Exploring Data Processing Key Types and Examples | iCert Global

Every time you use the internet to learn about something, make an online payment, order food, or do anything else, data is created. Social media, online shopping, and streaming videos have all contributed to a huge increase in the amount of data we generate. To make sense of all this data, we use something called data processing. Let’s explore what data processing is and how it works.

What Is Data Processing?

Raw data, or data in its unorganized form, isn’t very helpful to anyone. Data processing is the process of turning this raw data into useful information. This is done in a series of steps by a team of people, like data scientists and engineers, who work together in a company. First, the raw data is collected. Then, it’s filtered, sorted, analyzed, and stored before being shown in an easy-to-understand format.

Data processing is very important for businesses because it helps them make better decisions and stay ahead of the competition. When the data is turned into charts, graphs, or reports, people in the company can easily understand and use it.

Now that we know what data processing is, let’s look at how the data processing cycle works.

Step 1: Collection

The first step in the data processing cycle is collecting raw data. The type of data you gather is really important because it affects the final results. It’s important to get data from reliable and accurate sources so the results are correct and useful. Raw data can include things like money numbers, website information, company profit or loss, and user activity.

Step 2: Preparation

Next comes data preparation, also known as data cleaning. This is when the raw data is sorted and checked to remove mistakes or unnecessary information. The data is checked for errors, duplicates, missing details, or wrong information. The goal is to make sure the data is in the best possible shape for the next steps. By cleaning up the data, we get rid of anything that could mess up the final results, ensuring that only good quality data is used.

Step 3: Input

Once the data is ready, it has to be turned into a format that computers can understand. This is called the input step. The data can be entered into the computer using a keyboard, scanner, or other tools that send the data to the system.

Step 4: Data Processing

This step is when the actual work happens. The raw data is processed using different methods like machine learning or artificial intelligence (AI) to turn it into useful information. Depending on where the data is coming from (like databases or connected devices) and what it will be used for, the process might look a little different.

Step 5: Output

After processing, the data is shown to the user in an easy-to-understand form, like graphs, tables, videos, documents, or even sound. This output can be saved and used later in another round of data processing if needed.

Step 6: Storage

The final step is storing the data. In this step, the processed data is saved in a place where it can be quickly accessed later. This storage also makes it easy to use the data again in the next data processing cycle.

Now that we understand data processing and its steps, let's take a look at the different types of data processing.

Data Processing is the way we take raw data (like numbers, facts, or information) and turn it into something useful, like a report or an answer. It helps us organize, sort, and understand the data better.

Understanding Data Processing and Its Different Types

Types of Data Processing:

1. Manual Data Processing:
   • This is when people process data by hand, like writing things down on paper or doing math on a calculator.
   • Example: Doing math homework without a computer.
2. Mechanical Data Processing:
   • This uses simple machines, like early calculators or typewriters, to help process data.
   • Example: Using a basic adding machine to do math.
3. Electronic Data Processing:
   • This is when computers and software are used to process data quickly and accurately.
   • Example: Using a computer to calculate grades in a school.
4. Real-time Data Processing:
   • Data is processed immediately as it happens.
   • Example: Watching live sports scores online.
5. Batch Data Processing:
   • Data is collected and processed all at once, instead of right away.
   • Example: Doing everyone's school grades at the end of the semester.
6. Distributed Data Processing:
   • This is when data is processed by multiple computers working together.
   • Example: Using cloud storage where data is stored and processed on many different computers.
7. Online Data Processing (OLTP):
   • Data is processed as soon as it's entered into a system, like when you buy something online.
   • Example: Making an online purchase where your payment is processed right away.

What is Data Processing: Methods of Data Processing

There are three main ways to process data: manual, mechanical, and electronic.

Manual Data Processing

Manual data processing is done completely by hand. People collect, filter, sort, and calculate the data without using any machines or software. It’s a low-cost method that doesn’t need special tools, but it has some downsides. It can lead to a lot of mistakes, take a lot of time, and require a lot of work from people.

Mechanical Data Processing

In mechanical data processing, simple machines and devices are used to help process the data. These could include things like calculators, typewriters, or printing presses. This method has fewer mistakes than manual processing but can still be slow and complicated when there’s a lot of data.

Electronic Data Processing

This is the most modern way to process data, using computers and software programs. Instructions are given to the software to process the data and create results. Although it’s the most expensive method, it’s also the fastest and most accurate, making it the best option for large amounts of data.

Examples of Data Processing

Data processing happens all around us every day, even if we don’t notice it. Here are a few real-life examples:

• A stock trading software turns millions of pieces of stock data into a simple graph.
• An online store looks at what you’ve searched for before to recommend similar products.
• A digital marketing company uses information about people’s locations to create ads for certain areas.
• A self-driving car uses data from sensors to spot pedestrians and other cars on the road.

Moving From Data Processing to Analytics

One of the biggest changes in today’s business world is the rise of big data. Although managing all this data can be tough, the benefits are huge. To stay competitive, companies need to have a good data processing plan.

After data is processed, the next step is analytics. Analytics is when you find patterns in the data and understand what they mean. While data processing changes the data into a usable format, analytics helps us make sense of it.

But no matter what process data scientists are using, the huge amount of data and the need to understand it means we need better ways to store and access all that information. This leads us to the next part!

How to obtain Bigdata certification? 

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Conclusion  

The future of data processing can be summed up in one short phrase: cloud computing.

While the six steps of data processing stay the same, cloud technology has made big improvements in how we process data. It has given data analysts and scientists the fastest, most advanced, cost-effective, and efficient ways to handle data. So, the same technology that helped create big data and the challenges of handling it also gives us the solution. The cloud can handle the large amounts of data that are a part of big data.

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