What is Data Processing?

Data processing is the process of gathering, organizing, and analyzing data to make it useful. It consists cleaning, sorting, and converting raw data into a format suitable for decision making and forecasting..

In this article, we will look at the importance of data processing, its various steps and processes, and how it helps businesses and individuals make informed decisions. Let’s dive in!

Table of contents:

• What is Data Processing?
• Steps in Data Processing
• Types of Data Processing
• Examples of Data Processing
• Advantages of Data Processing
• The Future of Data Processing
• Conclusion

What is Data Processing?

The process of transforming raw or unprocessed data into a clean and readable format is called data processing. When we say data is transformed, we mean that we will be applying multiple data operations, like removing null data, sorting it, filtering it, applying a dataframe, etc., to make the raw data more readable. Usually, data processing is done by either a Data Engineer or a Data Scientist.

1. Need for Data Processing

Data processing is required to transform unprocessed data into information that can be used in decision-making. It helps them spot patterns and trends and make educated decisions. After data processing, the processed data can be used to track consumer trends, measure consumer behaviour, and create customer segments. With its help, businesses will be able to customize their goods and services according to customer preferences. It will increase sales and customer satisfaction.

Let’s take the example of Zomato. For Zomato, delivering the food to the location is the most important part of their workflow. To accomplish this, they use the previous data to analyze and predict the traffic for the next weekend. This helps them manage delivery agents for hassle-free deliveries.

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Steps in Data Processing

The Data Processing cycle is a set of practices used to transform unusable data into information.
There are six stages of Data Processing:

1. Data Collection

Data collection is the first stage of Data processing, wherein data is collected from various valid sources. The source of the data must be trustworthy, as the outcome or inferences drawn from the data depend on the quality of the generated data. Raw data can contain null values, user behaviors, some symbols, website cookies, and all other impurities.

2. Data Preparation

Data preparation also called pre-processing or Data Cleaning, it is the second stage of data processing. The main goal of this stage is to bring out the best data for business intelligence. In this stage, we get rid of bad data (redundant, incomplete, or incorrect.) by using multiple transformation operations like filtering, sorting, and multiple data manipulation techniques.

3. Data Input

Data input is the third stage of data processing. In this stage, you will see the raw data taking a readable form for the first time. Here, data is usually converted into a readable format using programming languages like Python or R, and then the data is stored in some data warehouse like Redshift or some CRM like Salesforce or Zoho.

4. Processing

Processing or data processing is the fourth stage. In this stage, we use multiple machine learning algorithms along with frameworks like Spark, Pyspark, and libraries like Pandas, Koalas, etc to perform data transformation. The process or steps are subject to change based on the data source and its intended use.

5. Data Output

It is an interpretation stage wherein the data is checked and visualized to see if further processing is required or not. At this stage, the data is made available to the members of the organization to perform analysis on the data. 

6. Data Storage

Data storage is the last stage in the data processing lifecycle. In this stage, the processed data is stored along with the metadata on some data lake or S3 glacier. It can be easily accessed by the members of the organization for further use. Storing the data properly also allows us to retrieve the data and use it as a data input during the next data processing cycle.

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Types of Data Processing

There are several types of data processing, based on the source of the data, at what interval the data is processed, and how the data is processed.

Here are a few of them:

Examples of Data Processing

1. Data Entry and Validation

• This includes gathering raw data and verifying its accuracy before storing it in a system.
• For example, when a user completes a signup form, the system verifies email formats and ensures that all essential areas are filled out.

2. Data Cleaning and Transformation

• Raw data often contains errors, duplication, or inconsistencies that must be cleaned to improve accuracy.
• Examples include removing duplicate customer records from a database and harmonizing date formats across several data sources.

3. Statistical Analysis

• Organizations analyze data to identify trends, patterns, and insights that aid decision-making.
• For example, a retailer analyzes consumer purchases to determine the best-selling products and adjusts inventory accordingly.

4. Data Aggregation

• This method involves summarizing and combining data from several sources to provide useful insights.
• For example, a marketing team may use website traffic data, email open rates, and social media participation to evaluate the efficiency of a campaign.

5. Real-Time Data Processing

• Some industries require immediate data analysis and decision-making, which is frequently powered by AI and automation.
• For example, banks use real-time fraud detection systems to identify suspicious transactions and prevent fraud.

Advantages of Data Engineering

1. Better decision-making

Data processing enables companies to evaluate and interpret data, which produces decisions that are more informed that boost productivity, profitability, and competitiveness.

2. Increased efficiency and productivity.

Data processing can automate regular operations, allowing workforce to focus on more important work. These systems can process huge amounts of data rapidly and precisely, resulting in quicker turnaround times while reducing the possibility of human mistake and producing more accurate and dependable findings.

3. Improved Customer Understanding

Businesses that analyze customer data can obtain a deeper understanding of their customers’ wants and preferences, allowing them to provide more personalized and effective service.

4. Optimized Data Management

Data processing allows for the building of structured and organized databases, making it easier to access, manage, and retrieve data. It involves data cleansing, which guarantees that the information is correct, consistent, and reliable.

The Future of Data Processing

The amount of data generated by technology and companies is continuing to expand tremendously, and the data being generated is becoming more powerful, complex, and huge. Therefore, a lot of resources are required to store and process it.

The future of data processing is cloud computing, wherein we will be using the services of public clouds like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) for data processing. Previously, we used some on-premise systems to process this huge amount of data, but it was not feasible as it cost a lot. 

Technologies like the public cloud will help reduce costs and improve the efficiency of the life cycle. Public clouds are affordable and can be scaled easily as the company grows in size.

Technologies like distributed processing which includes Hadoop, MapReduce, and Spark are continuously evolving. Therefore, cloud-based distributed processing is the future.

Through the use of these technologies, data processing will be more accurate, efficient, and automated, allowing quicker and wiser decision-making.

Conclusion

Data processing is a method of converting raw data into meaningful information through various steps like cleaning, organizing, and analyzing. Understanding data processing is very important for working with data effectively. If you are interested in learning more, our Data Science course can help you gain the skills needed to handle and process data quickly.