Data processing is applied in every field today—be it education, business, healthcare, or research. With growing advancement in computer science through machine learning—a subset of artificial intelligence or data security, the need for data science courses is also growing.
Before we delve into the concept of data processing, let us first start by understanding the data processing definition.
Data in its original, raw form cannot be used. Like some raw vegetables need to be 'cooked' before consumption, data needs to be 'translated' before using.
Data processing includes gathering, manipulating, and processing collected raw data. Nowadays, the processing is done automatically through computers. This automatic processing in computers is known as manipulating data by computers. It involves a predetermined data flow sequence through the CPU and memory to output devices and output formatting or transformation. The gathered data is then processed and translated into a readable format as per requirements.
Data is acquired from several sources like database, text, or excel files, and unorganized data such as images, GPRS, audio clips, and video clips. Storm, Hadoop, HPCC, Statwing, Qubole, and CouchDB are the most common data processing tools. The output is transformed into an audio, graph, image, vector file, chart, etc.
For instance, conversion of extensive stock data into a simple graph through a stock trading software; a self-driving car using sensor-based real-time data to detect cars and pedestrians; or an advertising company using customers' search history for product recommendations.
The data processing cycle involves predetermined data processing steps in which the raw data (input) is put into a process (CPU) for actionable insights (output). It is a cyclic process. The output from the previous data processing cycle is stored and fed as the input for the next cycle.
The six main data processing stages are—
The primary stage in the data processing cycle involves collecting raw data acquired from standard sources such as data warehouses and data lakes. It is crucial to collect high-quality and trustworthy data to acquire useable and valid output. Raw data consists of website cookies, the company's financial statements, user behavior, etc.
Data preparation eliminates inaccurate and useless data by segregating collected raw data for duplication, errors in calculations, etc. Therefore, this pre-processing step is known as data cleaning and helps generate high-quality data into the processing unit—crucial for improved business intelligence.
With the help of CRM, the collected, prepared data is translated into machine-readable language and stored in the processing unit. This is done by entering data by conventional means of a keyboard or scanner or any other input source.
The input data is processed for interpretation via artificial intelligence or algorithms at this stage. This may vary based on the source of processed data (connected devices, online data, social networks, data lakes, etc.) and the use of the output (medical diagnosis, advertising patterns, customer requirements, etc.)
At this stage, the data gets converted into readable formats like videos, images, tables, vector files, plain text, audio, Docx files, etc. Members of an organization can now store, use or analyze this output for their projects.
Storage is the final stage in the cycle. At this point, employees store data and metadata for easy access and retrieval. It is crucial to store data effectively for it to comply with GDPR (data protection legislation).
Manual Data Processing
As the title explains, the data is processed manually in this method. All the data processing steps like gathering, filtering, sorting, and other operations occur with human intervention and do not involve using any electronic device or automation software. Naturally, this is an inexpensive method, but time-consuming, prone to errors, and comes with high labor costs.
Mechanical Data Processing
All the data processing stages occur mechanically, i.e., through simple devices such as calculators, typewriters, calculators, etc. This method is usually preferred for simple data processing operations and involves fewer errors than the manual method. It is, however, not compatible with excess data.
Electronic Data Processing
This method involves the use of modern technologies and data processing software and program. The data is processed through a series of instructions given to the software to acquire the desired output. It is a highly accurate and reliable method but comes at a high cost.
It is a preferred choice with extensive data where data is collected and processed in batches.
For e.g., payroll system.
It is a preferred choice with small quantity data, where data is collected and processed in real-time, i.e., within seconds of data input.
For e.g., ATM money withdrawal.
It is a preferred choice for continuous data processing, whereby data is stored automatically into the CPU when it becomes available.
For e.g., barcode scanning.
Multiprocessing or Parallel processing
It is a preferred choice with fragmented data, whereby data is broken down into small frames and processed through two or more CPUs within a single computer system.
For e.g., weather forecasting.
This type enables simultaneous distribution of data and computer resources to several users located at various terminals.
In recent years, the demand for data science has picked up. Most of our work depends on data—academic, scientific research, or even personal, private, or commercial uses. Data processing eliminates complicated paperwork, improves efficiency, provides a competitive edge to companies, and also helps them to create better business strategies.
Without access to data processing, companies tend to limit their business intelligence, productivity, and profits. If you are keen to learn data processing in detail along with other crucial data science concepts, then join our data science course and transform yourself into a highly skilled data science professional.
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