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Blog / What is ETL (and how does it help with marketing analytics)?

What is ETL (and how does it help with marketing analytics)?

Collecting data, cleaning it, and making sense of it, is sometimes a lot to deal with. So how do you take control of data and get it to work for you? The answer is ETL.

But what is ETL, what does it mean, and why is it important to marketing? In this post, we define what ETL is and why it plays a crucial role in marketing analytics.


What is ETL?

ETL stands for ‘extract, transform and load’, and essentially describes a three-step process of extracting data from a source, transforming it so it is cleaned, harmonized, and can be compared with other data sources, and then loading it into a target database such as a data warehouse or BI tool.



ETL stands for extract, transform, load



Why is ETL important for marketing analytics?

To explain the importance of ETL for modern marketing analytics, let’s take a look at each stage in more detail.


Extracting data refers to acquiring data from a particular data source, for example, extracting data from your Google Ads platform. This is arguably the most crucial step of the three since, in order for ETL to be done efficiently, data needs to be collected directly from its source and in its rawest form.

Now, in its simplest form, extraction can be the simple act of manually exporting CSV files directly from Google Ads. However, while this sounds simple enough, this gets much more complicated when you are looking at multiple data sources that you want to compare. This is where transformation comes in.


In ETL, Raw data is extracted direct from sourceRaw data extracted direct from source



Transforming data essentially means to strip it of the various formatting that is unique to that datasource and then apply a new set of formatting that is consistent with all the data from all your data sources so they can be compared.


In ETL, data is harmonized to a single schema mappingData harmonized to a single schema mapping

For instance, Google Ads famously uses the term ‘Cost’ whereas Facebook Ads uses the term ‘Spend’. Thus, in order to compare both data sets, you need to reformat both sets of data so that both use the same terminology. This is known as Schema Mapping and is a central part of the Transformation process.

The data can then be enriched by adding new metadata that, for example, convert everything to a single currency or add additional region and channel information. Lastly, some ETL processes will also include a naming convention phase during transformation to ensure that things like campaign names are all consistent and any errors or discrepancies are avoided.


In ETL, data is enriched by converting to a single currency and adding regional and channel metadata 

Data is enriched by converting to a single currency and adding regional and channel metadata 



The last step is loading the data into a target destination, including business intelligence (BI) tools and database applications, ready to be analyzed.

At this stage, marketers and marketing analysts, have what is sometimes called a single source of truth. Because all their data is all in one place and consistent in terms of formatting, they are now able to compare the data to draw useful insights into how their campaigns are performing on different channels or platforms so they can adjust their strategies and budgets accordingly.


Is ETL the same as data integration?

In short, no. While ETL and data integration are often used interchangeably, nowadays, data integration as a term refers to a combination of several processes, including ETL but also, for example, ELT (see: What’s the difference between ETL and ELT?) or data federation processes.

It is important to note that, with the advance in data marketing technologies, today ETL alone is rarely considered enough to handle the growing amounts of data out there.

What is ETL Best Practice?

Ultimately, ETL describes a process and this process can be done manually. In other words, you could export CSV files from all your different data sources and start manually converting all the numbers, copy and paste them into new columns, add in meta-data, clean up inconsistent naming, and then upload it all to a database.

This is sometimes called manual data wrangling and, for any but the smallest of tasks, is not advisable. While the above example might not look too complicated, it is only examining a single discrepancy between just two data sources. If you throw in all the discrepancies those data sets might have (date formats, impression figures, etc.) it gets much more complex.

Then add the fact that you might be looking at data from not just two but often three, four, five, or even more data sources, and this process can take an extremely long time. Moreover, because of human error, the likelihood of inaccuracies creeping in rises dramatically.

This is why, today, most marketing analyst teams utilize sophisticated tools that can automate the ETL process making it significantly faster and more accurate.

Learn more about ETL

Today, ETL has become a crucial tool for marketers ensuring they can get the most value out of an ever-increasing number of data sources, platforms, and channels.

To learn more about ETL, check out our Definitive Guide to ETL & Data Integration for Marketers.

For more information on best practices, check out our ETL workshop.


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