A Beginner's Guide to ETL Processes: ETL Stages and Benefits Explained
Analyzing your data is one of the best ways to evaluate performance and make better business decisions. But in order to effectively analyze data from multiple sources, you need to properly aggregate, clean, and store it. According to Forbes, 95% of businesses need new means to arrange their data. That's where an ETL (Extract, Transform, Load) tool can help. In this article, we’ve broken down the ETL process, explained how it works, and demonstrated how an ETL process can help you streamline your marketing data.
What is ETL?
💡ETL is an acronym that stands for Extract, Transform, Load.💡
Essentially, it's the process your data has to go through before you an analyze it. First, you extract the source data from different platforms, then transform the data into a different format, and finally, load the data into a data warehouse.
Typically, organizations implement ETL processes to manage large volumes of data from multiple sources, like ad campaigns or their CRM.
For example, ASUS utilizes Improvado ETL platform to aggregate their marketing data and turn it into actionable insights.
You can pull data from as many locations as you need, create a data flow based on preset parameters, and get a clean set of data at the end. ETL tools are the quickest and most efficient way to manage all that data and turn it into a usable format.
Improvado is also one of the top ETL software based on the clients reviews.
Should Your Team Implement an ETL Process?
There are numerous reasons why companies turn to ETL tools to streamline their data transformation processes. Some of the most common reasons include:
- Making data easier for management and outside stakeholders to understand.
- Handling more data from more sources than manual processes can.
- Customizing and automating the data aggregation process.
- Scaling as you generate more data and run more campaigns.
- Improving efficiency, saving money, and reducing the number of working hours needed on data transformation.
- Formatting the end data exactly how you need it.
- Uploading data into a warehouse, so reports and dashboards can be easily generated.
- Reducing the margin for human error.
Breaking Down the ETL Process into Stages
To help you better understand how the ETL process works, here's a breakdown of each stage.
The first stage is extraction. If you have many data sources, such as files, databases, spreadsheets, etc. that you want to transform into a different format, an ETL tool will automatically aggregate all this data for you. This data goes into what's called a "staging area," where the information is temporarily housed.
Specifically, there are two main types of extraction methods: logical and physical.
When it comes to logical extraction, there are two subtypes.
Full Extraction—Full extraction is used when you are extracting data for the first time, and all of the data is extracted at the same time.
Incremental Extraction—Incremental extraction is used to extract data from the last successful extraction. In an ETL tool, you'll be able to see the timestamp of every data extraction, and view recent changes in a table.
There are two types of physical extractions: online and offline.
Online Extraction—Online extraction is when the ETL tool has a direct connection to the data sources. Improvado uses online extraction to connect to all your different data sources automatically.
Offline Extraction—Offline extraction is when the data isn't extracted directly from the source. Instead, it gets aggregated into a flat file, which can be used to create charts and analyze the data manually.
The extraction process through Improvado's perspective
Let's review the extraction process on the example of Improvado.
Improvado lets data analysts set up an automatic data extraction process from 500+ marketing and sales data sources.
One of the most important features of ETL systems is that they provide an autonomous method of data aggregation. Improvado offers customizable data extraction patterns that allow analysts to determine a precise flow of insights to the data warehouse. With their help, you can specify the data source (Google Ads, Twitter, Instagram, or else), the extraction time, data fields, and other parameters.
Another noticeable functionality of ETL platforms is their ability to modify raw data. With Improvado, users can create custom metrics for their marketing reports and change the data view in any convenient way. Adjustments of metrics, data sources, and target audiences allow analysts to tailor final insights to their specific needs.
In analytics, saving historical data is mandatory to compare previous insights to your current results. That's why ETL solutions should store previously extracted data to help analysts create a more holistic picture of their efforts. When extracting data, Improvado can store it for 12+ months to ensure an in-depth view of the user's marketing and sales performance.
The transform stage is where data transformation takes place. This is when you apply the filters, functions, and any other criteria you want. As the user, you'll have goals and clear visions of how you want the data presented upon completion. Because ETL processes are highly customizable, it's up to you to adjust them to suit your needs.
For example, you may want to combine several data sets to present all the data in uniform. Or, display sales data in a way that's easy to analyze and spot strengths and weaknesses for geographic areas, sales teams, products, and so on. At Improvado, we use our Marketing Common Data Model (MCDM) to align data from different sources and convert it to a single source of truth.
One of the key benefits of this stage over manual transformation is telling the software to make global changes to the data sets. For example, you can eliminate special characters, completely change the layout of data, remove erroneous records, and more.
The transform process through Improvado's perspective
Transforming marketing data to a digestible format is necessary to see the actual results of your marketing campaign. Improvado's DataPrep is a data transformation module that helps to cleanse, deduplicate, and normalize raw revenue data. After the transformation, marketers get analysis-ready data saving a lot of time on manual data processing.
What's even more important, is that ETL systems lower the barriers to entry for non-technical analysts. With a manual approach, they have to manipulate data with the help of SQL queries combined with programming languages, such as R or Python. Improvado and other ETLs offer a spreadsheet-like UI well-known to all data scientists and regular marketers.
Once the data transformation process is complete, the final stage is loading the data into a data warehouse. Loading large amounts of data into a warehouse makes it easy to access and use the data. Regardless of how many different types of data went through the ETL process, the result is one clean set of data that is ready to use.
The loading process through Improvado's perspective
Keeping normalized marketing insights in a data warehouse allows analysts to keep required data at their fingertips and access it with ease.
Besides, the platform connects warehouses with data visualization software to supply real-time dashboards with up-to-date insights. Improvado supports Tableau, Looker, Power BI, and 10+ visualization solutions. The ETL system helps analysts see changes in their marketing campaigns' performance in real-time and analyze the slightest changes.
Furthermore, as a revenue analytics software company, Improvado helps marketers set up insightful dashboards and populate them with proper data.
ETL Benefits for Businesses
If you’re still not convinced about optimizing your data processes with an ETL system, let's look at some of the other benefits.
Data transformation is a time-consuming process when done manually. Writing bits of code for each process, managing data transformation, and developing internal processes requires a lot of time and management. A well-designed ETL system allows for a more "hands-off" approach, so you don't have to invest as many hours into managing the process. For example, learn how we helped a fitness apparel company to save 60 hours a week by removing manual data processes.
Many companies recruit a point person to manage their different source data types. For example, one person might be in charge of overseeing email marketing data, and another is in charge of Google Adwords data. That can create inconsistencies and errors when gathering the data.
As a result, a lot of organizations turn to ETL processes tools because they know the data they are analyzing will be consistent and accurate. It significantly reduces the risk of human or processing errors.
No Developer Expertise
One of the biggest perks to using an ETL tool is that you don't need to work with a developer. There's no coding, custom scripts, or languages you need to know. The best ETL solutions on the market have all the built-in features and tools you'll need to set up and execute data transformation yourself.
Time is money, and efficient processes save time. ETL tools are capable of saving organizations many hours every week by speeding up their data transformation processes.
Implementing ETL solutions early on is just as important as bringing them in when your data processing tasks become too difficult to manage. The software enables you to scale up your processes without the need for rewriting methods that were previously in place.
Companies today need to take advantage of their data in order to stay competitive. But you don't need to rely on time-consuming manual processes to gain valuable insights from your data. With an ETL, you can save time, money, and reduce the risk of human error.
If you're interested in learning how you can benefit from implementing an ETL system in your business, check out a list of Improvado's integrations to see how quick and easy it is to aggregate your data from various platforms.