Data is all around us. What began as a byproduct of globalized technology and the rise of computer technologies has become a valuable asset for organizations. By the year 2025, about 450 exabytes data will be generated every day worldwide – that’s an unimaginably large amount of data that will likely take plenty of resources and time to process manually. This is where Data Automation becomes a necessity in today’s data-driven world.

As data grows exponentially, managing it can become challenging and expensive. To counter the growing expense of data management, nearly 24% of organizations are already implementing low-code workflow automation software. In this article, we will take a deep dive into how automating your data workflows can benefit your organization, and increase operational efficiency.


What is Data Automation?

Data automation is an innovative process that involves using automated technologies to manage, upload, process, collect, clean, and store data in an organized fashion rather than relying on traditional manual processes.

This approach can involve using computer programs, simulations, and artificial intelligence to optimize data-related tasks and improve workflow accuracy.

Data automation techniques include data preparation, validation, transformation, and analytics, among others.

The primary objective of data automation is to increase operational efficiency and reduce errors in data processing. 

This frees up human resources, which can be utilized on analytical tasks that require advanced critical thinking and decision-making skills. With data automation, organizations can unlock unparalleled productivity gains, streamline processes, and achieve better overall outcomes.


Why is Automating Data Processes Important?

Data automation is a game-changer for businesses seeking to enhance their productivity and improve customer satisfaction.

By automating repetitive tasks, your businesses can save valuable time and effort, allowing for greater efficiency and workforce optimization.

Here are some of the advantages of automating your data:


  • Reduce errors

The advanced algorithms used in data automation significantly reduce the need for human input, minimizing errors in data processing. This enables human resources to focus on high-level tasks such as analysis, decision-making, and management.

  • Reduce time taken

Automating your data processes frees up employees from time-consuming tasks, enabling them to focus on more critical tasks. This results in faster task completion times and enhanced overall efficiency.

  • Better efficiency

Data automation has been around for decades in more rudimentary forms. But recent technological advancements have made it more efficient than human labor. With minimal human intervention, it can process vast amounts of data 24/7, leading to better outcomes and enhanced productivity.

  • Highly scalable

Data automation offers scalable and cost-effective solutions for businesses of all sizes. It can track key data points, find insights, and summarize them in a single report, which can then be distributed to different departments or stakeholders without human intervention.


Components of Data Automation


Data Automation involves three essential components: Extract, Transform, and Load, or ETL for short.


Data Automation involves three essential components, known as Extract, Transform, and Load, or ETL for short.



In the extraction phase, data is extracted from various sources, such as databases, applications, spreadsheets, or other data sources. 


In the transformation phase, the data is processed and transformed into a common format or schema, which can be used for analysis. This includes cleaning and filtering data, as well as converting it into a consistent structure that can be used across multiple systems. 


In the loading phase, the transformed data is loaded into a target system, where it can be accessed by business intelligence tools for analysis and decision-making.


Step to Implementing Data Automation Solutions

Implementing data solutions in businesses can be a complex process, but it becomes simpler when a business can recognize its requirements.

Here are some key steps to help get started:


Identify Your Data Needs

Start by identifying your business’s data needs, including what data needs to be collected, how it needs to be processed, and who needs access to it.

Evaluate Your Current Data Processes

Assess your current data processes to identify areas for improvement, such as data entry, data analysis, and data storage.

Choose a Data Automation Tool

Choose a data automation tool that best suits your business’s needs. Consider factors such as functionality, scalability, and ease of use.

Define Your Automation Workflows

Define your data automation workflows, including the data sources, the data transformation steps, and the output formats.

Implement Automation Workflows

Implement your automation workflows by configuring your automation tool according to your defined workflows. Test your workflows and refine them as necessary.

Train Your Team

Train your team on how to use the data automation tool and how to interpret the data generated by it.

Monitor and Optimize

Monitor the data workflows regularly to ensure that they are working as expected. Optimize the workflows as necessary to improve efficiency and accuracy.


When to Optimize Data Analytics with Automation


24% of organizations are already implementing low-code workflow automation software

24% of organizations are already implementing low-code workflow automation software


Knowing when to replace human effort with data automation can be a tricky balance. In general, automation works best for tasks that are rules-based and need to be performed frequently.

Determining when to automate data analytics depends on several factors – the type of data being analyzed, the complexity of the analysis, and the frequency.

Broadly speaking, data analytics tasks that are rules-based, repetitive, and performed frequently are ideal candidates for automation. These may include tasks such as data cleansing, data transformation, and report generation.

Automating data analytics is of value when dealing with large datasets that would take significant resources to analyze manually.

However, not all data analytics tasks are suitable for automation. One-time studies with a high level of complexity need the expertise of a human analyst to ensure accurate results.


Driving Business Growth with FLIP: The DataOps Automation Tool

In conclusion, data automation has become a necessary asset for organizations looking to stay agile and effective with their data. As the growth of data exponentially rises, businesses have now turned towards DataOps tools that can automate their data processes for them.

FLIP is an automated DataOps tool developed by Kanerika that enables businesses to automate their data processes in a zero-code interface easily. It comes pre-loaded with industry-standard templates and functional rules that give the power of data in the hands of business owners. Sign up now for a free account today!


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