Data processing — that is, turning data into usable information — is an important aspect of data science and data management. It is a foundational element of creating actionable insights from raw data sources. The data processing step involves several key tasks such as data cleaning, transformation, integration, and enrichment. During data cleaning, errors, inconsistencies, and redundancies are identified and corrected or removed from the dataset. Data transformation reshapes the data into a suitable format for analysis by performing operations like filtering, sorting, aggregating, and deriving new attributes. The integration phase combines data from multiple heterogeneous sources into a unified view. Finally, data enrichment augments the dataset with additional context from external sources, enabling deeper analysis. By systematically executing these data processing steps, organizations can enhance data quality, consistency, and analytical value, paving the way for robust data-driven decision-making. To learn more, let’s dive deeper into exactly what data processing is, what it’s used for, the details of the stages, how to enter the field, and even some potential data processing careers.
What Is Data Processing?
Data processing involves translating raw data into readable, usable information such as graphs, charts, and documents that an organization can use. Typically, a team of data scientists will perform data processing and disseminate the information throughout the organization. Per Anuj Khandelwal (2023), “In today's digital age, data processing is an essential part of almost every industry. It plays a crucial role in enabling organizations to extract valuable insights from their data to gain a competitive advantage, improve operational efficiency, and provide better customer service.”
What Is Data Processing Used For?
The diverse application of data processing is astounding, and it is used in every organization, large and small. Let’s take a look at some real-life examples:
- Business intelligence and analytics: Data processing enables organizations to clean, transform, and integrate data from various sources, making it ready for analysis. This processed data can then be used for business intelligence, reporting, data visualization, and identifying trends and patterns to support decision-making.
- Machine learning (ML) and artificial intelligence: Data processing is a critical step in preparing data for training machine learning models. It helps to clean, format, and preprocess data, ensuring that the input data is of high quality and in a suitable format for the chosen ML algorithm.
- Customer service and support: Data processing is used to clean and integrate customer data from various sources like support tickets, chat logs, social media mentions, and surveys. The processed data enables better understanding of customer issues, sentiment analysis, and personalized support experiences.
- Scientific research and experiments: In scientific fields, data processing is used to clean and prepare experimental data, remove errors and inconsistencies, and transform data into a format suitable for analysis, modeling, and hypothesis testing.
- Customer analytics and marketing: Data processing enables businesses to integrate and analyze customer data from various sources, such as sales records, website interactions, and social media. This processed data can be used for customer segmentation, targeted marketing campaigns, and personalized recommendations.
- Fraud detection and risk management: Financial institutions and other organizations use data processing to clean and integrate data from multiple sources, enabling them to identify patterns and anomalies that may indicate fraudulent activities or potential risks.
- Supply chain and logistics optimization: Data processing is used to clean and integrate data from various sources, such as inventory records, shipping data, and supplier information, to optimize supply chain operations, reduce costs, and improve efficiency.
- Internet of Things (IoT) and sensor data processing: In IoT applications, data processing is used to clean, filter, and aggregate data streams from numerous sensors and devices, enabling real-time monitoring, predictive maintenance, and automated decision-making.
- Government and public sector applications: Data processing is used by government agencies for tasks such as census data analysis, policy development, resource allocation, and improving public services by leveraging processed data from various sources.
These are just a few examples of the numerous applications of data processing across different sectors and industries. As you can see. Data processing plays an important role in translating data throughout each and every one.
What Are the Stages of Data Processing?
There are several main stages of data processing. Will Hillier breaks down the stages into a data processing lifecycle for Career Foundry, which encompasses the following six stages:
- Data collection: The process of gathering raw data from various sources, such as databases, sensors, surveys, or external providers.
- Data preparation: The tasks involved in cleaning, transforming, and organizing raw data to make it suitable for processing and analysis. This includes steps like data validation, cleaning, integration, and formatting.
- Data input: The stage of introducing the prepared data into the data processing system, such as loading it into a database, data warehouse, or analytical software.
- Data processing: The core stage of performing operations on the input data to convert it into a more useful form or extract insights. This can involve tasks like calculations, sorting, filtering, aggregating, or applying algorithms.
- Data output: The resulting data or information produced after the data processing stage, which can take various forms like reports, visualizations, models, or actionable insights.
- Data storage: The process of securely storing the processed data outputs in a structured manner, typically in databases, data warehouses, or cloud storage, for later retrieval and analysis.
How to Enter the Field of Data Processing
The U.S. Bureau of Labor Statistics (BLS) devotes a great deal of attention on how to enter data processing — and specifically how to become a data scientist. Let’s examine their findings in greater detail here.
According to the BLS, data scientists typically need at least a bachelor's degree in fields like mathematics, statistics, computer science, business, or engineering to enter the occupation. However, some employers require or prefer candidates to have a master's or doctoral degree. At the college level, courses in computer science are crucial in addition to math and statistics to learn data-oriented programming languages and analytical tools. In terms of other experience, some employers may require industry-specific experience or coursework related to the field, such as finance or banking for asset management companies.
Additional important qualities of data processing:
- Analytical skills to research, examine, and interpret findings
- Computer skills to write code, analyze data, develop algorithms, and use visualization tools
- Communication skills to convey analysis results and make business recommendations
- Logical thinking skills to design statistical models and analyze data
- Strong math skills to use statistical methods for data collection and organization
- Problem-solving skills to address issues in data cleaning, model development, and algorithms
The BLS highlights the need for a strong foundation in quantitative fields, coupled with computer and analytical skills, to succeed as a data scientist. Industry-specific knowledge and the ability to communicate findings effectively are also valuable assets.
Overview: What Is Data Processing?
Data processing involves converting raw data into usable information such as graphs, text, charts, and other helpful tools. Data processing is typically performed by data processors, data engineers, data scientists, and the like. The stages of data processing include: data collection, data preparation, data input, data output, and data storage. The field is heavily technical and analytical with an emphasis in communication and programming.