What is ETL? Everything marketers need to know

What are ETL tools? Plenty of professionals use them to make data collection and data analysis easier. As a marketer, you will likely use ETL solutions to measure campaign success.

Collecting and analyzing data sounds simple enough until you need to work with multiple formats or discover complexities you never imagined. ETL helps simplify those complexities through a process called "extract, transform, load".

Once you see the benefits of ETL, you can integrate even more tools into your evaluation process to improve your marketing strategies and key performance indicators (KPIs). They also come with some disadvantages, but don't worry. We'll break it all down for you.

What is ETL?

ETL is a data integration process that stands for "extract, transform, and load." Reliable ETL tools can extract data from multiple sources, transform data into a unified format, and load the transformed data into a target system, such as a cloud data warehouse or data lake.

Data scientists and other data professionals might use complex approaches to data extraction, data transformation, and data loading. Many of them might even learn how to build manual data pipelines.

Marketers and other business users rarely have the time or desire to learn about building data pipelines. You just want a simple way to prepare data, so you can use data analytics apps to spot trends and measure success.

ETL tools can help, because you can take structured and unstructured data from practically anywhere, reformat it, and send it to your data processing app.

The ETL process explained

⬇️ Extract:

Extract means gathering data from its source – like a database or an application.

🔀 Transform:

Transform means cleaning, de-duplication and standardization.

⤵️ Load:

Loading is sending the transformed data to a data warehouse or similar place where it can be used in BI tools, for example.

What is an ETL process?

Let's take a closer look at how the ETL process performs data integration. We will describe Extract, Transform and Load: 

1. Extract raw data

An ETL platform can extract data from multiple sources simultaneously. For example, you might use ETL tools to collect:

• Survey results
  
• Email response rates and other email data
  
• Performance marketing data
• Organic website traffic
  
• Sales on e-commerce platforms
  

An ETL platform acts like a series of pipes that connect these and other data sources to a single destination.

2. Transform data to clean it and make it ready for analysis

Before data can reach its destination, an ETL must put all information in the same format and check for data quality.

For some professionals, data transformation might be a hard-to-grasp concept. When you drill into the details, it can get very confusing.

The good news is that you don’t need to know how the data integration process works. As long as your ETL tool does the job well, you can combine data from multiple sources. The data quality check means any incomplete or corrupted data values will get removed so they don’t skew your results. 

3. Load the data into a data warehouse or other data destination

Once your extracted data has been reformatted and cleansed, the ETL software can load it to a data warehouse or other destination.  

Where you put your data depends on how you want to use it. If you just want to store information so you can review it later, you can use pretty much any data store your business has.

Technically, you will need to differentiate between structured and unstructured data.

Structured data has a standardized format and doesn’t usually include any text. It includes information like your bounce rate and conversion rate. You can put structured data into a database.

Unstructured data can include live chat messages, survey results, and social media exchanges. It’s often text-based and hard to quantify. This type of information can go into a data lake.

A quick guide: data lakes and data warehouses

What’s the difference between data lakes and data warehouses, and how does that affect your ETL tools and processes?

Data warehouses:

• Store and manage structured data, so a data warehouse is a good target system for an ETL process involving organized data sets.
• Index and optimize query performance for efficient data extraction and transformation.
• Centralizes data, making it consistent and minimizing errors.
• Make strategic decisions easier with historical patterns and trends.
• Can scale with you as your data volume grows – perfect for enterprise environments.
• Data warehouses integrate with business intelligence (BI) tools and reporting platforms, for seamless insights. 
• Comply with regulatory standards in data governance and security.

Data lakes:

• Allow for structured, semi-structured and unstructured raw data, which means more flexibility to accommodate diverse types of data.
• Are more cost effective than a data warehouse if you’re storing large volumes of data.
• Can be more agile than a data warehouse, and allow for experimentation in data analytics or transformations without the need for an upfront data schema or data modeling.
• Integrate seamlessly with big data tech, letting businesses perform complex data processes at greater scale than a data warehouse.
• Gives access to raw data that can be explored and analyzed by data scientists.
• Support real-time data processing for faster insights and decision-making.

You usually won’t need to worry about choosing a data lake or a data warehouse, because the vast majority of popular ETL tools will direct data flows to the correct type of target system or destination.

You can also load data to business intelligence and data analytics applications. For the most part, though, it makes sense to store the information in one or multiple databases. Otherwise, you might lose access to source data you need later.

Want to dive deeper? Try this: An introduction to marketing data warehouses or watch our YouTube video below. 

ETL vs. ELT

As you explore options for ETL options for marketing data, you will almost certainly run into ELT platforms too. They're both data integration processes –  so what's the difference?

With ETL: Extract, Transform and Load

So, data transformation happens within the ETL’s server before loading it to the destination.

With ELT, the process’s steps go:

1. Extract
   
2. Load
   
3. Transform
   

The difference between how ELT and ETL tools and processes work.

The ETL process loads raw data to the destination. Data cleansing and formatting take place in the target system.

ETL and ELT are not the only data integration methods, of course. If you want to dive a bit deeper, have a look at our blog all about data integration here. 

How are ETLs used?

Now that you have a better idea of how ETL works, you can start applying the concept to marketing campaigns.

As a marketer, you could use ETL to:

• Collect data from multiple sources
  
• Reformat the data
  
• Load the transformed data into a single source
  
• Use business intelligence tools (BI) to analyze the data
  
  

Related reading: Which data transformation tools are best for digital marketers?

ETL use case: business intelligence

Some large companies have dedicated BI teams that rely on ETL tools to reformat and cleanse data before analyzing it.

Power Digital provides a use case that shows how business intelligence teams can use ETL to save time and improve insights. The marketing company collects data from diverse sources, including Shopify, Google Ads, Good Analytics, and Facebook Ads. Some of its data destinations include Google Data Studio, Google Cloud Storage, Google Sheets, and Amazon S3.

When Power Digital adopted Funnel (which is notably not just an ETL) as a tool capable of backing up historical data, the company’s BI team:

• Reduced its data collection process time to about one hour.
  
• Saved each team member three to four hours of work per month.
  
• Benefited from custom connectors that save hundreds of hours per month and avoid high engineering costs.
  

Now, the marketing business has access to reliable data, avoids unnecessary data refreshes, and enjoys considerably higher efficiency that leads to deeper insights without long wait times.

Imagine you're a creative marketer who dreads keeping up with challenging technologies. Now, imagine how much better you would feel when your employer adopts an ETL solution that doesn't require a lot of technical knowledge. What a relief! You can do great work and analyze the results with help from a user-friendly, no-code ETL platform.

Benefits of using an ETL

Like any digital tool, you will find benefits and disadvantages when using an ETL. No platform works perfectly for every company in every situation.

Review the following pros and cons of adding an ETL to your data analysis and collection process. 

The benefits of using an ETL tool or process.

1. Delivering a single source of truth 

Data can’t do much for you when you store it in multiple locations. Today’s companies need to embrace digital transformation to get away from inefficient, siloed legacy systems. ETL can help by moving data from all of your sources to one destination.

Now, you have a single point of view that makes it easier for you to manage and store data. The next time you analyze data, you don’t need to worry if someone didn’t include information from one of your sources. An ETL’s data pipelines can automatically collect data from all of the sources you use. Just set them up and let them do the work for you. 

2. Improving efficiency and productivity

If your company has an application development team, they probably know how to transfer and reformat data manually. That’s a huge waste of resources and potential, though. An ETL automates the process so your development teams can focus on innovation that helps your products stand out.

You might ask your developers to work on special data transfer projects. For the most part, though, an ETL can handle the job without requiring much oversight from experienced — and well-paid — data professionals.

3. Providing historical context

Effective marketing insights often come from discovering trends in data collected over time. Some ETL tools can combine legacy enterprise data with information from a specific platform. This gives you a large data cache you can use to determine which aspects of your marketing campaign work well and which need fine-tuning.

With an ETL, you get the opportunity for unbiased analysis based on historical and recent data.

Disadvantages of using an ETL

Using an ETL can benefit a lot of organizations, but the technology isn’t right for everyone. The following disadvantages might prevent you from adopting ETL for your data integration.

1. ETLs can be expensive

While you save time and money by automating your data collection process, you can expect many ETL platforms to charge high prices. Some of the most popular ETL software costs $8,000 or more per month. How much you pay often depends on how much data you move.

A few thousand dollars per month probably doesn’t mean much to a large company that wants to streamline processes. Small businesses, however, might struggle to find room in their budgets for an ETL.  

2. ETLs are not very flexible when it comes to data transformation

ETL developers design products to meet the needs of most users. If you want to transform common data formats, ETL software can almost certainly help. Unfortunately, an ETL doesn’t offer much data transformation flexibility that applies to unique projects.

You could find yourself needing a data scientist even after you pay for an ETL. That team member might not need to build many custom pipelines, but having them on hand certainly helps. Otherwise, you risk losing some data that could lead to greater business insights.

3. Many end-users lack the technical know-how to use an ETL effectively

Not all ETL tools are easy to use, especially for marketers who don’t have much – or any – experience writing code and working with data. The creative people building your marketing campaigns should have the opportunity to focus on creating good design and writing effective copy. They don’t want to spend a lot of time gaining the technical know-how to use ETL tools efficiently.

Luckily, they don’t have to. Some ETL tools give users drag-and-drop interfaces that require very little technical knowledge. Instead of getting data engineers to build data pipelines manually, you can simply connect data sources to destinations without any manual coding.A drag-and-drop solution also makes it easier for you to transform data by showing your options. You don’t need to know it’s impossible to turn format X into format Y. Your data pipeline only lets you choose viable options. 

Again, you might need data engineers or similar professionals to build custom pipelines on occasion. By and large, though, your marketing team can use a no-code ETL to perform daily tasks. With these types of ETL tools, a little onboarding goes a long way.

4. An ETL doesn't store data

ETL tools use pipelines to move and transform data. They do not, however, store data. You will need a separate platform, such as a marketing data warehouse, for that. You can dive deeper into data warehousing on the blog here.

While that’s a drawback for ETL solutions, you can find platforms that combine ETL and data warehousing. These platforms can work with data from sources and move information to a database or similar destination. But they also store historical data so you always have a backup available.

Learning to use data warehousing could help your marketing team analyze more data. It’s worth learning, but you don’t need to lose access to the data you move in the meantime.

Building an ETL strategy

So how do businesses set up an ETL process? First it’s important to understand your data and goals. Before diving into the technical aspects, it's essential to have a clear understanding of your data and business objectives.

• Identify data sources: Determine where your data resides (databases, files, APIs, etc.).
• Define data requirements: Understand the specific data elements needed for analysis and reporting.
• Set clear goals: Establish what you want to achieve with the ETL process (data warehousing, reporting, machine learning, etc.).

7 steps to build an ETL strategy

1. Data profiling and assessment
   The first stage is all about understanding the data, the amount to process, and ensuring that any issues are cleaned. That means:

• Analyzing data quality, consistency, and completeness.
• Identifying potential data issues and cleaning requirements.
• Understanding data volumes and velocity to determine ETL tools and infrastructure.

1. Data modeling:

This stage involves designing the structure of your data to ensure it effectively supports your business needs. It includes:

• Designing the target data structure (data warehouse, data mart, or other).
• Creating data models that align with business requirements.
• Defining relationships between data elements.

1. ETL process design:

Here, you outline the specific steps involved in moving and transforming your data.

• Outline the ETL pipeline, including extraction, transformation, and loading steps.
• Determine data transformation logic (cleaning, formatting, calculations, etc.).
• Define error handling and recovery procedures.

1. Tool selection:

Choosing the right tools is crucial for efficient ETL. 

• Evaluating ETL tools based on data volume, complexity, and budget.
• Considering open-source options or commercial tools.

1. Data quality and validation:

Ensuring data accuracy is paramount. This step focuses on:

• Implementing data validation checks at each stage of the ETL process.
• Defining data quality metrics and monitoring processes.
• Establishing data governance policies to ensure data accuracy and consistency.

1. Testing and deployment:

Before going live, thorough testing is essential. Businesses will need to:

• Develop comprehensive test cases to verify data accuracy and transformation logic.
• Conduct performance testing to identify bottlenecks and optimize the ETL process.
• Deploy the ETL pipeline into a production environment.

1. Monitoring and maintenance:

The ETL process is an ongoing journey, so it’s important to observe and revisit your strategy ongoing.

• Continuously monitor ETL job performance and data quality.
• Implement alerts for data anomalies or errors.
• Schedule regular maintenance and updates to the ETL process.

Using data to meet your marketing goals

A marketing data hub can play critical roles in marketing success by connecting your data sources, transforming data formats, and putting information in a location right where you want it. You just have to find a solution that works well for your team.

Check out: Platform overview

FAQs

What are the potential ROI benefits of using ETL for marketing?

The potential ROI benefits of using ETL for marketing are significant. By streamlining data integration and analysis, ETL can help marketers:

• Improve decision-making: ETL provides a centralized and consistent view of marketing data, enabling marketers to make data-driven decisions more quickly and accurately.
• Increase efficiency: ETL automates many manual data tasks, freeing up marketers to focus on strategic activities.
• Enhance customer segmentation and personalization: ETL can help create detailed customer profiles, allowing for more targeted marketing campaigns.
• Optimize marketing spend: By analyzing campaign performance data, marketers can identify areas for improvement and allocate resources more effectively.
• Gain competitive advantage: ETL can help uncover insights that competitors may not have access to, providing a competitive edge.
• Improve customer satisfaction: ETL can enable marketers to deliver more relevant and personalized customer experiences, leading to increased satisfaction and loyalty.
• Measure marketing ROI: ETL can help track the effectiveness of marketing campaigns and measure their ROI, providing valuable insights for future planning.

What are the key factors to consider when choosing an ETL tool?

When choosing an ETL tool, keep in mind the following:

• Data volume and complexity: The amount and type of data you need to process will significantly impact your choice. Some tools are better suited for large-scale data processing, while others are more efficient for smaller datasets or specific data formats.
• Scalability: Ensure the ETL tool can handle your growing data needs. Consider its ability to scale horizontally (adding more nodes) or vertically (upgrading hardware).
• Performance: Evaluate the tool's performance in terms of data extraction, transformation, and loading speed. Look for features like parallel processing and optimization capabilities.
• Integration capabilities: The ETL tool should integrate seamlessly with your existing data sources, data warehouses, and other systems. Consider the availability of connectors and APIs.
• Flexibility and customization: Assess the tool's ability to handle complex data transformations and customizations. Look for a flexible architecture and scripting capabilities.
• Ease of use: If you're not a data engineer, consider the tool's user interface and ease of use. Look for features like drag-and-drop interfaces, guided workflows, and pre-built templates.
• Cost: Evaluate the pricing model (per-user, per-node, or subscription-based) and consider factors like maintenance costs and support fees.
• Support and community: Look for a tool with a strong support team and an active community. This can be helpful for troubleshooting issues and finding resources.
• Cloud vs. on-premises: Decide whether you prefer a cloud-based or on-premises solution based on your IT infrastructure and security requirements.
• Future-proofing: Consider the tool's long-term viability and its ability to adapt to evolving data technologies and standards.

What are the challenges in implementing ETL in large organizations?

Data complexity and volume: Large organizations often deal with massive amounts of data from diverse sources, making ETL processes complex and resource-intensive.

Data quality issues: Inconsistent data formats, missing values, and errors can hinder ETL efficiency and accuracy.

Legacy systems: Integrating ETL with older, legacy systems can be challenging due to compatibility issues and varying data structures.

Performance and scalability: ETL systems must be able to handle high data volumes and complex transformations while maintaining acceptable performance levels.

Data governance and security: Ensuring data security, privacy, and compliance with regulations is crucial in large organizations, adding complexity to ETL implementations.

Integration with other systems: ETL systems must often integrate with other enterprise applications, such as data warehouses, BI tools, and CRM systems, which can introduce challenges.

Change management: Implementing ETL can involve significant organizational changes, requiring careful planning and communication to ensure buy-in from stakeholders.

Technical expertise: ETL projects often require specialized technical skills and expertise, which can be challenging to find and retain in large organizations.

Cost: The cost of implementing and maintaining an ETL system can be substantial, especially for large organizations with complex data requirements.