Data is generated every single second, whether you use the internet to order food, make financial transactions, or learn about a particular subject. Social media use, online shopping, and the use of video streaming services have all contributed to the rise in data. According to a Domo study, by the year 2020, every person on the planet will produce 1.7MB of data every second. And data processing is necessary in order to make use of and gain insights from such a vast amount of data.
As we move forward, let’s define data processing.
Data processing: What Is It?
Any organisation 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 typically performs it in a step-by-step manner. The unprocessed data is gathered, sorted, processed, examined, and stored before being presented in a readable format.
For businesses to improve their business strategies and gain a competitive edge, data processing is crucial. Employees across the organisation can understand and use the data by converting it into readable formats like graphs, charts, and documents.
Let’s look at the data processing cycle now that we’ve defined what we mean by data processing.
The Data Processing Cycle in Detail
Raw data (input) is fed into a system in a series of steps known as the data processing cycle in order to produce useful insights (output). Although the steps are carried out in a specific order, the whole procedure is cycled back on itself. As shown in the illustration below, the output of the first data processing cycle can be saved and used as the input for the following cycle.
In general, the data processing cycle consists of six main steps:
Step 1: Gathering
The first stage of the data processing cycle is the gathering of raw data. The type of raw data gathered has a significant influence on the results generated. Therefore, in order for the subsequent findings to be reliable and applicable, raw data should be gathered from well-defined and precise sources. Financial data, cookies from websites, company profit/loss statements, user behaviour, etc. are all examples of raw data.
Step 2: Preparation
Sorting and filtering raw data to remove erroneous and inaccurate information is known as data preparation or data cleaning. To prepare raw data for further analysis and processing, it is checked for errors, duplication, errors in calculations, and missing data. This is done to make sure that the processing unit only receives the best data.
Step 3: Input
The raw data is transformed into machine-readable format and fed into the processing unit in this step. This can be done by entering data using a keyboard, scanner, or some other input device.
Step 4: Data Processing
In this step, machine learning and artificial intelligence algorithms are used to process the raw data in a variety of ways to produce a desired result. Depending on the data source being processed (data lakes, online databases, connected devices, etc.) and the intended use of the output, this step may vary slightly from process to process.
Step 5: Output
Finally, the user receives the data and it is presented to them in a readable format, such as graphs, tables, vector files, audio, video, documents, 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 quickly accessed and retrieved whenever necessary and used directly as input in the subsequent data processing cycle.