All About Data Processing: Cycle, Methods, Steps, and Examples

INTRODUCTION

Data is generated every second, whether you use the internet to order food, make financial transactions, or learn about a particular subject. Social media use, internet shopping, and video streaming services have contributed to the rise in data. According to a recent report, by the year 2023, every person on the earth will produce 1.7MB of data every second. And data processing is necessary to make use of and gain insights from such a vast volume of data.

As we move further, let's define data processing.

Data processing: What Is It?

Any organization cannot benefit from data in its raw form. Data processing is the process of taking raw data and turning it into information that can be used. An organization's team of data scientists and data engineers often performs it in a step-by-step manner. First, the unprocessed data is gathered, sorted, processed, examined, and stored before being provided understandably.

For businesses to improve their business strategy and gain a competitive edge, data processing is crucial. Employees across the organization can understand and use the data by turning it into readable representations like graphs, charts, and texts.

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Data Processing Cycle

Raw data (input) is fed into a system in a series of steps known as the data processing cycle in order to yield useful insights (output). Although the steps are carried out in a certain order, the whole procedure is cycled back on itself.

In general, the data processing cycle consists of six basic steps:

Step 1: Gathering

The initial stage of the data processing cycle is gathering raw data. Getting the right kind of raw data is critical since it has a big impact on the final outcomes. Therefore, for the ensuing findings to be reliable and applicable, raw data should be acquired from well-defined and precise sources. Financial data, website cookies, firm profit/loss accounts, user activity, and other types of raw data are all examples of raw data.

Step 2: Making ready

Sorting and filtering raw data to remove erroneous and inaccurate information is known as data preparation or data cleaning. Errors and duplications are checked before raw data is analyzed and processed, as well as missing data. This is done to make sure that the processing unit only receives the best data.

To start compiling high-quality information so that it may be used optimally for business intelligence, this stage aims to remove bad data (redundant, incomplete, or erroneous data).

Step 3: Input

The raw data is transformed into a machine-readable format and sent into the processing unit in this step. This can be done by entering data via a keyboard, scanner, or any other input device.

Step 4: Data processing

In this step, machine learning and artificial intelligence algorithms are used to process the raw data in various ways to produce the desired result. Depending on the data source being processed (data lakes, online databases, linked devices, etc.) and the result's intended use, this stage may differ slightly from one process to the next.

Step 5: Produce

Finally, the user receives the data, which is presented to them in a readable format, such as graphs, tables, vector files, audio, video, papers, etc. The following cycle of data processing can store and further process this output.

Step 6: Storage

Data and metadata are stored for future use during the storage phase of the data processing cycle. As a result, information can be swiftly retrieved and utilized as input in the succeeding data processing cycle.

Examples of Various Data Processing

Whether or not we are aware of it, data processing happens every day. Here are some examples of data processing in the real world:

A stock trading program that creates a simple graph from millions of stock data points

An online retailer uses customers' search histories to suggest related products.

A digital marketing firm plans location-specific ads using demographic information about consumers.

A self-driving car uses real-time sensor data to identify other vehicles and pedestrians on the road.

Data includes a wealth of valuable information for businesses, researchers, institutions, and individuals. Thus, data scientists and data engineers are in high demand as the amount of data collected each day continues to rise. Learnbay's, designed in collaboration with IBM provides the best learning experience to help you master data science skills you'll need.

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