What are different types of process?

What Is Data Processing?

Data in its raw form is not useful to any organization. Data processing is the method of collecting raw data and translating it into usable information. It is usually performed in a step-by-step process by a team of data scientists and data engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format.

Data processing is essential for organizations to create better business strategies and increase their competitive edge. By converting the data into readable formats like graphs, charts, and documents, employees throughout the organization can understand and use the data.

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Now that we’ve established what we mean by data processing, let’s examine the data processing cycle.

All About the Data Processing Cycle

The data processing cycle consists of a series of steps where raw data [input] is fed into a system to produce actionable insights [output]. Each step is taken in a specific order, but the entire process is repeated in a cyclic manner. The first data processing cycle's output can be stored and fed as the input for the next cycle, as the illustration below shows us.

Fig: Data processing cycle [source]

Generally, there are six main steps in the data processing cycle:

Step 1: Collection

The collection of raw data is the first step of the data processing cycle. The type of raw data collected has a huge impact on the output produced. Hence, raw data should be gathered from defined and accurate sources so that the subsequent findings are valid and usable. Raw data can include monetary figures, website cookies, profit/loss statements of a company, user behavior, etc.

Step 2: Preparation

Data preparation or data cleaning is the process of sorting and filtering the raw data to remove unnecessary and inaccurate data. Raw data is checked for errors, duplication, miscalculations or missing data, and transformed into a suitable form for further analysis and processing. This is done to ensure that only the highest quality data is fed into the processing unit. 

The purpose of this step to remove bad data [redundant, incomplete, or incorrect data] so as to begin assembling high-quality information so that it can be used in the best possible way for business intelligence.

Step 3: Input

In this step, the raw data is converted into machine readable form and fed into the processing unit. This can be in the form of data entry through a keyboard, scanner or any other input source. 

Step 4: Data Processing

In this step, the raw data is subjected to various data processing methods using machine learning and artificial intelligence algorithms to generate a desirable output. This step may vary slightly from process to process depending on the source of data being processed [data lakes, online databases, connected devices, etc.] and the intended use of the output.

Also Read: Top 10 Machine Learning Algorithms For Beginners

Step 5: Output

The data is finally transmitted and displayed to the user in a readable form like graphs, tables, vector files, audio, video, documents, etc. This output can be stored and further processed in the next data processing cycle. 

Step 6: Storage

The last step of the data processing cycle is storage, where data and metadata are stored for further use. This allows for quick access and retrieval of information whenever needed, and also allows it to be used as input in the next data processing cycle directly.

Now that we have learned what is data processing and its cycle, now we can look at the types.