Reverse ETL: Power Data-Driven Decision-Making at Each Stage
ETL, or Extract, Transform, Load, is a data analytics pipeline process that includes three stages of sourcing, cleaning, and loading data into a repository that can be accessed and operationalized.
However, what if you could reverse ETL? That is, power decision-making at each stage of the process using data collected from the source.
While there’ll always be irregularities and vulnerabilities in your data architecture, reverse ETL is the best way to ensure that everyone is working off the same information and that reporting numbers are accurate and more precisely predict company performance.
This guide will help you understand reverse ETL, why it's helpful, and everyday use cases.
- Reverse ETL enables you to automate the process of getting clean and ready-to-use data from your source systems into downstream analytics and BI tools.
- Use reverse ETL to improve efficiency, flexibility, visibility, and consistency while operationalizing your data.
- Take advantage of dedicated reverse ETL tools and move away from unreliable (and costly) custom solutions or exhausting point-to-point automation.
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What is Reverse ETL?
Reverse ETL is the practice of synchronizing data from a source of truth, typically a data warehouse or data lake, directly to different business applications such as CRMs, ad platforms, ERPs, and many more.
To understand the concept better, here’s a quick refresher on ETL and ELT systems and how reverse ETL is different.
ETL, ELT, and reverse ETL are all data pipelines. They move data from system A to system B while applying transformations to the data along the way. "E" is for "extract," "T" is for "transform," and "L" is for "load." Specifically:
- ETL involves extracting data from one or more sources and transforming it into a format that can be loaded into a target system.
- ELT is a similar process that reverses the order of the Transform and Load steps. The data is first loaded into the target system and then transformed to fit the requirements of that system.
- Reverse ETL reverses the order of the Extract and Load steps. Data is extracted from the source system and loaded directly into the target system without being transformed.
Reverse ETL eliminates the need for an intermediate transformation step, which can save time and resources. However, it also means that the data may not be compatible with the target system and may require more processing before you can use it.
As a result, reverse ETL thrives in situations where the source and target systems are very similar or where there isn’t a need to transform the data.
Benefits of Integrating Reverse ETL
Reverse ETL tools use what's known as a hub-and-spoke approach. Meaning you can use your data warehouse for all outbound connections. Your business tools can pull data from the same trusted underlying source, avoiding potential differences between the many point-to-point integrations.
Here are some advantages of reverse ETL:
- Operationalize data: Surfacing data to each “spoke” in your tech stack empowers your team to take abstract information and turn it into something concrete and measurable.
- Data consistency: By ingesting data from a unified source, you can be confident that everyone is working off the same information. Accessibility to centralized data is crucial for sales and marketing teams who need accurate reports to forecast company performance.
- Improved efficiency: Proper implementation of reverse ETL will save you time and resources by eliminating the need for an intermediate transformation step, especially for your data team. All the API connections integrate with the warehouse, so you don’t have to worry about building or maintaining custom code internally. So reverse ETL frees up your data team to focus on high-value work.
- Greater flexibility: With reverse ETL, you can choose which data to sync and when—allowing you to easily add or remove applications from your reverse ETL process as needed.
- Increased visibility: Reverse ETL provides you with a complete picture of your data flow, making it easy to spot potential errors or areas for improvement.
- Tool consistency: By sending transformed data directly to the business applications, users can stay in their native tool, which they’re more comfortable using over a BI tool.
Reverse ETL Use Cases
Now that we’ve gone over the benefits of reverse ETL, let’s look at some specific use cases where this framework is effective.
Uploading customer data into a CRM
In this scenario, you extract data from your internal systems, for example, your ERP, finance, or order management tool.
This data is then loaded into the CRM system so that sales and marketing teams can access customer information in one central location. You don’t need to transform the data because it's already in a compatible format.
Syncing data between two similar systems
This use case is similar to the one above, but the two systems are not necessarily compatible. You may need to transform the data into the target system before loading it. For example, you may need to convert data from CSV to JSON format.
Migrating data to a new system
You may move from an on-premises data warehouse to a cloud-based solution or switch CRM systems. Whatever the case, you can set up reverse ETL transfer to the data.
This integration would avoid the need to transfer data manually or write custom scripts. Note that you may need to transform the data to fit the new system's requirements.
Creating a backup
Managing backups is an everyday use case for reverse ETL. Data is extracted from the source system and loaded into a backup one. You don’t need to transform the data because it's not required for anything other than backing it up.
How Reverse ETL Fits The Modern Data Stack
The applications of operationalizing your data using reverse ETL are endless. Let's look at three examples of using reverse ETL in your data stack.
Sync Data To CRM For Your Sales Team
CRM tools like Salesforce have some pretty good out-of-the-box reporting solutions and are usually where your sales team will spend most of their time.
You’ll still extract and load raw Salesforce data in your warehouse, combining it with other company data to create custom metrics in your regular ETL/ELT pipeline.
But you can use your reverse ETL tool to sync that new customized data and metrics from your warehouse to your CRM for your sales team.
Your sales team still uses the shared warehouse logic but doesn’t have to go to a separate reporting tool to see it. And they don’t need to create custom reports to figure out what they need.
Use Customer Data For Marketing Campaigns
Your marketing team wants to make a segmented list of customers from your data warehouse for a new marketing campaign. Rather than writing a query and exporting the data, they can use reverse ETL to automatically send the data from your warehouse to a Google Sheet, spreadsheet, or something similar.
The marketing team can then use the data however they need and don't have to rely on the engineering team to get the data for them.
Improve Customer Support With Data
Customer support uses a combination of Slack and Zendesk to manage customer tickets. But what if you could use data from your warehouse to automatically route tickets to the right support agent?
You can use reverse ETL to monitor your data for specific occurrences and then take action accordingly. In this case, you would send a message to Slack that includes the ticket details and assigns it to the right support agent.
This way, your customer support team can focus on solving tickets rather than routing them. And you can be sure that the right ticket is going to the right person.
There’s no limit to what you can do with reverse ETL. The key is understanding how to use it to form a complete data pipeline.
Build vs. Buy: What Reverse ETL Solution To Choose?
While the concept of reverse ETL isn't new, until recently, there weren't any tools available to help with implementation. That's changed with the advent of cloud-based data warehouses.
In the past, you would have to create a custom application to sync data across channels. Such an undertaking would include being responsible for connecting and managing APIs and designing interfaces. More importantly, you must maintain both the product and the code.
The problem with this approach is that one or two engineers need to be available if something goes wrong.
Another approach is trying to mimic data in another BI tool within a dashboard; however, it's challenging to match numbers accurately using this method.
You could also try using automation tools like Zapier or Make. These tools can be effective for small workloads—like if you want to create a one-off trigger. However, the number of these syncs will quickly increase as your needs do, making the automation impractical for anything more than their intended use.
That's where a reverse ETL tool comes in—it provides a way to manage data syncing without needing custom code or relying on engineers.
By limiting the dependency on other departments, you can be up and running faster than with a custom-built solution. It’s also easier to use and maintain, as the vendor will provide support and updates.
More importantly, sending transformed data directly to the business applications means that your team can continue using software they know instead of dealing with an often overwhelming BI tool interface.
Reverse ETL is a scalable solution that can grow with your needs while remaining maintainable. As such, it's quickly becoming the go-to solution for managing data across channels.
Next Steps To Implementing Your Reverse ETL Model
To make data-driven decisions, you need the correct data in the proper format at the right time, and reverse ETL ticks most boxes.
By integrating a reverse ETL system, you can automate the process of getting clean and ready-to-use data from your source systems into downstream analytics and BI tools. As a result, you'll be able to improve decision-making and gain more insights from your data faster than ever before.
If you're looking for a reverse ETL solution that fits your unique needs, our team at Improvado is here to help. We’ll help you build a model and get you up and running to make better and faster data-driven decisions today.
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