Your customers interact with your brand via search, social, advertisements, email, videos, ecommerce tools, and multiple other channels. You’re basically sitting on a gold mine of data and data processing helps you access that gold.
Data processing is the method of transforming raw data into meaningful insights. Without processing data into reliable insights, you’ll have meaningless raw data that won’t help you make strategic business decisions.
For example, let’s say your business launches a new ski apparel line.
Within the first week, sales on the down ski jacket are significantly higher than the other jackets in the new line.
You’ll naturally have questions like:
Without a method for processing this raw data, you won’t be able to get the answers to your questions. And you won’t be able to adjust your apparel promotion strategy accordingly.
Data preprocessing is part of the overall data processing method and is necessary for a reliable data management strategy. It’s the preparation you take to ensure your data is ready for processing.
Preprocessing includes cleaning and organizing data by removing duplicates, removing inconsistencies, and developing standards to future-proof your data management.
By preprocessing your data, you’ll be able to process your data more effectively.
Data processing can be broken down into five distinct stages, each serving a crucial purpose in the overall process.
The first stage involves gathering raw data from various sources such as your company website, social channels, Customer Relationship Management tool (CRM), and ecommerce platform.
Make sure you’re collecting raw data from accurate sources so you can process helpful information for your business.
This raw data may include website cookies, customer support inquiries, social media engagement metrics, user behavior, and purchase history, for example.
In this stage, data is inspected for errors, missing values, and inconsistencies. These issues are addressed to ensure data quality.
For example, you’ll want to remove duplicates like “Maria Seelos” and “M. Seelos” to ensure you’re pulling data from one record.
This is where data is transformed into a structured format and fed into a processing unit such as a CRM.
In this stage, your raw data is finally ready for you to start pulling usable information.
In order to process your data you need to store it. This is where having a Single Source of Truth (SSoT) for your data is helpful.
A SSoT provides a holistic view of all of your data from across multiple sources, allowing you to access your data anytime and manage it more effectively.
Once the data is cleaned and transformed, it is subjected to various artificial intelligence and machine learning techniques to extract meaningful insights and patterns.
Visualizations like charts, graphs, and plots are used to communicate the results of the analysis, making complex information more accessible.
Data processing encompasses various techniques and methods that serve different purposes based on the nature of your data and your goals for processing.
These are the following types of data processing:
Batch processing involves processing data in bulk at scheduled intervals. Data is collected over a period and then processed all at once.
Use Cases: Payroll processing, monthly financial reporting, and data backups.
Real-time processing, also known as online processing, involves immediate data processing as soon as data becomes available.
Use Cases: Online shopping transactions, real-time stock market trading, and sensor data analysis.
Stream processing is a subset of real-time processing that deals with continuous data streams. It involves analyzing and acting upon data as it flows in real-time.
Use Cases: Fraud detection, IoT data processing, and monitoring social media feeds for trends are instances where stream processing is valuable.
Interactive processing focuses on responding to user queries or requests in real-time or near-real-time, often involving human interaction.
Use Cases: Online customer support chats, interactive data visualization tools, and web search engines employ interactive processing.
Distributed processing involves using multiple computers or nodes to process data simultaneously, often to handle large-scale computations efficiently.
Use Cases: Big data analytics, distributed databases, and cloud computing platforms.
Parallel processing is a form of distributed processing where data is divided into smaller chunks and processed concurrently using multiple processors or cores.
Use Cases: Scientific simulations, video rendering, and complex data analysis tasks.
Cloud processing leverages cloud computing resources and services to store, process, and analyze data, offering scalability and flexibility.
Use Cases: Cloud-based data storage, machine learning services, and web application hosting are common applications of cloud processing.
Edge processing involves processing data locally on edge devices, such as sensors or IoT devices, rather than sending all data to a central server.
Use Cases: Real-time monitoring of industrial equipment, autonomous vehicles, and smart home devices employ edge processing to reduce latency and bandwidth usage.
Cognitive processing involves the use of artificial intelligence and machine learning algorithms to analyze and interpret data, mimicking human cognitive functions.
Use Cases: Natural language processing, image recognition, and recommendation systems.
In-memory processing stores and processes data in the computer’s main memory (RAM) rather than on disk, providing faster access and processing.
Use Cases: High-speed data analytics, real-time dashboards, and caching systems utilize in-memory processing.
Image and signal processing involve techniques for enhancing, analyzing, and manipulating images and signals, such as audio and video data.
Use Cases: Medical image analysis, video compression, and audio noise reduction are applications of image and signal processing.
There’s no one-size fits all way to process your data. It’s unique to the amount of data you have and the sources you’re working with. Data processing is the first step toward in-depth data analysis that will help drive your business decisions.