What Is Data Processing?

Today, businesses gather large volumes of data about their operations, vendor partners, and customers as a matter of standard practice. However, simply hoarding data is not enough to optimize business performance.

If you want to put all that information to use, you need to leverage data processing. So, what is data processing, exactly? We’ll tell you everything you need to know. 

What is Data Processing?

Data processing involves gathering information and transforming it into usable insights. Data scientists use specialized software to consolidate, filter, and optimize the dataset. Once they are satisfied with the data’s quality and accuracy, they present it in a digestible format that offers actionable information. 

Types of Data Processing

The most common types of data processing are as follows:

Batch Processing

Batch processing involves gathering data and processing it in large batches. Use this approach to analyze large volumes of data, like quarterly sales information. 

Real-Time Processing

With real-time processing, you process data as it is collected. Use this approach when you need up-to-the-minute insights. For instance, real-time processing can help you track site traffic during a product launch.

Online Processing

Online processing is used to process data continuously. There is often a slight latency, but this process feeds data to your information management system as soon as it is available. You can use this approach to track changing inventory levels.

Multiprocessing

Also known as parallel processing, multiprocessing uses two or more servers to simultaneously process multiple datasets. The servers are part of the same system but offer more computing power than a single device. Data scientists use multiprocessing for advanced analytics, like demand forecasting.

Time-Sharing

Time-sharing allocates computer resources to users in time slots to avoid overloading devices or systems. These tiny time intervals are called quantums or time slices. Transaction processing systems, such as those found on most e-commerce websites, use time-sharing protocols to process multiple customer orders with minimal latency.

6 Stages of Data Processing

There are six steps in most data processing workflows:

1. Data Collection

You can collect data from individual applications or unified storage locations, like data warehouses or data lakes. The key is ensuring your sources are trustworthy. 

When collecting data from one system and routing it to another application, you also need to map it. Mapping data involves matching fields from the source database to fields in the receiving database to promote information usability and traceability. 

2. Data Preparation

Next, you must prepare your data by eliminating duplicate records and correcting errors. Maximizing the quality of your data will help you generate more relevant and accurate insights. 

3. Data Input

During the input stage, you enter the data into its destination, such as a data warehouse or CRM. When consolidating data from multiple sources into a unified location, you need an advanced strategy, like the extract, transform, and load method. Otherwise, you can use a more simplistic input strategy, like a manual data transfer. 

4. Processing 

Processing leverages machine learning algorithms and other analytics tools to organize data, identify trends, and generate practical business intelligence.

5. Interpretation

During interpretation, your data analysts convert the processed data into digestible reports. Most of these reports visualize the data with charts or graphs to make it easy to read and understand. 

6. Storage

Finally, you must store your newly processed data. As a data collector, you are responsible for maintaining the security and integrity of the information you gather. You could face fines and other penalties if your data falls into the wrong hands due to negligence.

Data Processing Methods

These are the most common methods of data processing: 

Manual Data Processing

Manual data processing is time-consuming and labor-intensive, so this method is rarely employed. However, there are a few instances where you may need to use manual processing. For example, if you conduct a paper survey at an expo, you and your team might manually review survey responses.

Mechanical Data Processing

Mechanical data processing uses simple devices. For instance, if you document survey responses in a spreadsheet or crunch numbers with a calculator, you’re processing data mechanically.

Electronic Data Processing

Electronic data processing uses modern computing devices to rapidly collect, process, and analyze information. 

You will need advanced technologies like an open database connectivity API and a database management system (DBMS) to perform electronic data processing. A DBMS stores, retrieves, and queries data from your databases. An open database connectivity API is an interface that helps you interact with your DBMS.

Data Processing Examples

Whether you know it or not, data processing occurs all around you. Let’s look at some real-life examples of data processing: 

• A digital marketing company collects and reviews consumer demographic data to plan its next campaign.
• An e-commerce company analyzes a customer’s purchase history to recommend similar products.
• Accounting software converts quarterly sales data into an easily digestible chart.

These real-world use cases will help you apply data processing techniques to your own business model so you can obtain actionable intelligence about your industry and customer base. 

Data Processing: Next Steps

Cloud technology is foundational to future-proof data processing strategies. Cloud-based data management provides exceptional scalability while building on the convenience of existing data collection and processing methods.

Your organization needs a robust management information system to support cloud-based data processing, as well as a powerful communication solution to streamline the flow of data between you and your trading partners. Orderful’s API-powered platform makes it easy to exchange data, communicate with vendors, and keep everyone on the same page.

Simplify Data Processing with Orderful

Don’t let antiquated communication tools hinder your ability to process and share data. Transition to Orderful and make collaboration effortless.

With Orderful, you can support EDI payments, securely exchange documents, and standardize communications. Speak to an expert today to discuss how Orderful can take your business to the next level.