DP (Data Processing)

What is DP?

Data processing is a technique in which raw data is converted into useful information that organizations can use to run their business successfully. The team of data scientists is responsible for data processing. Raw data is provided to the data scientists and they follow the data processing life cycle to complete the task.

DP Lifecycle

DP lifecycle consists of six processes and they are discussed below in detail.

Collection

Data collection is the first step in the data processing life cycle. There are many sources in an organization from where raw data can be collected. Raw data may include monetary figures, profit and loss statements, user behavior, website cookies, different departments, and many more.

Preparation

This is the second step in the data processing lifecycle. This is a process in which duplicate data is removed and data is sorted and organized. Data may also have miscalculations and inaccurate information. All these things have to be removed to make the data perfect for analysis. This process improves the quality of the data.

Input

This is the step in which the organized data is converted into a format that can be read by a machine. This format is then sent to the processing unit with the help of a keyboard, scanner, and other input sources.

Processing

In this step, data is processed with the help of artificial intelligence and machine learning algorithms. This process helps in generating the quality output. The sources of data processing can be databases, connected devices, and many more.

Output

The output of the data can be available in different forms like tables, graphs, vector graphics, video, audio, documents, and many more.

Storage

The data is stored so that it can be used further and processed more if needed.

Types of DP

Data processing is of many types and we will discuss them here in detail.

Batch Processing

Batch processing is a process in which a large amount of data is processed. In this process, the data is stored and a time is set for its processing. You will not get instant results through this process but if there is no deadline, you can use this efficient way to process a large amount of data.

Distributed Processing

Distributed processing has to be done if the raw data is present on different machines or servers. If a large amount of raw data cannot be stored on a single computer, professionals use multiple computers to store the data. This is a great option because the failure of a server does not affect data processing on other servers.

Multiprocessing

Multiprocessing is a technique in which many processors are available in the same unit to process data. One of the drawbacks is that failure of a processor can slow down data processing. This method of processing is useful for those professionals who work on sensitive data and want it to be stored on a single system.

Real-Time Processing

Real-time processing is a method of processing data in which professionals can get the data instantly. They will get the data as soon as the processing is completed. The data is processed quickly and entries with errors are skipped. This type of result may have a few errors.

Transaction Processing

Transaction processing is a method of data processing in which accurate information is available. In this process, if an error is found, the processing stops till the time the error is removed or fixed. The data processing system can be designed while including hardware and software.

Methods of Data Processing

There are three methods which can be used to process data and we will discuss them here.

Manual

Manual data processing is a method which can be adopted by professionals who do not have any electronic device This method is not efficient as it may have a few errors. Such a method can be used in elections in which voting has been done by using a ballot.

Mechanical

Mechanical data processing can be done with the help of simple computing devices which can process data. Calculators and typewriters are a few such mechanical devices which can be used for data processing.

Electronic

This is the common method in which advanced technology is used for data processing. Some of the tasks can be automated to reduce the workload. This method is preferred by many organizations as data is processed at a fast speed.

Data processing output formats

There are various formats in which the output of the data is generated. These formats are discussed here.

Simple text files

These files can be interpreted easily as they are one of the basic output formats. Saving these files takes up very little space on the disk. Their processing is also easy.

Spreadsheet

Spreadsheet is used if the data is numeric and a lot of calculations have to be done. Data sorting, filtering, calculating, etc. can be easily done.

Charts and Graphs

Many software applications can be used to create charts and drafts as per the data available. This output is beneficial if professionals want to show growth or decline in productivity, profits, etc. Different types of charts and graphs are available which can be chosen to display the data.

Maps, vector, and image files

You can also show data in the form of maps, vectors, and image files. Maps can be used if you want to show directions. You can also use images and vectors to explain the output. Such an output is useful for scientists, foresters, climate scientists, and many more.

Advantages of DP

Data processing has many advantages and some of them are listed below −

• Productivity is increased and that leads to an increase in profit

• Making business decisions is easy

• Operational costs are low

• Reporting, storing data, and distributing is done easily and in less time

• Data accessibility is improved

Conclusion

Data processing is a technique in which raw data is processed and the output is used by organizations to make further decisions. Data processing helps organizations to make better decisions to run the business smoothly. Data scientists are hired to process raw data into useful information.

FAQs

FAQ 1: What is the future of data processing?

Ans: Data processing can be done by using different technologies which are being improved. This data processing is helpful for organizations to increase productivity and profits. The future of data processing is great.

FAQ 2: How many steps are included in the data processing lifecycle?

Ans: The number of steps included in the lifecycle are collection, preparation, input, processing, output, and storage.

FAQ 3: Why data processing is important?

Ans: Data processing is important as it improves decision-making ability. Reports are generated at a fast speed.

FAQ 4: How many types of data processing methods are there?

Ans: Manual mechanical, and electronic are the three methods of data processing

FAQ 5: In which formats do we get output

Ans: We get output in the form of text, images, graphs, maps, etc.