Data represent a collection of information in its raw form. Its structure needs refining. The formatting is not identical. Simply put, there are several downsides that require thorough work to improve them. This is where data processing appears in a key role.
What Is Data Processing?
The process of collecting raw data chunks from various resources through web data scraping or offline resources and then, translating them into a useful piece of information is called data processing. This definition itself highlights that it involves many sub-processes, which cover but are not limited to data migration, conversion, cleansing, de-duplicating, normalization, appending, verification, and standardization.
All of these sub-processes are carried out step-by-step by experts who can be data scientists, AI experts, data engineers, and data analysts together with researchers. Their efforts convert a raw bit of data into insightful information that is easy to understand via data visualization. Charts, graphs, documents, and likewise presentations make visuals really effective, which lets businesses discover phenomenal decisions.
Simply put, this processing is crucial for transforming business practices, improving productivity and efficiency, and integrating a cutting edge. Many companies involved in processing and data entry outsourcing ensure assisting with these business benefits.
This understanding of “what this processing is” would make it easier to learn about its cycle.
Data Processing Cycle
This cycle is run through a series of steps, which have already been aforementioned. It’s like a computer memory or CPU, which takes inputs, converts them into knowledge, and finally, produces an actionable output. Today, AI and machine learning are brought to life using this method, which is typically called the Extract, Transform, and Loading (ETL) process. This is also a technique of carrying out sub-processes for figuring out insights.
Basically, the data processing cycle has six main phases. Let’s get through the roundup of all of them separately.
Data Collection
As the name suggests, it refers to the method of pooling raw information. For this purpose, the end goal is taken into account because it lays the foundation of the entire collection. So, the niche is decided, at first to carry out research and then, capturing of the useful datasets. The freshness of these details is vital to get rid of obsolete results or decisions. So, the professionally expert team digs niche-based resources and accesses datasets accordingly through web extraction, OCR conversion, and data capturing methods. Finally, an enterprise filters gainful insights upon studying the whole set of information. These can be related to transactions (banking) web cookies, profitability, scalability, customers, and their behavior.
Preparing Datasets
This is the next phase, wherein preparations go on for cleaning the collected information. This stage involves accessing those files, ingesting the whole set of information, and combining or compiling them from different locations and resources. Here, analysts and researchers appear in a key role, in running this phase smoothly. The success of this phase reflects in the feasible outcome.
Conversion of Files
The collected files or details may be in PDF or read-only format. It requires to be converted into digital through the OCR conversion method. This is all about scanning, recognizing characters, and then, capturing them all to convert into digital. Once done, the cleansing follows it up.
Cleansing Datasets
Typos, de-duplication, errors, appending, enriching, standardization, and normalization-these and many other subsets are executed upon assessing the structure and measuring the quality in accordance with the goal. Having a ton of discrepancies, errors, or incomplete detail certainly strains your budget. Also, the time and efforts go in vain. This is why cleansing makes the day of processing units.
Processing for Knowledge
This phase is concerned with knowledge discovery, which is also called the KDD process. This method proves exceptional when your business struggles with stock maintenance or any other problem, and you don’t have any idea about how to overcome it. With the collection of neat and clean details, the insights appear crystal clear. They guide you to make decisions that can actually fix the root cause of that business problem.
Machine learning is also about modeling or discovering algorithms that can become learning for machines. With the knowledge-centric model’s discovery, simulating human behavior is like a walkover. Recently, the Finnish Center for Artificial Intelligence (FCAI) has simulated the way a man makes typing mistakes and then, does fixing. Now, a bot is capable of simulating typing behavior of a data operator. This breakthrough is achieved by drilling eyes and hand movement-based data of operators. The modeling proved right, and the milestone is achieved now.
Data Visualisation
This step ensures converting insights into graphics or visual data. For this purpose, a number of readable forms are used like graphs, tables, vector files, audio, videos, documents, etc. Finally, the discovered insights are turned more comprehensive or understandable through the graphical or pictorial presentation. It shortens the time to make decisions, which are no less than a turning point for any business.
Storage or Data Warehousing
This is the final step, wherein the processed information is secured on a robust server environment. Here, metadata and tagging are done beforehand for making it easy to recall valuable points whenever required in a few moments. This happening also makes the retrieval of information easier.
Various Types of Processing
The trade or corporate world has a number of domains wherein this method is carried out in different types. There is no “one-size fits all” method because of the unique nature of businesses. Let’s get through what these are,
Batch Processing
It denotes the processing of transactions in a cluster or batch. You don’t require any user to communicate with a batch when it’s under process. Herein, the information is collected in a bulk or a massive volume, as in the payroll system.
Real-time Processing
It is the technique of rapidly updating data as per changes and then, offering results instantaneously. It ensures that the update would be visible at the point of entry right away. This method is helpful in quickly addressing a request.
Online Processing
This method represents an automated way to input and process data or reports over and over. The source documents are available as are in this case. Barcode scanning is its biggest example.
Multiprocessing
This is relevant in the context of cloud computing, or computing. It involves two or more processors in a system together processing two or more different parts of the same program. Also called parallel processing, the datasets are parted into frames and then, processed in two or more processors. Weather forecasting represents how it happens.
Time-sharing
This method represents an operation in which multiple users interact with different programs simultaneously through the central processing unit (CPU) of a large-scale digital computer.
All of these types of processing methods are used for adding convenience to our business practices, improving efficiency, and making every task or alignment transparently visible in real-time. With AI and machine learning, these processes are evolving to such an extent that the workflow has turned visible in real-time. This is called the speed or pace that the business wants to have for accelerating quick decision-making.
Summary
Data processing is helping many businesses through AI-driven applications and software. It is necessary to adopt transformation, which is achieved through its cycle. It involves data collection, conversion, cleansing, processing of knowledge, and storage. With these stages, the entire cycle produces results that are used in different types of businesses.