By Brian Laleye · March 22, 2022 · 16 min read
Today, you will learn all about Reverse ETL and how it gives your business a real-time competitive edge.
To deliver a great customer experience, you need excellent and real-time data. Your teams use a variety of tools to get information about prospects, accounts, and customers: spreadsheets, CRM systems, form submissions, emails, surveys, and more. Unfortunately, each of these tools stores data in a different format, which means that you’re forced to hire expensive data specialists to keep your data from growing stale. The alternative is Reverse ETL (i.e., Reverse Extract-Transform-Load).
In this guide we explain what Reverse ETL is, the difference with ETL, what are the use cases for Reverse ETL and how it can benefit to each department?
Let’s get started.
Data warehouse (DWH) systems are a common practice in many organizations nowadays. They play a critical role in business intelligence (BI) and internal data analysis. However, they also create a significant workload for the IT department in terms of development, support, maintenance, and updates.
The DWH often requires refreshing – i.e., updating the data regularly. In addition to loading the data into the DWH, this process often involves updating and maintaining the source-side systems to suit their target DWH representation. A large amount of work is required to maintain these systems along with their associated mappings and transformation logic.
This is where Reverse ETL can help!
The idea behind this process is to create a single, comprehensive data source that provides a single, trusted view of enterprise data. Reverse ETL processes are generally used to augment existing ETL processes, and they run on defined time intervals.
For the data warehouse, ETL is used to copy data from the OLTP (Online Transactional Processing) database, transform the data to meet the data warehouse schema and requirements. It may also involve aggregating data or combining it with reference data.
For example, a retail business may have a transaction database that stores sales transactions. Each transaction has a product ID that relates to a product table in the database that stores details about each product.
There are also dimension tables in the retail database such as customer and store. When loading data into the data warehouse, ETL would copy sales transactions from the transaction tables but transform them from their detailed form into fact tables with individual records for each sale and store attributes such as customer ID, store ID and product ID in separate columns.
ETL processes can be complex and time-consuming. They typically run on a schedule such as once per day or week rather than real-time. In some cases, organizations need to get more current information into their data warehouse.
For example, they might want to know how many customers are currently shopping in their stores at any given time. To address this need for real-time or near-real-time reporting, Reverse ETL tools is the solution.
Reverse ETL is essentially the same as ETL except it moves data in the opposite direction. Data is extracted from a source database, transformed according to business rules, and loaded into another database or business application.
The idea behind doing it in reverse is that you can consolidate all of your company’s information in one central location, allowing for more efficient analysis and decision-making down the road. By centralizing your data you are also creating a single source of truth for your organization.
The reverse ETL toolkit is a key driver of a business’s ability to scale its data warehouse, as it allows for faster data ingestion and transformations, more reliable deployments and rollbacks, the ability to leverage the latest open source tools and a centralized location for business logic.
In addition to these benefits, there are many other reasons why organizations find it advantageous to perform Reverse ETL.
We’ve compiled a list of the reasons below as well as some questions that will help you determine if your organization could benefit from Reverse ETL:
Reverse ETL is an essential part of the DataOps process thanks to Operational Analytics. It enables a single source of truth by moving insights from analytics tools back to operational systems, so that business teams can act on them in their usual workflow.
Why would you want to do this? Let’s consider an example. Say your company has an established reputation for great customer service: You answer phone calls quickly and resolve problems efficiently.
One day your CEO asks if you can find more efficient ways to handle customer inquiries — but how can you tell whether current call times are good or bad? You could look at historical records that show average call times from years ago, but that doesn’t help much because so much has changed since then: Customer behavior has changed; new technologies have emerged; new services have been added. How can I answer my CEO’s question?
Operational analytics is the solution because of its focus on analyzing data in real-time, or near real-time.
Tech companies like Uber have already explained how they use analytics to create optimals trip experiences, such as determining the most convenient pick-up points in order to predict the fastest routes for riders.
Today, Operational Analytics is not limited to tech-savvy companies like Amazon, Uber, Apple. Any company using data can make more data-driven decisions thanks to real-time data. For instance, let’s take any reseller who wants to improve its margin. The data gathered at one of his points of sale can be used to manage inventory and ship more units to local stores that are running promotions.
In the real world, not all data problems are so glamorous. “Can I get a CSV to issue some invoices?”, your finance team asks. “My contract expires in a month and I need to see how much that customer is paying us per month!”, your sales manager chimes in.
Reverse ETL poses a simple solution. While we use ETL to move data from multiple sources into one central place — data warehouses or data lakes — Reverse ETL moves data out of these central places back into single-purpose systems.
You don’t need any help from your data engineer. The required data is already in the data warehouse, and with Reverse ETL, you can extract data from the warehouse and sync it to external tools thanks to a Drag & Drop SQL Editor, making it the most straightforward approach.
Reverse ETL is a core piece of data infrastructure. While it’s not the only way to build data infrastructure, for many teams it’s the best way. If you’ve spent any time building ETL pipelines, you know that they can be a pain. They’re often brittle, difficult to debug, and slow to build. And there’s nothing worse than realizing months into development that your data warehouse wasn’t set up correctly.
The emergence of Reverse ETL as a general-purpose pattern in software engineering has happened because today’s digital organizations are generating more and more data from more and more sources.
Reverse ETL makes it easier to create a separate database that contains all the data you need, in one place, with no gaps or errors. It also helps ensure you have a single source of truth — even if your team changes its mind about how data should be stored and accessed.
Reverse ETL is useful for many different business applications. Retailers use it for accurate inventory data and for sales forecasts, among other things. Manufacturers use it to calculate the true cost of their products — a key performance indicator (KPI) that is essential to measuring the health of their business. Any company will be able to build dedicated views for business teams on their metrics: MRR, CAC, LTV, customer health, revenue and any other KPIs. Combine all relevant data sources to ensure accuracy and consistency across all your dashboards and reporting.
Some common examples include:
Reverse ETL is also used in Customer Success processes like determining churn and winback rates. Since Reverse ETL automatically updates customer data across systems, it’s easy to see when a customer has canceled his service or changed plans. Besides, you’ll be able to sync any business indicators from your data sources (Database, data warehouse, CRMs..) into Google Sheets and Airtable.
How Reverse ETL helps on the process of identifying high-likely churners:
To sum up, Reverse ETL enables your Customer Success team to get a 360° customer view right in your day-to-day business apps.
Reverse ETL is a process that is used in sales to import leads from third party tools into your CRM. A major benefit of Reverse ETL is that it allows your CRM to have a 360 degree view of your customer and all the data associated with them by combining CRM data with other applications like marketing automation software and accounting systems.
CRM dashboards are extremely useful for sales managers, who need to track the performance of their sales team and identify opportunities for more growth. Reverse ETL can improve the accuracy of those dashboards by automatically updating the information they contain by using data sourced from marketing automation tools, such as Hubspot or MailChimp.
Moreover, Reverse ETL allows to maximize leads conversions and revenue with your own scoring rules thanks to three factors:
As you look at your current digital advertising infrastructure, you may find that you have multiple tools across your organization that are not integrated together. As a result, you may have gaps in performance tracking and attribution due to misaligned data points in the marketing funnel.
Here is an example of a common scenario:
With Reverse ETL, you will access a full picture of any of your customers in your marketing applications without the burden of managing a new ‘source of truth’.
The marketing teams will often use Reverse ETL:
Data teams use Reverse ETL to move data from a source database into a target database.
Here are some common use cases:
In addition to these data features, one of the main advantages of Reverse ETL for data teams is that it facilitates collaboration with business teams.
Without code, data teams will deliver cooperatively value by modeling data instead of focusing on building, coding and maintaining integrations. Data and engineering teams can serve any stakeholder with operational analytics in minutes, not weeks.
Last point, Data teams can manage all the domains of their organization in one place that encompass:
Reverse ETL needs to be flexible enough to handle any volume, at any time. Adapting new technologies in real-time means speed and agility are vital for success. Since every organization works a little differently, each one has different needs with regard to what data is of importance and when it needs to be available.
As a result, you’ll need a partner who can listen closely, ask intelligent questions and guide you through your unique journey. And that’s exactly what we do here at RestApp; we are experienced with multiple industries ranging from retail to real estate and have helped our clients make sense of their datasets.
The main point that you need to consider is the use case for Reverse ETL. You need to clearly understand why you are doing it and what is the expected outcome. Are you looking to migrate off an existing system? Are you looking to build a strategic 360-degree customer view? Or simply looking to consolidate data from multiple sources into a single data warehouse? It is critical to have a well defined use case before starting out on Reverse ETL. It will help you define your objectives and measure them.
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