In the Spotlight: Data Automation Tools

“Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong.”

This was an assertion made by Suhail Doshi, Founder at Mighty, back in 2015. The fundamental problem back then was that most of the world was using largely manual techniques to harness data. And then came the age of automation.

Today, every data-driven organization needs data automation tools to function successfully. But choosing a data automation platform to suit their needs is no mean feat. Let’s take a closer look at the types of data automation, before exploring real-world data automation examples.

Types of data automation

There are different types of data automation pipelines which can be used depending on the requirements of the data application.

Batch data pipeline – used to process or transfer a large amount of data from source to its destination in one process. This is either carried out periodically or at predefined intervals. A report can then be generated from this data set.

Streaming data pipeline – used to process or transfer data continuously as it is created at the source. For example, streaming data can be used to move real-time data from multiple sources into machine learning algorithms for analysis to make product recommendations.

Change data capture pipeline – used to process or update the differences made since the last sync (rather than the whole data set). Change data capture pipelines are often used between two cloud services which share the same data set.

​Source data automation – used to extract data from a source system in real time. For example, scanning ticket QR codes at an event to authorize entry and update the guest list in real time. This removes the need for manual data collection, resulting in increased speed, reduced cost, and elimination of human error.

Data automation examples

Today, the practical applications for data automation are limitless. Let’s take a look at some data automation examples in action.

Ecommerce: Using data automation tools to update ecommerce websites in real time with supplier data. For instance, a brand retailer buys stock from a wholesaler, which means their product availability and prices are dependent on those of the supplier. Using data automation tools, the retailer can be confident their online catalog is showing products at the right price, as well as advising of low stocks levels.

​Social media marketing: Similarly, brands can use data automation tools to monitor various data sets and post to their social accounts. For example, a real estate agent could plug into live property data and automatically post updates about new property, or price changes, directly to Facebook or Twitter.

What is automation testing?

Just as work processes can be automated, so too can the testing of these processes. Without getting too technical, this typically relies on scripted sequences to examine software or equipment. For example, a computer application can be stress-tested to find bugs, or a robotic production line can be checked at each stage of the manufacturing process for defects.

Testing can be done using a ready-made platform, or a manual programming language with powerful database drivers, such as Selenium or Java with JDBC (Java DataBase Connectivity).

In ETL automation testing specifically, tools are used to test the final data set to ensure the ETL process has been successful, and that it meets your individual business needs. Read more about ETL testing in our recent blog post.

The advantages of automation testing

As with any form of automation, automation testing has a number of advantages:

More real-time feedback – testing software works faster than humans, resulting in a faster feedback cycle, which in turn enables you to flag any issues earlier

Improved error detection rates – automation has a much higher quota on the number of tests that can be performed and verified (compared with manual detection), which generates a higher test coverage.

Reproducibility – applying automation to testing introduces a much higher degree of reproducibility of results

Reduced cost – all these factors combined makes testing far less expensive