Data Processing - What Exactly Does It Mean?

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Data Processing - What Exactly Does It Mean? Data Processing
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Introduction

Data processing can sometimes be easily confused with data manipulation or data analysis, but it’s an important concept that shouldn’t be overlooked. Data processing means collecting and translating data into operable, helpful, and valuable information, which can be used to make business decisions. In fact, most business decisions are made with the help of data processing.

Data processing can be used in a number of different ways, but it essentially boils down to extracting information from raw data to produce insightful results. Whether you’re looking at your banking records or trying to locate and plan the most effective marketing strategies, data processing can take your business to the next level. In this article, we’ll be discussing exactly what data processing entails and how it can benefit you as an individual and your organization. Let’s get started!

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What exactly is data processing?

Data processing is the process of collecting and translating data into usable information. It refers to all of the tasks involved in turning raw data into usable information that can be acted upon. It’s an important step in any process, as it facilitates decision-making and increases the value of the information at hand. This data is used to improve business processes and make strategic decisions. 

Before any data can be processed, it must first be collected, which includes everything from inputting data into the database to scanning receipts at the point of sale. After collecting the data, it’s important to organize it so that you know what you have to work with and how to use it effectively in future operations.

Data processing can also include cleaning, verifying, enhancing, analyzing, and converting different types of data. All businesses will take raw data and convert it into usable information that can be used to make important business decisions. Some companies even add in extra steps like encrypting data or formatting it on certain devices to give their customers ease of use.

Data is everywhere

in our phones, laptops, tablets, cars, and even in our watches. With more data being created daily we have seen an increased need for companies to process all of the data to make sense of it all. As data becomes all over the place in modern society, a new field of study has emerged: data processing. 

According to statistics, approximately 90 percent of data created today is unstructured. This makes it incredibly difficult for companies to process and analyze. That’s where data scientists come in – they take raw data from all kinds of sources and clean up information from databases that contain personal information such as names or phone numbers. They will extract specific bits of information out of files without disturbing other parts and make that information readily available for those who need it.

Why do you need to process data?

When data needs to be processed, it has to be made ready for analysis or presented in a way that is meaningful to people. Generally, processing data involves cleaning it up and formatting it so that people can consume it.  

An enormous amount of data exists today: According to a report we create over 2.5 quintillion bytes of new information each day and most of it remains untouched by human eyes. In fact, as reported by Inc, 73% of all stored data is never analyzed. As our reliance on digital information grows, we’re going to need ways to process all the raw digital content quickly, accurately, and intelligently; otherwise our systems will become bogged down with too much extraneous information.

Data processing is a series of programmed steps that are carried out on data, whether it be structured or unstructured. Unstructured data like text messages and emails can’t just be run through a database system to extract important information, they need to be processed first to make sense of what they mean.

Six phases of data processing

  • Data collection

The first stage in data processing, data collection is all about getting a hold of raw information. It should be collected from accurate and reliable sources. 

  • Data preparation

Before doing anything with the data, It first needs to be prepared or cleaned. Data preparation is about removing noise and formatting your data in a way that makes sense for downstream analysis. In other words, sorting out the collected raw data.  

  • Data Input

This step involves getting the raw data into a digitally readable format. Getting data into the system is usually a top priority. This could be done in any number of ways – manually or other types of input devices that collect structured or unstructured data. 

The biggest consideration at this stage is accuracy and quality – are you sure that what’s coming in is clean and can be trusted to perform analysis on?

  • Data Processing

In this stage, data is processed for interpretation. Raw data is processed using machine learning and artificial intelligence algorithms.

  • Data Output

Ultimately, the data is transferred and presented in a readable format to the user such as documents, graphs, files, etc.

  • Data Storage

The last phase of data processing is a storage of the processed data. After data is transmitted and displayed, the data is stored for future use and references. 

The types of data processing

There are multiple types of data processing processes available to select from based on your unique situation, but understanding the basics of each one will make it easier to determine which one or ones are most appropriate for your particular needs. 

  • Batch Processing

Batch Processing means processing data in batches. It involves processing large amounts of data as a single unit, like once per day or once per month. 

Batch processing is great for reports and dashboards because it’s simple to set up and allows to pull historical trends out of large amounts of raw data in real-time. For instance, generating electricity bills at the end of the month. 

  • Real-time processing

Real-time processing is used for analyzing data as it’s coming in, and it typically involves an immediate response to a trigger event. It processes and transfers data as soon as it’s obtained. It helps rapid decision-making. 

For example, when the company receives a query from a customer about an order, you want to be able to answer that query immediately by pulling up relevant details about their order (such as payment method) from existing records. In other words, you don’t want to wait until tomorrow or next week to return their call. Real-time processing does just that—it updates information in your database almost instantaneously as new data comes in.

  • Online Processing

This is a data processing where businesses can upload their raw data and receive processed results online. Online processing is fast and simple. 

The main idea behind online processing is that data can be entered through an interface, such as a Web browser, phone, etc., at any time when it’s convenient for users.

For example, When you buy a pen in a supermarket, the barcode is scanned for payment and the invoice, and the item is marked as sold in the supermarket’s inventory system. It also gets updated in costs and sales reports. Once the payment is made, you can receive your results in real-time.

In general, most online processors will process your data on-demand. 

  • Multiprocessing

Multiprocessing refers to one computer system having more than one processor. It has two or more microprocessors. The purpose of a multiprocessor system is to distribute data processing among several processors so that they can execute different parts of a single program concurrently (instead of sequentially). 

This approach permits data-intensive applications to run faster. Examples include tasks in financial services, scientific and engineering computations, video editing, and audio editing systems.

  • Time-sharing

One of the time-sharing’s main characteristics is that it allows many users to have access to a computer system simultaneously. While in batch processing only one user can make changes and then another batch job runs; with time-sharing several users can execute jobs concurrently with the central processing unit (CPU)

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Five Advantages of Data Processing

  • Increased productivity and profits

When analyzing data, there is a large chance that you will be using it for multiple purposes such as data mining and decision-making. This can lead to increased productivity within the company as well as better profits. 

  • Better decisions

Preparing data for analysis helps you spot trends and patterns in the data that would otherwise be difficult to identify. Once cleaned up, a dataset will be easier to analyze and review, allowing you to draw better conclusions from the analysis.

  • Accurate and reliable 

With Accurate and reliable data companies can identify trends in how their company’s products or services are sold in relation to competitors’ goods or services. 

  • Cost reduction

Before beginning to analyze data, you need to have clean and consistent data. If the data quality is not good enough, it might cost more time and money than necessary during analysis.

  • Ease in storage, distributing, and report

The data is stored in a proper format. This way, you are also able to distribute, easily report and manage the data. The data is then ready for reporting and analysis. 

Hence, data processing is very important in any business. Keeping good track of your business data is another thing that helps to keep a strict check on the performance, finances, and even future predictions to excel in any business.

How does data processing help modern technology?

Data processing has been a part of science since its inception. However, it was only recently that technology came along and opened up some real doors for us. Now we can process data in ways that weren’t feasible before. Through data processing systems, we can overcome barriers that were previously thought impossible. Without it, progress would be hampered significantly. With these systems, we have access to information in ways that completely change how we interact with society as a whole.

You might not know it, but data processing lies behind many of today’s most revolutionary technological advances. Automated stock trading, virtual reality – all these innovations and more would be impossible without a robust method of storing and analyzing big data. 

Today, businesses of all shapes and sizes use cloud-based data processing to quickly and effectively process information in real-time. In doing so, they can accurately measure key metrics and make better business decisions in a fraction of time that was previously impossible.

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