipulating and analysing the data. It can also involve automation, for which you can use different functions of a data processing application. You can set goals for processing the data or let the processing system work with the data using its default settings.

Output

A data processing system transforms data into highly presentable information. The information may include reports, forecast data, macroeconomic data, charts and graphs. In this stage, professionals who are not data scientists begin interacting with this processed information.

Storage

Once you have the information as output, you may choose different options to store it. An organisation may use this data in the future to make critical decisions. Storage allows the organisation to access the information at any time and proceed to the interpretation phase.

Types Of Data Processing

Below are some common types of data processing:

Batch processing

A batch processing approach allows you to process large groups of data simultaneously. This may involve storing the data and processing it at a set time. While this method may not provide instant results from processing information in real time, it offers an efficient way to process large amounts of data when timelines are not a concern.

Distributed processing

Distributed processing happens when information is present on multiple machines or servers. Professionals often use this approach when storing large amounts of data in a single machine is not possible or when it is necessary to use the information available across multiple devices. This method also offers fault tolerance, as data processing continues to function on other servers when one server malfunctions.

Multi-processing

In multi-processing, each processor functions within the same physical unit. In distributed processing, instead, there are multiple processors that expedite the process. A potential limitation of the multi-processing approach is that if one of the processors malfunctions, it may slow down data processing. This approach remains a trustworthy method for professionals working with sensitive information, as localised data on a single server is typically easier to protect.

Real-time processing

You can use real-time processing when you require instant results since it produces output as the system processes data. In this type of processing, a computer quickly processes data, skipping entries that have errors and continuing to process the next set of data. This approach may be efficient if you require quick results, although it may produce occasional errors in the data.

Transaction processing

Transaction processing is a real-time method ensuring that the information is accurate. Unlike real-time processing, which skips errors, transaction processing halts the data processing until you fix the error. You can include hardware or software components while designing your data processing system. These components can help you to deliver solutions and resume the process if the system encounters errors.

Data Processing Methods

Below are the three methods for processing data:

Manual

Professionals typically use the manual data processing method when they do not have access to mechanical devices or electronics. It is a less common approach, given the accurate and quick computation of modern-day systems. The manual method can still be useful in some cases. For instance, an electoral agency may utilise a hand count on ballots during civic body elections.

Mechanical

Mechanical data processing refers to using simple computing devices for processing data. Tools like calculators or typewriters are some examples of mechanical processing systems that convert information into a functional format. Mechanical data processing is a robust system that may enhance the efficiency of a project and reduce potential for errors.

Electronic

Modern data processing utilises sophisticated technology and advanced computers that help professionals to process information accurately. Organisations often prefer this method to increase the speed and capability of data processing. These advanced computing systems are adept at automating processes, reducing manual effort and the potential for errors.

Uses Of Data Processing

Here are some common uses of data processing:

Operations research

An organisation may use data processing to solve problems relating to the coordination or optimisation of its operations. Data processing offers a computerised arrangement that analyses varied activities of businesses. The management may utilise this assessment to improve the effectiveness of operations, enhance planning and guide decision-making processes. With meaningful information, the organisation can better understand and optimise its operations.

Route optimisation

With data processing, aviation or courier companies may find optimal routes that can save both time and money for the organisation and its customers. With the help of structured data, they may assess their existing route plans and deploy route finding algorithms to determine the most efficient routes. Data processing can help these service providers to evaluate data relating to their operations and allow them to develop robust navigation systems for their air or road networks.

Administration and management

A company may use a data processing system to manage its operations and employees better. With the help of processed data, an organisation may identify the factors resulting in a waste of resources, time and effort. For example, using computers can automate document production and reduce workforce costs. Data processing can also be a great tool for an organisation that intends to optimise its forecasting.

Resource planning

When organisations use data processing, they get access to meaningful information. They may use this data to analyse their resources and allocate them among various projects efficiently. With clear data points, they can understand how to improve the planning, allocation and distribution of resources. For example, they can monitor package deliveries to efficiently arrange for the delivery of goods between parties.

Sequencing of tasks

A company may use data processing to identify the order of steps for completing a task. This can lead to efficient prioritisation to help analyse workflow problems and cost flow issues. This assessment can help an organisation to determine the most optimal sequence of tasks and make decisions accordingly. With the help of data processing, the organisation can develop a workflow strategy with clearly defined steps that may increase its productivity and performance.