According to Forbes, 95% of companies need to manage unstructured data and use it to gain actionable insights. Data processing is the way to do just that. Unfortunately, poor data management occurs when companies don't make full use of the information they collect.

Table of Contents

  1. Understanding Data Processing
  2. Data Processing Stages
  3. Methods of Data Processing
  4. Data Processing with

Understanding Data Processing

If you have ever viewed a graph detailing results from a survey or other gathered data, you have seen data processing. Data processing begins with gathering data and ends with using it or storing it for use in the future. The process happens almost continuously as new data pours in from the everyday workings of your business.

Companies process two primary types of data: structured and unstructured. Most data is unstructured, meaning it has no pre-defined schema. By contrast, structured data formats according to a schema.

While these two types of data may require different processing methods, they both go through the same six stages.

Data Processing Stages

The six steps of data processing are collection, preparation, input, processing, output/interpretation, and storage.

1. Collection

You collect data from a variety of sources, including data lakes and warehouses. When transferring data from these sources, it is essential to use secure protocols to protect your clients' interests.

2. Preparation

Data preparation is when you discard worthless data and organize what remains to get the best value from the information. You can also call this "data cleaning," and it is absolutely necessary in order to improve the quality of data.

3. Input

Now the data is ready to move to a destination, often some kind of BI (business intelligence) tool or CRM (Customer Relationship Management) software.

4. Processing

Before you can properly interpret data, you must process it through the use of various algorithms. These algorithms sort data further and transform it into something that is understandable to the user. This is where excels, helping you extract, transform and load (ETL) data on the fly.

5. Output/Interpretation

Reporting dashboards and similar BI tools help interpret and present data, making it actionable. You do not have to be a data analyst to understand the output from most of these tools.

6. Storage

Quality data can have lasting use. After processing, you can store it so you can access it as needed.

Security is essential throughout these stages for the sake of compliance and protecting your clients.

Methods of Data Processing

What processes you use to manage data may depend on factors such as critical deadlines, data set size, and level of redundancy. Here are three of the most significant options:

Real-time processing: You can use this when you need to process data seconds after you collect it. Real-time processing simply bypasses data with errors so it can get through the set as quickly as possible. However, you can only use this for relatively small data sets. A major sub-method is online processing, which continuously processes data, moving it directly to the CPU upon input. You use it when you are constantly receiving input from new transactions — for example, from purchases in stores, etc.

Batch processing: Large data sets usually require batch processing. This cannot make the data available as quickly as real-time processing. You perform it at regular intervals, and it is best suited to situations in which large amounts of data are input at the same time.

Distributed processing: This is a good option for tasks that require additional redundancy or extra processing power. Distributed processing uses two or more processors for the same task.

Regardless of which method you use, speed and ease of management are likely to be top priorities for data processing.

Data Processing with

You may need to take a second look at data processing and how you can do it more efficiently. gives you an opportunity to do that. Whether you are a small startup or a well-known business seeking to improve upon a legacy system, allows you to build data pipelines in minutes with no coding experience.

With, you can perform secure ETL and automate data processing. This will also ensure compliance with HIPAA (Health Insurance Portability and Accountability Act), GDPR (General Data Protection Regulation), and other standards required by law. Our platform features a drag-and-drop GUI (graphical user interface) and connections to over 140 different data sources.

Contact us today to enjoy a 14-day demo and learn more about how you can simplify and speed up your data processing.