Automated Data Processing: Definition and Examples

There used to be a time when the topic of automated data processing was confined to computer science classrooms, idea labs, and development hubs. That is no longer the case.

With the advent of the current technology wave and the rising popularity of tools like MonkeyLearn, EdenAI and Make, automated data processing is everywhere -  and understanding it is paramount.

In this article, we’ll provide you with a series of definitions to grasp the concept and implications of automated data processing, including examples, solutions, tools, and its benefits.

Let’s not waste another minute and jump right into the topic.

What is automated data processing?

Automated data processing consists in using technology to sort through large amounts of data and fulfill a specific goal. 

Let’s break this definition down for clarity. 

By “technology”, we mean hardware (i.e. computer processors) and software, both of which help process data without human intervention, or with partial human intervention depending on the setting. 

“Large amounts of data” is also a broad concept: It can go from a spreadsheet with a few thousand records to a machine-learning model with billions of parameters getting processed in nanoseconds.

In today’s world, you can think of automated data processing as a lightning-fast process that sorts through data to produce a clearly-defined outcome. 

The outcomes can range from solving simple mathematical operations (i.e. calculating the average in a group of numbers) to creating human-like dialogues between fictional characters (i.e. natural language generation).

The end result of automated data processing can vary as well, but oftentimes it is linked to improving and maintaining products and services, and also to sustaining business operations at large. 

Examples of automated data processing

You can find examples of automated data processing all around us.

It's present in the spreadsheets we use to track business performance, in the search engine we type in words to find information, and in the drive-by-wire systems that control the majority of modern cars.

To keep things simple, let’s focus on the business examples of automated data processing, which are perhaps the most common and relevant ones.

For instance, calculating and posting weekly revenue figures to a Slack channel is a task that requires the processing of data (adding numbers with a timestamp), and that can be easily automated as well. 

Another example would be to analyze how users or patrons feel about your business. To achieve this, you can process data from public reviews, and understand what the prevailing sentiment is. 

You can go into Google My Business, read and qualify each review by hand, or automate the process entirely with the help of Make and MonkeyLearn.

As you can see, the first example is a simple one, as it automates a simple mathematical operation and then delivers the results to the stakeholders.

The second example is a more complex one, as it uses machine learning to accurately understand and qualify user sentiment from dynamic inputs (business reviews).

How to automate data processing

There are many ways to automate data processing in your business. 

Depending on your needs, skills, budget, and scale (among other requirements), these range from creating Excel macros to using Make or hiring a team of coders to deploy a custom automation solution.

And of course, you’ll need to have data to process in order to automate. 

If you don’t know the characteristics of your data - how it originates, where it gets stored, and what the standard management practices are - it is advised to start with that.

Next, you’ll need to understand the possibilities within that data, and then select the right tool to process it according to your particular needs and requirements. 

This is the long path, but at the same time, you can start tinkering with Make and automate data processing to fulfill specific goals - for example, adding LinkedIn leads to a spreadsheet (instead of having someone do this, you can automate the task). 

Benefits of automated data processing

As it happens with the examples, the benefits are all around us, as automated data processing is a key aspect of modern products and services.

In the context of business, automated data processing is one of the main (and most demanded) drivers of productivity, efficiency, and higher margins. 

This happens due to a number of reasons: When data is processed automatically, you can expect fewer errors, reduced costs, and an increase in outputs.

How long does it take a regular person to go through a stack of paper, or to review a couple of dozen spreadsheet records one by one? 

A simple automated solution does the same in a couple of seconds or less.

In addition to costs and errors, automated data processing is the cornerstone of scaling a business, as it multiplies the capacity but not the costs. 

For example, imagine an ecommerce company that manually reviews orders, to the tune of say, 10 orders per person per hour. Even the most basic automated data processing systems amplify that capacity by a large margin!

Final thoughts: A stepping stone to high-octane business growth

Now that you know the basics about automated data processing, we hope that the next steps you’ll take will be directed towards identifying the opportunities within your business to make the most out of the available data.

And if you’re curious and want to start building your own solutions, we highly encourage you to try out Make. 

Create an account, start your learning journey, and you’ll be developing useful solutions to boost your business before you know it!