The inability of businesses to handle their data restricts their access to the very information that might help them gain a competitive advantage and provide essential business insights. Because of this, it is essential for all businesses to have a solid understanding of why it is necessary to process all of their data and how to go about doing so. Companies are placing an ever-increasing amount of reliance on their data as the business environment continues to undergo continuous change. However, before you can use any data to your company’s advantage, you must first analyze both the structured and the unstructured data that you collect. Only then will you be able to use any data.

Although data visualization is the simplest and most well-known form of data processing, there are a number of other data processing methods that are routinely used to interact with data.

The alteration of data is accomplished through a process known as “data processing.” It refers to the process of transforming raw data into content that is meaningful and readable by machines. In its most basic form, it is the process of transforming raw data into information that can be understood. “It may relate to the practice of employing computerized procedures in order to process business data.” In most cases, this involves carrying out actions that are not very complicated and are repetitive in order to analyze massive amounts of information. The term “raw data” refers to the information that is entered into a system before it is processed in order to provide “meaningful output.”

The processing of data begins with the data in its raw form and changes it into a format that is easier to understand (graphs, documents, etc.). This gives the data the shape and context required for computers to interpret it and for personnel throughout an organization to use it.

There are six levels of processing data, Let us examine them below:

1. The gathering of data

The first stage in processing data is collecting the data that will be used. Data is retrieved from the various sources that are available, such as data lakes and data warehouses. It is essential that the data sources that are at one’s disposal are reliable and professionally constructed in order to ensure that the data gathered (which will subsequently be used as information) is of the greatest possible quality.

2. Data preparation

Following the completion of the data collection stage, the next step is the data preparation stage. In the first step of data processing, known as “data preparation,” the unprocessed data are cleaned up and arranged in preparation for the second stage of data processing, which is called “pre-processing.” Throughout the process of preparation, the raw data is carefully examined for the presence of any inaccuracies. This step’s objective is to get rid of bad data (data that is redundant, missing, or wrong) and get started on creating high-quality data for the purpose of achieving the greatest possible business intelligence.

3. Data entry

Next, the data are translated into a language that the destination can comprehend, and then they are entered into their destination (which may be a customer relationship management system like Salesforce or a data warehouse like Redshift). The first step in the process by which raw data is transformed into information that can be used is called the data input stage.

4. Data Processing

During this stage, the information that was fed into the computer during the stage before is actually processed so that it can be interpreted. The processing is done using machine learning techniques, however the process itself may vary slightly based on the source of the data being processed (data lakes, social networks, linked devices, etc.) and its intended purpose. The processing is done using machine learning methods (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.).

5. Data output/interpretation

At the stage known as the output and interpretation stage, the data are eventually made useable by people who are not data scientists. It is translated, legible, and typically in the form of graphs, movies, photos, plain text, etc.). The data that individuals inside the firm or institution need to conduct their own data analytics initiatives can now be accessed by them independently.

6. Data storage

The final stage of data processing is storage. When all of the data has been processed, it is then saved for utilization at a later time. Although some of the knowledge might be useful right away, the majority of it is going to be put to use at a later time. In addition, compliance with data privacy legislation such as the GDPR requires that data be stored in an appropriate manner. When data is stored appropriately, individuals of the organization are able to gain access to it in a timely manner and with little effort whenever it is required.

Now, let us look at some practical and real-world examples where Data processing is predominant. 

• Electronics: A digital camera applies a set of algorithms that are based on a color model in order to turn the raw data that is received from a sensor into a photo file.
• Support for Making Decisions A stock trading program will take the data that represents millions of stock trades and convert it into a graph that a trader can readily understand.
• Integration is a process that moves data from one system to another and is known as integration. In order to accomplish this, a procedure of transmitting the data and converting its format is required.
• Automation: The monthly charges for customers are computed by a telecom billing system. These charges are dependent on parameters such as the customer’s service plan and the customer’s data usage for the month in question. A paper that provides a summary of the fees is sent out in the mail to the consumers. The whole procedure is computerized, and a team of billing specialists watches over it.
• Transactions: When a user submits a request for a money transfer to a banking website, the website will validate the request and format it so that it can be executed by a backend system.
• A website will take user-submitted media like videos and will then convert them to a standard format before displaying them to users of the website.
• When it comes to communication, a messaging tool will encrypt a message before sending it out into the world by utilizing an encryption method and a public key. PKI (public key infrastructure) for IoT has progressed past basic X.509 character endorsements. Our elite PKI architects can help with planning rich, cryptographically secure gadget characters that adjust with shifts in your business and innovation and backing confided in computerization at the edge..
• An autonomous vehicle’s “artificial intelligence” analyzes data gathered in real time from several sensors to determine whether or not there are pedestrians in its path. The application of models that were generated through a process of machine learning, in which an artificial intelligence analyzed millions of hours of sensor data in order to practice identifying pedestrians, is used to accomplish this goal. The models are applied in order to accomplish this.