Data processing

What is data processing?

Data processing is the series of steps involved in collecting, transforming, and organizing raw data into meaningful insights. Every company has many data sources, which could support you in decision-making and many other activities. Data processing involves integrating all these data sources, cleaning and changing them into suitable formats to glean actionable insights out of them.

Modern enterprise data is vast in volumes, comes from various sources, and has various formats (structured, unstructured, etc). Dealing with these complex data sets is often called big data processing.

There are five steps involved in data processing, which are data collection, data preparation, data transformation, data analysis, data storage, and data output.

1. Data collection means aggregating data from sources like ERP, CRM, databases, sensors, company files, and other systems.

2. Data preparation is the second stage of data processing, where you use data cleansing techniques to remove duplicates, missing values, and outliers. 

3. Data transformation includes changing data into a suitable format for analysis, like normalization, manipulation, smoothing, mapping, etc.

4. Data storage is where you move the transformed data into a centralized storage database or a warehouse for future use.

5. Data analysis is where you use statistical techniques, models, or algorithms to turn data into business analytics. It also involves converting the analysis output into visualizations, charts, and reports.

Tools required for data processing include Apache Kafka, Amazon S3, Apache Hadoop, etc. 

Why is data processing important?

To make data-driven decisions: To make accurate and growth-driving business decisions, you need data that’s processed, cleaned, and has no misleading values. Data processing is mandatory to get useful and actionable insights from your data.

Scale data management easily: If your data volumes increase in the future, you will be able to manage effortlessly and continue receiving insights without affecting productivity or performance.

Improves data quality: data processing ensures that your data is clean, consistent, up-to-date and usable, so your business can confidently make decisions relying on that.

Risk management: Data processing becomes mandatory to ensure that your data is securely handled and that all security and compliance measures are in place. 

Example of data processing

Let’s say that a retail company wants to analyze the buying trends of its customers so they could refine their marketing and targeting strategies. Here is how a typical data processing would happen in this case. 

It involves collecting data from sources like CRM, store logs, transactional systems, social media, and other customer records. 

After aggregating data, it involves cleansing data, removing duplicates, and standardizing everything into the same format. Some data processing tools you could use for this purpose are Python, OpenRefine, etc.

This processed data will be used for analysis after the company moves to a destination using ETL pipelines like Talend. Other list of ETL tools you could use are FiveTran, Matallion, Azure data factory, etc. Later, with the help of visualization tools like Power BI, the company uses the stored data for analysis and identifies key metrics like most bought products, customer lifetime value, advertising ROI, etc.

Use cases of data processing

• Data processing is an integral part of ETL (Extract, Transform, and Load). ETL processing is needed for extracting and transforming datasets. 

• Data processing is also required for streaming data analysis, real-time analytics, etc.

• AI use cases like sentiment analysis, fraud detection, predictive analytics, recommendation systems, supply chain management systems, etc. 

Types of data processing

Depending on the nature of data and how it's processed, data processing could be of many  types. Let’s see what the seven types of data processing: batch processing, real-time processing, online processing, distributed processing, parallel processing, multiprocessing, automated processing,

Batch processing is when the data is processed in batches at scheduled times. Examples: payroll systems, billing systems, etc.

Real-time processing: Here, the data processing happens as soon as the data is generated. Example: social media analytics, fraud detection, traffic monitoring, etc.

Online processing processes and updates real-time transactions that happen in great volumes. Example: payment processing, eCommerce site management, etc.

Distributed processing is when data processing happens across multiple nodes, servers, or devices concurrently. Example: cloud computing

Parallel processing refers to the usage of two or more CPUs for efficient data processing, especially while handling complex queries or use cases. Examples: complex machine learning models.

Cloud processing is when the data processing uses cloud resources for storage, compute, or networking needs. 

Manual processing is about the manual data analysis and handling by users, especially when the data volumes are very low. Examples: small business data collection or user research.

Based on your use case and requirements, you will use one of these data processing types. 

Without data processing, it’s hard to understand your business and market trends and where it’s going. Data processing and big data processing are crucial for businesses to reap benefits from huge volumes of raw data.