What Is Data Processing? Methods, Steps, Types, Cycle And Examples

Whether you use the internet to place a food order, do financial business, or study more about a certain topic, data is produced every single second. The increase in data has been attributed to the usage of social networking, online commerce, and video streaming services. Every person on Earth will generate 1.7MB of data every second, according to a Domo study. In order to utilise and get insights from such a huge number of data, data processing is required. In this article, we will delve into the following data processing methods, data processing types, and data processing cycles. You will achieve mastery over these with top data science courses and certifications. So let us read on.

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What Is Data Processing?

Data in its raw form cannot be useful to any company. It is the process of collecting unprocessed data and turning it into useful information. It is frequently carried out step-by-step by a team of data engineers and data scientists within a business. Before being made readable, the raw data is collected, sorted, processed, reviewed, and stored.

Data processing cycles are essential for firms to enhance their corporate strategy and obtain a competitive edge. By transforming the data into usable representations such as charts, graphs, and texts, employees across the organisation can understand and use it. Let us look at the data processing cycle now that we have defined what we mean by data processing.

Data Processing Cycles

Here is the explanation of what is data processing cycle. In order to produce usable insights, raw data/information (input) is fed into the application in a sequence of phases known as the data processing cycle (output). The steps are performed in a specific order, yet the process is looped back on itself. The result of the first information processing cycle could be preserved and utilised as the input for the subsequent cycle, as indicated in the figure below.

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Data Processing Cycles: Steps

Below are the steps to explain data processing cycle.

Step 1: Collection

The collection of raw data is the first step in the data processing cycle. The findings produced are significantly influenced by the kind of raw data that was collected. Therefore, raw data should be obtained from well-defined and accurate sources in order for the resulting findings to be trustworthy and useful. Examples of raw data include financial information, cookies from websites, corporate profit and loss statements, user activity, and the like.

Step 2: Preparation

Data preparation, also referred to as data cleaning, is the process of filtering and sorting raw data to eliminate false and inaccurate information. This is one of many data processing steps and here raw data is checked for mistakes, duplication, computation errors, and missing data before being further analysed and processed. To ensure that the processing unit only receives the finest data, this is done.

This step of data processing tries to eliminate faulty data in order to begin developing high-quality information that can be used in the best way for business intelligence (incomplete, redundant, or erroneous data).

Step 3: Input

In this stage, the raw data is converted into a machine-readable format and transmitted to the processing unit. Data entry using a keyboard, scanner, or another input device is possible. This is one of the most crucial steps in data processing methods.

Step 4: Data Processing

This step involves transforming the raw data into a machine-readable format and sending it to the processing unit. It is possible to enter data via input devices such as scanners, keyboards, and the like.

Step 5: Output

A comprehensible format, such as papers, tables, vector files, graphs, music, and video is then provided to the user after they have received the data. This output can be stored and processed further during the next round of data processing cycles.

Step 6: Storage

During the storage stage of the data processing cycle, data and metadata are saved for later use. As a result, data can be easily accessible, recovered, and used as input right away in the future data processing cycle. This is one of the most important and final steps involved in data processing.

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After learning about what are the steps in data processing, we can now look at the types.

Data Processing Types

There are numerous forms of data processing, depending on the data source and the steps the processing unit takes to produce a result. Processing raw data cannot be done using a single, all-encompassing strategy.

What is Data Processing: Data Processing Methods

There are three basic data processing stages: mechanical, electrical, and manual.

Manual Data Processing

This is one of the Data processing types where manual labor is used to process this kind of data. The entire process of information gathering, calculating, filtering, sorting, and other logical operations is carried out manually without the use of any other technological apparatus or automation software. Although this step in data processing is a cheap approach with little to no equipment needed, there are some disadvantages, including high error rates, high labor costs, and a lengthy processing time.

Mechanical Data Processing

These Data processing types are where Data is processed mechanically using tools and machines. Simple tools such as printing presses, calculators, and typewriters can be included in this category. Simple data processing tasks can be carried out using this method. The increasing amount of information has made this procedure more difficult even though it has significantly fewer flaws than human data processing.

Electronic Data Processing

This is one of the Data processing types where Data processing programmes and software are used to process data using current technologies. A set of guidelines is provided to the software so that it may process the data and generate results. Even though this is one of the most expensive methods of data processing but provides the best results in terms of dependability and precision, as well as the quickest turnaround times.

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Examples of Data Processing

Now let us take a look at some examples of Data processing cycles. It occurs every day, whether or not we are aware of it. Here are some real-world instances of data processing:

• A stock trading application that uses millions of stock data points to make a straightforward graph

• An online store uses the search history of its customers to recommend relevant products to them.

• A digital marketing company designs location-specific advertisements utilising customer demographic data.

• Real-time sensor data is used by self-driving cars to recognise other vehicles and people on the road.

Moving From Data Processing to Analytics

Now that we have delved deeper into data processing cycles, let us move to analytics. If we were to pick just one thing to revolutionise the corporate sector today, big data would be it. Despite the astounding amount of information handling needed, the benefits are clear. Because of this, organisations need a strong data processing strategy if they want to stay competitive in the twenty-first-century market.

Analytics is the obvious next step after data processing and involves finding, examining, and displaying significant patterns in data. Data processing transforms data from one form to another, whereas analytics takes the newly processed data types and makes meaning of them.

Regardless of which of these approaches data scientists choose to use, the sheer volume of data and the analysis of it in its processed forms require greater storage and access capabilities.

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The Future of Data Processing

The best approach to describe the future of data processing cycles is through cloud computing.

The six steps of data processing still apply, but cloud computing has advanced data processing technologies dramatically, offering data scientists and analysts access to the fastest, most complex, most cost-effective, and most effective data processing methods available today.

Thanks to the cloud, businesses may merge various platforms into a single, user-friendly solution. Cloud technology enables the seamless integration of new updates and upgrades to legacy systems while offering enterprises tremendous scalability.

Additionally, affordable, cloud platforms serve as a great leveler between larger and smaller organisations. Therefore, the same IT developments that gave rise to big data and the problems that went along with it also provided a remedy. The cloud is capable of managing the enormous workloads that are typical of big data operations.

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Conclusion

Data comprises a variety of information for individual consumers, researchers, businesses, and institutions. To help with the understanding of the expanding amount of data produced every day, more data engineers and data scientists are needed. You may learn in the most effective environment possible with the help of Simplilearn's Data Engineering Certification Course, which was created in collaboration with IBM and Purdue University. Utilising IBM's extensive practical, industry-relevant training expertise and Purdue University's academic strength in the discipline, this programme will help you advance your career as a data engineering specialist.

Take a look at a thorough selection of online courses and certificates now that you have completed these data processing cycles. Along with online degree and certificate programmes, we offer free online courses. You will learn about their service providers, timeframe, costs, and the like.