How do you automate a data process?

With digital media companies handling increasing volumes of data every day, it’s no surprise data automation has become a top priority.

In fact, 67% of companies use business process automation solutions to improve visibility across their systems.

So, let’s explore how to automate a data process.

What is automated data processing?

Automated data processing is the use of computing tools to structure, transform, and transfer data with little or no human intervention. The purpose is to process large amounts of information faster and more efficiently, so that it can be presented easily to the relevant users.

Automated data processing examples can range from very simple to highly complex. For instance, a digital camera applies a series of algorithms to raw sensor data and converts this into a digital image file. At the other end of the scale, a complicated ETL pipeline can extract data from multiple sources, apply transformations, and load the completed dataset into another system.

Scientific data processing examples differ from commercial applications in that they typically involve a large number of computational operations but fewer inputs and outputs. They also have a low or zero tolerance for errors, since this would lead to incorrect conclusions.

What are the types of process automation?

When it comes to data processes, there are four main types of automation that can be used, depending on the requirements and application.

1. Batch data pipeline

Batch data pipelines process or transfer data from source to destination in one go. This can be carried out periodically or at predefined intervals, such as transferring a CRM system to a data warehouse on a weekly or monthly basis.

2. Streaming data pipeline

Streaming data pipelines process or transfer data continuously as it is generated. For example, moving real-time data from multiple sources into ML algorithms for analysis to make product recommendations.

3. Change data capture pipeline

Rather than process or update the whole dataset, change data capture pipelines only process or update the differences made since the last change, so only data that has been modified needs to be addressed. This type of pipeline is often used between two cloud services which share the same dataset.

4. Source data automation

This is a pipeline which extracts 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 method of data entry removes the step where data is collected manually, resulting in increased speed, reduction in cost, and fewer human errors.

What are the advantages and challenges of data automation?

Advantages of data automation

There are considerable advantages that apply to all types of data automation. Here are some of the benefits of data automation:

• Increased speed – Automated data systems save significant amounts of time spent processing data because it’s much faster than carrying this out manually. As data sets grow, time savings will continue to increase.
• Improved data quality – The common factor in all data automation examples is that less human intervention results in fewer human errors. An automated process makes for more reliable output, and gives business leaders the confidence to make better decisions based on this data.
• Better scalability – Any changes required can be quickly propagated throughout the data pipeline, whereas manually updating data usually requires expert intervention.
• Better use of talent – Data processing automation tools take care of repetitive tasks, such as standardization and validation, which would normally be time-consuming to accomplish manually. This frees skilled employees up to focus more on high-level tasks, such as data analysis.
• Lower cost – All of these factors make processing data cheaper. Producing more accurate datasets more quickly speeds up decision-making, which in turn enables more profitable activities.

Challenges of data automation

While data automation has many benefits, it also creates some challenges. Here are the three main factors to address:

• High initial outlay – Company decision-makers may balk at the total cost of implementing data automation. Although the initial investment may be high, usually the savings due to automation more than offset its expenses. For instance, one estimate found the reduced cost to be 40-75%.
• Solutions may become redundant – As technology and business needs change, so will the data pipelines needed for automation. If an existing system is made redundant, this will require re-investment in another solution. However, system architects increasingly design components with adaptive capabilities to mitigate this issue.
• Employee displacement – Automation inevitably makes some jobs redundant, but this can also be overcome. Employees can be offered the opportunity to re-skill and change roles as automation plays a larger role within the organization.