Data Processing

Data processing is a primary concept in information technology and data management, as it helps convert raw data into actionable information through different models and technology.

It includes various stages that aim to organize, analyze, and interpret data to derive valuable insights and support decision-making.

What is data processing?

Data processing is a series of steps that manipulates, transforms, organizes, and analyzes the raw data to extract meaningful information involving unique information and methods to convert the data into useful and valuable information to make better decisions. Data processing can encompass a wide range of activities, from basic tasks like data entry and validation to more advanced steps such as data analyzing and modeling.

Boost Sales Performance by 94% with Our Gamified Commission Management Software

What is the data processing cycle?

The data processing cycle is the stages that data goes through from initial collection to its final use and disposal. It fosters various stages of data processing to transform the unorganized data into meaningful information. The various steps involved in data processing are as follows:

Data collection
Data entry
Data processing
Storing data
Data analyzing
Data visualization
Data interpretations
Decision making
Reporting
Feedback

Data collection: Data collection is the initial stage, where the unstructured data is derived from different sources such as transactions, surveys, social media interactions, and many other sources. The collected data must have an accuracy and quality that can propagate through the entire processing cycle.
Data entry: Once the data is collected, the information is entered into the system for further processing that can be manually entered or other alternative methods, which certainly depend on the sources and volume of data.
Data processing: the raw data is cleared and transformed into useful information, which includes identifying and rectifying errors, duplicate data, and missing values. Data transforming involves converting data into a structured format, aggregating or summarizing data, and calculations to prepare for further evaluation.
Storing data: Processing data is stored in the systems, data warehouses or other storage systems, ensuring easy retrieval and integrity. The choice of storing technology and architecture depends on the elements like volume, access frequency, and the specific requirements.
Data analyzing: Data analysis includes different statistical or computerized technology to explore the data and unveil patterns and trends. Data analysis can include summarized data and forecast future business analysis.
Data visualization: After analysis, data insights are often presented visually using charts, graphs and dashboards, which makes complicated data more understandable and allows stakeholders to easily grasp the key takeaways.
Data interpretations: Data experts or managers interpret the analyzed data to extract meaningful conclusions and insights and achieve the goals that guide proper decision-making.
Decision making: Using the knowledge gained from the data interpretation, stakeholders make proper decisions that help in the organization’s operations, processing and making proper strategies that range from tactical adjustments to strategic initiatives.
Reporting: The outcome gained from the data interpretations, stakeholders make valuable decisions documented and communicated through presentations, reports, or interactive dashboards.
Feedback: Organizations use the results of decisions and actions taken based on the processed data to assess their effectiveness, that helps refine strategies, optimize processes and improve further actions.

What are the types of data processing?

The types of data processing include:

Batch processing
Real-time processing
Online transaction processing (OLTP)
Online Analytical Processing (OLAP)
Multi-processing
Massively parallel processing (MPP)

Batch processing: Batch processing includes the collection and processing of large volumes of data all in one go, usually in a single operation or job, which often is used for routine tasks that don’t require real-time processing, like payroll calculations, billing and generating reports.
Real-time processing: Real-time processing includes handling and analyzing data as it’s generated, without any delay, as this type of processing is suitable for circumstances where immediate processing is required, like monitoring sensor data, financial trading and online gaming. Real-time processing often requires efficient data pipelines and systems to process and respond to streamline data in real-time.
Online transaction processing (OLTP): OLTP includes real-time processing of individual transactions, like updating a database record, processing an online purchase, or making a reservation, which focuses on maintaining data integrity and ensuring accurate, updated information.
Online Analytical Processing (OLAP): OLAP includes processing and analyzing large volumes of data to offer insights for decision-makers and strategic planning. These systems support complicated queries and allow users to explore data through different dimensions, enabling data visualization.
Multi-processing: Multi-processing includes using multiple processors to perform data processing tasks simultaneously. This type of processing can enhance performance and speed up computations, especially for tasks that can be parallelized.
Massively parallel processing (MPP): MPP involves using a large number of processors to perform data processing tasks in parallel. These systems are designed to handle complex analytical concerns on large datasets which are used in big data analytics.

What are the examples of data processing?

The examples of data processing are as follows:

E-commerce
Finance
Manufacturing
Transportations and logistics
Telecommunication

E-commerce: Analyze customer behaviour and browzing to personalize recommendations and promotions. Generating reports on sales, inventory levels and customer demographics.
Finance: Processing financial transactions involving credit card payments, online banking transfers, and stock trading. Detecting fraudulent transaction patterns and anomalies in in real-time.
Manufacturing: Monitoring and controlling manufacturing processes using sensor data for quality control and efficiency improvement. Supply chain optimization by analyzing inventory levels, demand patterns, and production schedules.
Transportations and logistics: Real-time tracking of vehicle locations and routine GPS data and optimizing delivery routes and schedules to minimize fuel consumption and delivery time.
Telecommunication: Processing call records for billing purposes and analyzing call patterns for network optimization and real-time analysis of network data to detect and prevent network outages and congestion.

What are the methods of data processing?

The methods of data processing are as follows:

Manual data entry
Mechanical data processing
Electronic data processing

Manual data entry: Data is entered into a system manually by people as the method used is to deal with small volumes of data or when data requires human verification and interpretation.
Mechanical data processing: Data is entered mechanically via the use of devices and machines. These can involve simple devices like typewriters, calculators or any other devices. Simple data processing allows to achieve easy operations and minimizes error than manual data processing.
Electronic data processing: In electronic data processing, modern technologies take place via data processing software and programs. A set of instructions is given to the software to process the data and yield output as this method is costly but also provides better results and reliability on the data outcome.

Employee pulse surveys:

These are short surveys that can be sent frequently to check what your employees think about an issue quickly. The survey comprises fewer questions (not more than 10) to get the information quickly. These can be administered at regular intervals (monthly/weekly/quarterly).

One-on-one meetings:

Having periodic, hour-long meetings for an informal chat with every team member is an excellent way to get a true sense of what’s happening with them. Since it is a safe and private conversation, it helps you get better details about an issue.

eNPS:

eNPS (employee Net Promoter score) is one of the simplest yet effective ways to assess your employee's opinion of your company. It includes one intriguing question that gauges loyalty. An example of eNPS questions include: How likely are you to recommend our company to others? Employees respond to the eNPS survey on a scale of 1-10, where 10 denotes they are ‘highly likely’ to recommend the company and 1 signifies they are ‘highly unlikely’ to recommend it.

Based on the responses, employees can be placed in three different categories:

Promoters
Employees who have responded positively or agreed.
Detractors
Employees who have reacted negatively or disagreed.
Passives
Employees who have stayed neutral with their responses.