A robust data warehouse is an invaluable asset. But for most companies, it’s serving reporting, or worse yet, it’s simply data hoarding with no clear objective.
Your 6-figure investment into building and hosting your data is at best a cost center.
The most valuable customer data is locked inside summarized reports, totally inaccessible to the sales, marketing, and service tools that drive your growth.
This creates an ROI gap. You have the data, but you can’t act on it.
To understand why this data is trapped, we need to look at how it got there. You’re likely familiar with ETL (Extract, Transform, Load).
For years, the entire goal of data engineering was to pull data from business tools (like Salesforce or your app) and load it into the warehouse for analysis. This process is critical for business intelligence, but it creates a one-way street. The data flows in, but it doesn’t flow out.
But consider what could happen if all of your data was available to other tools? How could you personalize messages if you knew which products your customers were most interested in? How could you help your sales teams close deals if they had visibility into which emails and content were most engaging for their prospects?
Reverse ETL (RETL) closes this gap. It flips the data-in model by extracting data FROM your warehouse and turning in into an active asset that enriches and supports all of your revenue-generating activities.
Use Cases RETL Supports
If traditional ETL is loading your warehouse, reverse ETL does exactly what its name implies: It moves data out of your warehouse and sends it to your marketing and other operational systems your business relies on.
This enables powerful use-cases, such as:
Building Highly Specific Audiences: You can query your warehouse to find very specific user groups. For example, you can identify “customers who purchased Product A but not Product B” and sync that list directly to your advertising platforms to run a targeted campaign.
Enrich Customer Profiles: You can send valuable data from your warehouse, like a customer’s LTV or their last support ticket rating, to your CRM or CDP. This gives your marketing and success teams a much richer, more holistic view of the customer.
Maintain Control of Sensitive Data: Operate within your own secure warehouse, giving you complete control over user identities. For regulated industries like healthcare needing HIPAA compliance, you can sync an audience to a tool like Braze without ever telling it why they are in that audience.
The Strategic Choice: A Question of Maturity and Speed to Value
Choosing a Reverse ETL tool isn’t just a technical decision; it’s a strategic one that depends on your data maturity – the amount of key data available and usable in your warehouse, and your team’s ability to model and prepare it for RETL.
Early/Developing Data Strategy: If your data strategy is still developing, your primary challenge is twofold: you need to ensure high-quality, real-time data collection and you need to activate it.
In this case, a Reverse ETL process only solves the second half of that problem. You still need to be able to collect rich data in an accessible format, so you can then join it with the data you already have, to make it actionable.
While you may have a limited data set that still has value, there is more to be done.
For High Maturity Teams: If you have a sophisticated data team and mature data models in the warehouse, you are likely well positioned for activating data immediately via RETL. All the work you’ve put into your warehouse and data engineering presents a massive accelerator into more advanced marketing and segmentation.
If you are also already implementing AI, predictive lead scores, or churn indicators, RETL is the mechanism that pushes these machine-learning-driven insights from the warehouse directly into your marketing and sales platforms, turning intelligence into action.
Customer Data Platforms: The Key to Activating Data
Often a Customer Data Platform (CDP) is the most robust way to bring your warehoused data into other systems via RETL. In the CDP world, this strategy of using the warehouse as the central hub for customer data is what the industry often calls a “Composable CDP.”
But we see this as simply a component of a composed marketing stack. With a strong data infrastructure, you have many options for how you integrate your data, and freedom to work within a variety of systems.
You can read our full perspective on this architectural trend here.
Among CDPs that enable data activation, Segment and Hightouch stand out as leading platforms.
The HighTouch CDP specializes in this sort of activation, leveraging your warehouse as the source of truth for all data. It is 100% focused on warehouses, providing a robust toolset. While it can handle some data ingestion, it is limited compared to a hybrid CDP like Twilio Segment.
Segment provides a bridge between data collection and warehouse activation. It gives similar warehouse-based data activation, connecting to multiple warehouses and providing multiple means of data activation.
Additionally, Segment can directly connect hundreds of data sources to your data warehouse or directly to other systems. This can be incredibly valuable for lower-data-maturity companies, quickly building out a robust and normalized data warehouse. It might also be useful for more data-mature companies to fill gaps in their data, or to support real-time use-cases.
Both HighTouch and Segment can also assist with the critical task of identity management, creating the “Rosetta Stone” to join your many data sets that have customer data. This often unlocks data sets that are otherwise impossible to connect. (Click here for our full perspective on Segment and Hightouch)
Alternative Approaches to RETL
While CDPs are the most robust systems for activating warehoused data for marketing, there are multiple other approaches:
Marketing automation platforms, like Braze and HubSpot, can directly connect to your warehoused data, importing contact and activity information. This is a great way to beef up your email and SMS platforms, but isn’t nearly as useful for other channels.
There are also dedicated tools for RETL and data management, some of which may already be in use by your data engineering teams. These tend to be less marketing/revenue focused, so integration can be challenging.
Expert Insight: Execution Determines ROI
With these many options, there are key decisions to make. Having a full understanding of your marketing stack and where there are robust integration points is a necessary first step.
Understanding the cost model of your RETL tools is one component. Some charge based on sources and destinations, others by unique audiences and segments, and many have a variety of different SKUs to enable specific features. Evaluate these against your business needs and validate you have enough volume for your initial use cases. Also, avoid over-buying for future expansion – you can always increase your contract when you’re ready.
Your data warehousing and processing costs are also critical, but difficult, to forecast and control. It is easy to have poor data management quickly balloon your cloud costs with compute, query, and storage costs. Since these systems charge based on a variety of different utilization metrics, this is often challenging to forecast. Because of this, you need tight data engineering practices to monitor and control utilization.
One client saved hundreds of thousands of dollars by adjusting the query timing and structure in their data warehouse. This required a refined understanding of their use-cases – specifically that real-time wasn’t vital for certain data sets – and generated significant savings without compromising ROI.
Getting Started: Define Your Strategy and Capabilities
Define Your Strategic Objective: First, clarify the business outcome you need. Are you trying to reduce data silos to improve the customer journey, or are you missing key data for segmentation and personalization? How quickly do you need to achieve ROI to justify your investment? Knowing the goal is the critical first step.
Visualize Your Current Architecture: Before you can optimize, you need a clear picture of what you have. Use our free StackBuilder tool to map your current data flows. Understand your current capabilities and gaps in your stack’s connection points.
Assess Your Data and Team Maturity: Be realistic about your current state. Do you have clean, actionable data ready for activation, or is there significant data engineering work required first? Does your team have the skills to manage this, or are they focused on other business goals? Your answers will determine the right path forward.
Your answers will shape your investment and approach.
Ultimately, Reverse ETL, or warehouse activation of any sort, is not a tool choice. It is a central capability that takes you from data-rich to revenue-generating.
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