Reverse ETL
Moving data from A to B location is a common problem data people have been trying to solve over the years. First the we had a single ETL job to move data from operational databases to the data warehouses, then we created multiple ETL jobs for different use cases, ETL jobs become ELT jobs as different teams want to label and use data differently, we ended up with tens of data pipelines with hundreds of dbt models. If it works, it works!
Finally Analytics and Data Engineers had to chance to take a deep breath. Our data warehouse or data lake looks so organised and beautiful! The reporting teams are happy with pre-aggregated and cleaned data for their PowerBI or Tableau reports. Data Science and Machine Learning teams can access either raw or combined tables through various locations for their models.
Then suddenly someone from operation teams get closer to you and says; how can I download the data from data warehouse or BI tool to upload to my tools? You feel the goosebump at your arms, your pupils get bigger and search for a place to hide. You invested months or even years to keep everything under your control at Garden of Eden but everything starts collapsing!
You realise that you built something that suits your needs (and maybe what market dictates you as a best practise) but doesn’t solve the problem for the people who need to use the data to improve operations and generate revenue. Today we are going to take a look into the Elephant in the room: Reverse ETL!
Before diving into details of Reverse ETL, we have to learn some new methodologies.
Customer Data Platform
A Customer Data Platform (CDP) is a marketing technology that collects, unifies, and organizes customer data from various sources and makes it accessible to other systems for analysis, segmentation, and personalization. CDPs enable businesses to create a comprehensive, single view of each customer by consolidating data from multiple touchpoints, such as website interactions, email campaigns, social media, CRM systems, and e-commerce transactions.
CDPs are specifically designed to help marketers and customer experience teams better understand and engage with their customers by providing a centralized and consistent source of customer data. They facilitate more effective targeting, segmentation, and personalization of marketing campaigns and customer interactions across multiple channels.
Data at Rest
Data at rest refers to any data that is stored in a non-transitory or non-active state on a digital medium, such as on hard drives, solid-state drives, or other storage devices. This includes data stored in files, databases, or backups that are not actively being accessed, processed, or transmitted.
Data at rest itself does have potential value for organizations, as it can contain valuable insights, historical information, and reference data that can be leveraged for analysis, decision-making, compliance, and other purposes. However, the challenge lies in extracting that value from the data.
Data sitting idle in storage doesn’t directly contribute to organizational objectives or outcomes. It’s when organizations effectively analyze, interpret, and utilize this data that it becomes valuable. Without proper tools, processes, and expertise to extract insights from data at rest, its potential value remains unrealized.
What is Reverse ETL?
Reverse ETL (Extract, Transform, Load) is a process of moving data from a data warehouse or data lake back into operational systems or Software-as-a-Service (SaaS) applications used by your marketing, advertising, and operations teams.
Why do we need Reverse ETL? Especially for CDP
Reverse ETL is critical for Customer Data Platforms (CDPs) because it helps to streamline the integration of customer data with various operational systems, marketing tools, and SaaS applications. This integration allows businesses to efficiently leverage the insights and processed data from the CDP to improve marketing campaigns, customer experiences, and overall business processes.
Let’s take a look some concrete examples;
- Enhanced data utilization: Reverse ETL allows businesses to sync the unified customer data from the CDP with other systems, enabling different teams to access and use that data to make data-driven decisions, drive automation, and personalize customer experiences across multiple touch points.
- Improved marketing effectiveness: By integrating the customer data from the CDP with marketing tools, businesses can better target their campaigns, create more relevant content, and optimize marketing strategies based on customer behaviour and preferences.
- Increased efficiency: Reverse ETL helps automate data syncing between the CDP and other systems, reducing the manual effort required to transfer data, maintain data consistency, and keep systems updated.
- Better customer engagement: With the ability to utilize the enriched customer data from the CDP across various systems and channels, businesses can create more personalized and tailored customer experiences, improving engagement and customer satisfaction.
- Cross-functional collaboration: By making the customer data available across different teams and tools, reverse ETL promotes collaboration between various departments, such as marketing, sales, and customer support, ensuring a consistent and unified approach to customer engagement.
Reverse ETL Landscape
According to G2, here are the components a Reverse ETL should provide;
- Ensure data sync from a data warehouse (source of truth) to business applications
- Support several pre-built connectors to various APIs to facilitate loading data into the applications
- Provide a system for detailed logging, auditing, and updates and provide alerts in case of any issues
Below we can see the landscape of Reverse ETL tools in the market ranked by G2 scoring.
Conclusion
Customer focused companies which try to unify and optimise their marketing, sales and operations need Reverse ETL either through 3rd party tools or in-house solutions. We are in a place that every single bit of customer interaction makes a big impact on the decision making process. We can’t allow our Data at Rest and doesn’t generate any value for our organizations.
