ETL and AI: how this duo can boost your data project

1. Introduction

Every day, companies generate data non-stop, from sales, customers, inventory, marketing , and operations. This data comes from different systems, scattered spreadsheets, messages, and even sensors. The problem? Without preparation, this data accumulates like loose pieces of an impossible-to-assemble puzzle.

According to a study by Experian , 95% of companies say that poor data quality directly impacts their results. This means decisions based on inaccurate information, constant rework, and missed opportunities.

But there is a way to transform this scenario: structuring the data flow from the source , ensuring that it is collected, standardized, and made available reliably. That's exactly what ETL does, and when we add artificial intelligence (AI) to this process, the gain is exponential . More than efficiency, it's the possibility of accelerating projects and decisions at the pace the market demands.

In this article, we will explore how the combination of ETL and AI is changing the game in data integration. Together, these technologies not only connect multiple sources, but also improve the quality of information and pave the way for faster decisions and more solid results .

Enjoy your reading!

2. What is ETL and how does it work in data preparation?

Today, a large portion of the data that companies produce is simply not used. A global study by Seagate indicates that 68% of the information available in organizations is never leveraged. This means that a gigantic volume of data remains inactive, losing value every day .

ETL, an acronym for Extract , Transform , Load , is the methodology that prevents this waste . It collects raw information from different sources, organizes and standardizes it, and delivers everything ready to be used in analysis and decision-making. In practice, it is the basis for any solid data strategy, whether in Retail, Healthcare, Finance, or any other segment that depends on reliable information.

2.1. ETL Stages

Before discussing automation and the role of AI, it's worth understanding the three stages that underpin ETL , a crucial process for transforming large volumes of data from diverse sources into reliable and usable information:

• Extract : collects data from various sources, internal systems, spreadsheets, APIs, sensors, bringing everything together in a single stream;

• Transform : processes and standardizes information, correcting errors, eliminating duplicates, and applying business rules to make it consistent;

• Load : sends the completed data to a centralized environment, such as a data warehouse or data lake , where it can be securely analyzed.

When these phases work together, the data cease to be disconnected fragments and begin to have real value for decision-making. But ETL is not the only way to structure this flow: there is also the ELT model , which we will learn about in the next section.

3. ETL vs. ELT: Understand the difference

Despite having almost identical acronyms, ETL and ELT follow very different paths for preparing data, and the choice between one and the other can change the pace and efficiency of the entire project.

In ETL ( Extract, Transform, Load ), data leaves the source, goes through a cleaning and standardization process before reaching its destination. It's like receiving a pre-reviewed report : when it arrives at the central repository, it's ready for use, without needing adjustments. This format is ideal when reliability and standardization are a priority from the very beginning—something critical in areas such as Finance, Healthcare, and Compliance .

In ELT ( Extract, Load, Transform ), the logic is reversed . First, the data is quickly loaded into the destination, usually a high-processing-power environment such as a data lake or lakehouse . Only then does it undergo transformation. This approach excels when the volume is large, the format is varied, and the need is to store everything quickly to decide later what will be processed and analyzed.

In short:

• ETL : prioritizes quality and consistency in input;
• ELT : prioritizes speed and flexibility in transformation.

Knowing which model to adopt depends not only on the type and volume of data, but also on how it will be used in your analytical environment . And this choice becomes even more interesting when we look at modern data architectures, which is the topic of our next section!

4. ETL in modern data environments

As data volume grows, simply "storing everything" is no longer enough: it's necessary to choose the right architecture and define how ETL will operate in that environment so that the information arrives reliably and ready for use. Among the most adopted options today are data lakes and lakehouses , each with specific advantages and ways of integrating ETL.

4.1. In data lakes : centralization and preprocessing

A data lake functions as a large repository of raw data, capable of receiving everything from structured tables to audio or image files. This freedom is powerful, but also dangerous : if the data lake is populated with low-quality data, it quickly becomes a "swamp" of useless information.

Therefore, in many projects, ETL is applied before the data enters the data lake , filtering, cleaning, and standardizing the information right at ingestion. This pre-processing ensures that the repository remains a reliable source, reducing rework costs and accelerating future analyses.

4.2. In lakehouses : flexibility for structured and unstructured data

Lakehouse created to combine the flexibility of a data lake with the organization of a data warehouse . It stores raw data but also offers performance for fast queries and complex analyses.

In this environment, ETL can be leaner : often, data is loaded quickly and only transformed when it reaches the analysis stage. This is useful for projects that need to test hypotheses, integrate new sources, or work with constantly changing data, without stalling the process in lengthy preparation steps.

In short, ETL can assume different roles depending on the type of architecture , ensuring quality from the input or offering flexibility for transformation later. With this foundation defined, AI comes into play, capable of automating and accelerating each of these steps, with the power to elevate the efficiency of the data pipeline

5. How AI empowers and automates ETL

The application of AI elevates ETL from a process with fixed rules to a system that operates autonomously and intelligently . Instead of simply following programmed instructions, an pipeline analyzes, interprets, and acts upon the data and its own functioning. This transformation occurs through specific mechanisms that make the process more dynamic and predictive.

Check out the AI ​​mechanisms behind each ETL capability:

1. Self-configuring data mapping : In a traditional process, a developer manually connects hundreds of fields between systems. AI automates this task by analyzing metadata and data content to identify similarities. Its algorithms compare column names, formats, and information patterns, inferring that, for example, " cod_cliente " in one database corresponds to " customer_id " in another, and then perform the mapping without human intervention.

2. Pipelines that predict and prevent their own failures : Instead of the reactive "break and fix" model, AI introduces proactive maintenance. Machine learning are trained with historical execution data (such as duration, volume, CPU usage) to learn what constitutes "normal behavior." By detecting a deviation that precedes a failure, such as a sudden increase in API latency, the system can warn of an impending problem or even reallocate resources to prevent it.

3. Data transformation that understands meaning : AI goes beyond structure and understands context. Using Natural Language Processing (NLP), it can interpret free text and classify its content semantically. A customer comment, for example, is automatically categorized as "complaint about delivery" or "praise for the product." This capability enriches the data with a layer of business intelligence at the time of transformation, something that manual rules cannot do with the same precision.

4. Execution driven by business relevance, not by the clock : the rigidity of schedules (e.g., running every day at 2 AM) is replaced by adaptive orchestration. Event detection systems monitor data flows at the source in real time, and AI models are trained to recognize important business triggers. An anomalous sales spike, for example, can trigger an ETL cycle immediately, ensuring that insights about that event arrive while they are still actionable, not hours later.

In this way, AI effectively transforms ETL from a simple passive conduit of information into a true "central nervous system" for company data . It not only transports data but also interprets, reacts, and learns. And it is this transition from a passive infrastructure to an active and intelligent system that unlocks the strategic gains we will see next!

6. Benefits of AI-powered ETL automation for data management

When the “nervous system” of data becomes intelligent, the impact reverberates throughout the organization, transforming operational liabilities into competitive advantages. Therefore, automating ETL with AI is not an incremental improvement: it's a leap that redefines what's possible with information . The benefits manifest in four strategic areas.

6.1. Unlocking human capital: from “data cleanup” to innovation

A company's most expensive talent shouldn't be wasted on low-value tasks. However, research shows a worrying scenario: data scientists still spend up to 45% of their time on preparation tasks alone, such as loading and cleaning data.

This work, often described as "digital cleanup," not only drains financial resources but also the motivation of hired professionals to innovate . AI-powered automation takes on this burden, freeing up engineering and data science teams to dedicate themselves to predictive analytics, creating new data products, and seeking insights that truly drive the business.

6.2. Capitalizing on time: agility in seizing opportunities

In today's market, the relevance of data has an expiration date. Therefore, the ability to act quickly is a direct competitive advantage. An agile transformation, driven by accessible data, can reduce the time to market for new initiatives by at least 40% , according to McKinsey .

An automated ETL with AI drastically shortens the " time-to-insight ," the time between data collection and the decision it informs. This allows the company to react to a change in consumer behavior or a competitor's move in real time, capturing opportunities that would be lost in an analysis cycle of days or weeks.

6.3. Trust as an asset: the end of decisions based on "gut feeling"

Poor decisions are costly, and the main cause is low-quality data. Gartner estimates that poor data quality costs organizations an average of US$12.9 million per year .

An AI-powered ETL pipeline attacks the root of this problem . By autonomously and consistently validating, standardizing, and enriching data, it creates a reliable "single source of truth." This eliminates uncertainty and debate about the validity of the numbers, allowing leaders to make strategic decisions based on solid evidence and statistical rigor presenting trends, deviations, and probabilities, rather than intuition or conflicting information.

As a reinforcement, it's worth remembering a practical point: investing in automation is pointless if the data source is unreliable . Loose spreadsheets, manual notes, or uncontrolled records can be easily altered, compromising the entire analysis. That's why discipline surrounding data collection and monitoring is as important as the technology applied in processing.

6.4. Efficiency that generates cash: reducing the hidden cost of inefficiency

Manual and inefficient processes represent an invisible cost that erodes revenue. Forbes research indicates that companies can lose up to 30% of their revenue annually due to inefficiencies, many of which are linked to manual data processes.

Automating ETL with AI generates a clear return on investment (ROI) : it reduces direct labor costs for pipeline , minimizes infrastructure expenses by optimizing resource use, and, most importantly, avoids indirect costs generated by errors, rework, and missed opportunities. And of course, this previously wasted capital can be reinvested in growth.

It is clear, therefore, that the benefits of intelligent ETL go far beyond technology. They translate into more focused human capital, agility to compete, safer decisions, and a more financially efficient operation. The question, then, ceases to be whether AI automation is advantageous, and becomes how to implement it effectively. This is where the experience of a specialist partner, such as Skyone, makes all the difference.

7. How does Skyone put this duo to work?

At Skyone , our philosophy is that data technology should be a bridge, not an obstacle Skyone Studio platform at the heart of the strategy.

Instead of a long, monolithic project, our approach focuses on simplifying and accelerating the data journey.

The initial challenge of any data project is the "connector chaos": dozens of systems, APIs, and databases that don't communicate with each other. Skyone Studio was built to solve exactly that. It acts as an integration platform, lakehouse , and AI that centralizes and simplifies data extraction . With a catalog of connectors for the main ERPs and systems on the market, it eliminates the need to develop custom integrations from scratch, which in itself drastically reduces project time and cost, as well as the flexibility to create new, customized, and adaptive connectors.

Once Skyone Studio establishes the continuous data flow, our team of experts applies the intelligence layer. This is where the concepts we discussed become reality: we configure and train AI algorithms to operate on the data flowing through the platform, performing tasks such as:

• Validation and standardization : ensuring that data such as CNPJs (Brazilian company tax IDs), addresses, and product codes follow a single standard, automatically correcting inconsistencies;

• Data enrichment : cross-referencing information from different sources to generate more complete data. For example, combining purchase history (from the ERP) with interaction records (from the CRM) to create a 360º view of the customer;

• Anomaly detection : Monitor flows to identify unusual patterns that may indicate either a problem (a system failure) or an opportunity (a sales spike).

With data properly integrated by Skyone Studio and enriched by AI, we deliver it ready for use in the destination that makes the most sense for the client —whether it's a data warehouse for structured analytics, a data lake for raw data exploration, or directly into BI tools like Power BI .

Therefore, our differentiator is that we don't just sell an "ETL solution." We use Skyone Studio to solve the most complex part of connectivity and, on this solid foundation, build a layer of intelligence that transforms raw data into a reliable and strategic asset.

If your company seeks to transform data chaos into intelligent decisions, the first step is to understand the possibilities! Talk to one of our specialists and discover how we can design a data solution tailored to your business.

8. Conclusion

On its own, data can be a burden. Without the right structure, it accumulates like an anchor, slowing down processes, generating hidden costs, and trapping company talent in a reactive maintenance cycle. Throughout this article, we've seen how traditional ETL began to erect this anchor and how AI has transformed it into an engine.

The union of these two forces represents a fundamental paradigm shift. It transforms data integration from an engineering task, executed in the background, into a business intelligence function that operates in real time. The pipeline ceases to be a mere conduit and becomes a system that learns, predicts, and adapts, delivering not just data, but trust .

In today's landscape, the speed at which a company learns is its greatest competitive advantage. Continuing to operate with a manual and error-prone data flow is the equivalent of competing in a car race using a paper map. AI-powered automation is not just a better map: it's the GPS, the onboard computer, and the performance engineer, all in one place.

With this solid foundation, the next frontier is to specialize the delivery of these insights . How do you ensure that the Marketing team, for example, receives only the data relevant to their campaigns, maximizing performance?

To explore this specialized delivery, read our article "Understanding what a Data Mart and why it's important" and discover how to bring data intelligence directly to the areas that need it most.

FAQ: Frequently asked questions about ETL and AI in data projects

The world of data engineering is full of technical terms and complex processes. If you're looking to better understand how ETL and AI (artificial intelligence) connect to transform data into results, this is the right place.

Here we've gathered direct answers to the most common questions on the subject .

1) What does ELT mean and how does it differ from ETL?

ELT stands for Extract , Load , Transform . The main difference between the two is in the order of the steps:

• ETL ( Extract , Transform , Load ): data is extracted, transformed (cleaned and standardized) on an intermediate server, and only then loaded into the final destination (such as a data warehouse ). It prioritizes the delivery of data that is already ready and consistent.

• ELT ( Extract , Load , Transform ): Raw data is extracted and loaded immediately into the destination (usually a data ake or lakehouse in the cloud). Transformation occurs afterward, using the processing power of the destination environment itself. It prioritizes speed of ingestion and flexibility to handle large volumes of varied data.

In summary, the choice depends on the architecture: ETL is classic for on-premise with structured data, while ELT is the modern standard for the cloud and big data .

2) What types of data sources can an ETL process access?

A modern ETL process is source-agnostic, meaning it can connect to virtually any data source. The list is extensive and includes:

• Databases: both traditional ( SQL Server , Oracle , PostgreSQL ) and more modern ( NoSQL such as MongoDB );
  
• Management systems (such as ERPs and CRMs): data from platforms like SAP , Totvs , Salesforce , etc.;
  
• Excel spreadsheets , CSV, JSON, and XML files;
  
• Web service APIs : social media information, marketing e-commerce platforms , and other cloud services;
  
• Unstructured data: the content of documents (PDFs), emails , and texts, which can be processed with the aid of AI (artificial intelligence).

3) Is it possible to start automating ETL even without 100% structured data?

Yes, and this is one of the scenarios where the combination of ETL and AI (artificial intelligence) stands out the most. Unstructured data (such as texts, comments, emails ) or semi-structured data (such as JSON files with variable fields) are a challenge for manual processes.

AI, especially with Natural Language Processing (NLP) techniques and the evolution of LLMs (Large Language Models), can "read" and interpret this data. It can extract key information, classify the sentiment of a text, or standardize information contained in open fields. In this way, AI not only enables automation but also enriches this data, making it structured and ready for analysis, something that would be impractical on a human scale.