What is data processing?

Historically, businesses relied on manual data processing and calculators to manage smaller datasets. As companies generated increasingly large volumes of data, advanced data processing methods became essential.

Out of this need, electronic data processing emerged, bringing advanced central processing units (CPUs) and automation that minimized human intervention.

With artificial intelligence (AI) adoption on the rise, effective data processing is more critical than ever. Clean, well-structured data powers AI models, enabling businesses to automate workflows and unlock deeper insights. Without high-quality processing systems, AI-driven applications are prone to inefficiencies, bias and unreliable outputs.

Today, machine learning (ML), AI and parallel processing—or parallel computing—enable large-scale data processing. With these advancements, organizations can draw insights by using cloud computing services such as Microsoft Azure or IBM Cloud®.

Although data processing methods vary, there are roughly six stages to systematically convert raw data into usable information:

1. Data collection: Companies might gather large volumes of data from sources such as Internet of Things (IoT) sensors, social media or third-party providers. Standardizing data management practices in this step can help streamline subsequent data processing tasks.
   
   
2. Data preparation: This step involves data cleaning, validation and standardization to maintain high-quality datasets. ML algorithms powered by Python scripts can detect anomalies, flag missing values and remove duplicate records, improving accuracy for analysis and AI models.
   
   
3. Data input: After curation, raw data is brought into a processing system such as Apache Spark through SQL queries, workflows or batch jobs. By prioritizing data protection during ingestion, businesses can stay compliant, especially in highly regulated environments.
   
   
4. Analysis: Algorithms, parallel processing or multiprocessing can uncover patterns in big data. Integrating AI here can help reduce the need for manual oversight, which speeds up data analysis.
   
   
5. Data output: Stakeholders can visualize data analysis outcomes by using graphs, dashboards and reports. Quick decision-making depends on how easily users can interpret these valuable insights, especially for forecasting or risk management.
   
   
6. Data storage: Processed data is stored in data warehouses, data lakes or cloud computing repositories for later access. Proper data storage practices aligned with regulations such as the General Data Protection Regulation (GDPR) can help businesses maintain compliance. 

Data processing helps organizations turn data into valuable insights.

As businesses collect an increasing amount of data, effective processing systems can help improve decision-making and streamline operations. They can also help ensure that data is accurate, rich in security and ready for advanced AI applications.

AI and ML tools analyze datasets to uncover insights that help organizations optimize pricing strategies, predict market trends and improve operational planning. Data visualization tools such as graphs and dashboards make complex insights more accessible, turning raw data into actionable intelligence for stakeholders.

Cost-effective data preparation and analysis can help companies optimize operations, from aggregating marketing performance data to improving inventory forecasting.

More broadly, real-time data pipelines built on cloud platforms such as Microsoft Azure and AWS enable businesses to scale processing power as needed. This capability helps ensure fast, efficient analysis of large datasets.

Robust data processing helps organizations protect sensitive information and comply with regulations such as GDPR. Security-rich data storage solutions, such as data warehouses and data lakes, help reduce risk by maintaining control over how data is stored, accessed and retained. Automated processing systems can integrate with governance frameworks and enforce policies, maintaining consistent and compliant data handling. 

High-quality, structured data is essential for generative AI (gen AI) models and other AI-driven applications. Data scientists rely on advanced processing systems to clean, classify and enrich data. This preparation helps ensure that data is formatted correctly for AI training.

By using AI-powered automation, businesses can also accelerate data preparation and improve the performance of ML and gen AI solutions. 

Advancements in processing systems have redefined how organizations analyze and manage information. 

Early data processing relied on manual entry, basic calculators and batch-based computing, often leading to inefficiencies and inconsistent data quality. Over time, innovations such as SQL databases, cloud computing and ML algorithms inspired companies to optimize how they process data. 

Today, key data processing technologies include:

Cloud-based processing systems provide scalable computing power, allowing businesses to manage vast amounts of data without heavy infrastructure investments. Frameworks such as Apache Hadoop and Spark process real-time data, enabling companies to optimize everything from supply chain forecasting to personalized shopping experiences. 

The rise of machine learning algorithms transformed data processing. AI-powered tools such as TensorFlow streamline data preparation, enhance predictive modeling and automate large-scale data analytics. Real-time frameworks such as Apache Kafka optimize data pipelines, improving applications such as fraud detection, dynamic pricing and e-commerce recommendation engines.

To reduce latency and improve real-time data analysis, edge computing processes information closer to its source. This is essential for industries that require instant decision-making, such as healthcare, where split-second decisions carry high stakes.

Localized data processing can also enhance customer interactions and inventory management by minimizing delays.

Quantum computing is poised to revolutionize data processing by solving complex optimization problems beyond traditional computing capabilities. As the number of use cases grows, quantum computing has the potential to transform fields such as cryptography, logistics and large-scale simulations, accelerating insights while shaping the future of data processing.

Companies can adopt different data processing methods based on their operational and scalability requirements:

• Batch processing: This method processes raw data at scheduled intervals and remains a cost-effective option for repetitive workloads with minimal human intervention. Batch processing is best suited for aggregating transactions or routine tasks such as payroll.
  
  
• Real-time processing: Real-time processing is vital for time-sensitive applications, such as healthcare monitoring or fraud detection, where data output is needed instantly. Automatic data validation, machine learning and low-latency tools can help organizations respond to events as they unfold.
  
  
• Multiprocessing: Multiprocessing distributes data processing tasks across several CPUs to handle big data efficiently. This approach is valuable for data engineers running complex data analytics in parallel, reducing total processing time.
  
  
• Manual data processing: As the name suggests, manual data processing involves human intervention. Although slower, this method can be necessary in regulatory contexts or when precise human judgment is needed to avoid errors—such as in specialized audits or critical data entry activities.
  
  
• Online processing: Online processing supports continuous real-time data interactions in environments such as social media or e-commerce. By constantly updating datasets, online processing can match user behavior analytics with dynamic system responses, deploying ML algorithms to refine experiences in real time.

Organizations face several challenges when managing large volumes of data, including: 

• Quality issues
• Scalability constraints
• Integration complexity 
• Regulatory compliance

Inadequate data cleaning or validation can result in inaccuracies, such as unintentional redundancies, incomplete fields and inconsistent formats. These issues can degrade valuable insights, undermine forecasting efforts and severely impact companies.

Consider when Unity Software lost roughly USD 5 billion in market cap due to a “self-inflicted wound” brought on by “bad proprietary customer data.” By maintaining rigorous data quality standards and reducing manual oversight, organizations can boost reliability and uphold ethical practices throughout the data lifecycle.

Traditional processing units or legacy architectures can be overwhelmed by expanding datasets. And yet, by 2028, the global data sphere is expected to reach 393.9 zettabytes.¹ That’s roughly 50,000 times the number of bytes as there are grains of sand on Earth.

Without efficient scaling strategies, businesses risk bottlenecks, slow queries and rising infrastructure costs. Modern multiprocessing and parallel processing methods can distribute workloads across several CPUs, allowing systems to handle massive data volumes in real time.

Bringing together raw data from different providers, on-premises systems and cloud computing environments can be difficult. According to Anaconda’s 2023 “State of Data Science” report, data preparation remains the most time-consuming task for data science practitioners.² Various types of data processing might be required to unify data while preserving lineage, especially in highly regulated industries.

Carefully designed solutions can reduce fragmentation and maintain meaningful information in each stage of the pipeline, while standardized processing steps can help ensure consistency across multiple environments.

Regulations such as GDPR make data protection a critical priority. Fines for noncompliance totaled approximately EUR 1.2 billion in 2024.³ As data processing expands, so do regulatory risks, with organizations juggling requirements such as data sovereignty, user consent tracking and automated compliance reporting.

Unlike processing steps focused on performance, regulatory solutions prioritize security and data quality. Techniques such as data minimization and encryption can help companies process raw data while adhering to privacy laws.