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ETL Automation in 2024: 7 Ways to Improve Business Processes

As digital transformation accelerates, keeping up with data growth becomes challenging for enterprises. Traditional methods for leveraging big data are manual and insufficient for data growth.

One of the newest methods adopted by enterprises to manage data is extract, transform, and load (ETL) automation. Using ETL automation, enterprises can:

This article explores ETL automation and ways enterprises can improve their business processes through ETL automation.

What is ETL automation?

ETL automation refers to using data warehouse automation using software tools or services to automatically perform Extract, Transform, and Load (ETL) tasks, which are crucial to data warehousing and analytics (Figure 1). These are known as ETL automation tools. ETL automation streamlines the data warehouse lifecycle pipelines by minimizing manual processes and errors, enhancing data integrity, and speeding up data preparation and delivery.

Figure 1: The ETL process

Source: informatica

Here’s a breakdown of the ETL components and how they’re automated:

  1. Extract: This process involves collecting or retrieving data from various sources, ranging from databases, excel files, web services, APIs, and more. Automation in this context can involve scheduling regular data pulls or triggering extraction based on certain events.
  2. Transform: After extraction, the data typically needs to be cleaned, filtered, enriched, and formatted to meet the specific requirements of the destination system. Automation here can include applying predefined transformations, including mapping, joining, splitting, or aggregating data, on a consistent, repeatable basis.
  3. Load: This is the final step, where the transformed data is loaded into a data warehouse or another destination system for storage or further analysis. Automated loading might involve scheduled data pushes to the destination or event-driven loading when new data becomes available.

Why should businesses use ETL automation?

1.Data integration

Organizations often use many software applications, relational databases, and systems, each with its data format and structure. The ‘Extract’ stage in the ETL process gathers data from these disparate sources, preparing it for integration. The ‘Transform’ stage then processes this raw data, normalizing it to a consistent format. Finally, the ‘Load’ stage brings the transformed data into a central repository. The result is an integrated view of business data, which can be incredibly valuable for gaining holistic insights into business operations and performance.

2.Data quality improvement

Poor data quality is a significant issue for many organizations and can lead to faulty business decisions and strategic missteps. ETL processes often include data cleaning and validation steps as part of the transformation stage. This can involve handling missing values, removing duplicate entries, standardizing data testing and validating data formats, and checking against predefined data quality rules. By ensuring the data is clean and accurate before it enters the data warehouse, ETL improves the reliability and value of business analytics and reporting.

3. Extract data using different tools and environments

In a large enterprise, entering or retrieving data manually is one of the pain points in large enterprises. The manual transfer of large amounts of data between different resources and data warehouses exposes an inefficient, error-prone, and difficult process. For instance, a global company suffered a USD 900 million financial loss because of a human lapse in the manual entry of loan payments.

ETL automation tools can eliminate many problems businesses face during data management. Enterprises can retrieve data easily from any platform, tool, or legacy system and transfer it to a data warehouse. Automating the entire process frees enterprises from overnight manual data entry. 

4. Automated exception management

Whether automated or not, data collection and entry errors are inevitable. Correcting these errors in the shortest possible time and with the least possible loss is of great importance to organizations.

Unlike batch scheduling, ETL automation offers a rule-based plan for the detection and remediation of exceptions. Without a manual effort from staff, it automatically prevents escalations and reports errors. Meanwhile, the data transfer process continues to run without any interruptions or delays. 

5. Get deeper insights and business intelligence 

The ultimate goal of ETL is to support better business intelligence (BI) and data analytics. Once data has been consolidated and transformed via ETL, it can be more easily queried, analyzed, and visualized. This allows for more effective reporting, trend analysis, predictive analytics, and other data-driven activities. By enabling a deeper, more accurate understanding of business performance, customer behavior, market trends, and operational efficiency, ETL can lead to more informed decision-making and strategic planning.

6. Ensure security and compliance of business processes

As the amount of data grows, securing data becomes critical. Enterprises with data stores and processing large amounts of data must ensure the security of the stored data and remain compliant with legal requirements. Enterprises need to know who has access to what data and its location.

ETL processes can help organizations comply with various data privacy regulations and standards. Data anonymization or pseudonymization can be incorporated into the transformation step to protect sensitive information. ETL automation tools typically maintain a record of data lineage – the path data takes from its source to the data warehouse. This can be invaluable for auditing purposes and for tracking down the source of any data quality issues.

7.Data Migration 

When organizations decide to change or upgrade their systems, ETL processes play a crucial role in migrating data from one system to another. The data from the old system can be extracted, transformed to match the requirements of the new system, and then loaded into the new system, all while minimizing data loss or corruption.

7 challanges to ETL automation

ETL automation’s challenges are often intertwined with the benefits covered above. Thus, organizations should be aware of the challenges when making the most of ETL automation.

1-Complex data integration

If an organization uses many different systems and has multiple data sources and formats, designing ETL processes that can handle this variety can be complex and time-consuming. In addition, new data sources can emerge over time, which might require updates to existing ETL processes.

2-Data quality issues

Even with automation, data quality can still be a significant challenge. The ‘garbage in, garbage out’ principle still applies: if the input data is poor in quality, the output data will be as well. Automated ETL processes need to include robust data validation, production data monitoring, and cleaning as steps to address this.

3-Performance and scalability

As the volume and velocity of data increase, ETL processes need to scale accordingly. Performance optimization becomes a challenge, especially for real-time ETL processes. The chosen ETL tools and the underlying infrastructure need to be able to handle the data load efficiently.

4-Error handling and recovery

Automated ETL processes should be designed to handle errors gracefully. If an error occurs during extraction, transformation, or loading, the process needs to be able to recover without losing data or causing downstream issues.

5-Security and compliance

Data handled by ETL processes can often be sensitive, and ensuring it is securely managed is a must. Compliance with data protection regulations is also a significant consideration. ETL testing automation tools need to provide robust security features, and ETL test processes should be designed with security and compliance in mind.

6-Change management

Over time, business requirements, data structures, and even source data and systems can change, requiring updates to ETL processes. Managing these changes while minimizing disruption to ongoing operations can be a significant challenge. 

7-ETL testing process

Testing ETL processes can be complex due to the need to validate data transformations and ensure the process works as expected under various conditions. This includes checking the accuracy of data transformation, the reliability of data loading, the performance of the ETL testing, and cloud data migration testing. You can read our ETL testing best practices article for deeper insight into the subject.

Frequently Asked Questions (FAQ) about ETL

Can ETL automation be integrated with existing systems?

Yes, ETL automation tools are designed to be compatible with a wide range of data sources and systems. They can be customized to fit the specific needs of your existing IT infrastructure.

Is ETL automation suitable for businesses of all sizes?

Businesses of all sizes can benefit from ETL automation. For small businesses, it can scale data handling without significant infrastructure investments. For large enterprises, it can manage vast amounts of data efficiently.

Can ETL automation handle unstructured data?

Modern ETL tools are increasingly capable of handling unstructured data. They can extract meaningful information from texts, images, and other non-traditional data formats, although this may require more sophisticated transformation processes.

Is coding expertise required to implement ETL automation?

While having coding expertise can be beneficial, many modern ETL tools provide graphical user interfaces and pre-built components that reduce or eliminate the need for manual coding.

How can one measure the success of ETL automation?

Success can be measured by improved data processing times, reduction in manual labor, lower error rates in data, enhanced reporting capabilities, and ultimately, the positive impact on business decision-making and cost savings.

Further Reading

To learn more about ETL and automation, feel free to read our articles:

If you would like to have a data-driven solution for your business, we have prepared a list of ETL tool vendors.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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