The world of data is ever expanding. As more companies begin to use data to make decisions about their business, it is as important as ever to have your analytics in order.
In today’s economy, data influences nearly every business decision. To find the best possible target audience, or to find out what catalysts in your business are driving sales, you need data.
More than that, you need good data; information that is secure, insightful, and well organized.
You want figures that show you how your customer base behaves on a long-term basis, as well as a short-term basis. They should also show trends that you can use to make decisions about your company.
In addition, you want to make sure your data is displayed and categorized neatly. This will help you pull data faster when you need it. Nowadays, most companies use cloud-based warehouses to store their data from businesses like Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse.
However, there are some common issues that companies run into when trying to build and organize data. Most notably, many businesses struggle with making their data easily accessible for their workers.
This is where reverse ETL comes in. This process, which facilitates data integration methodology, allows you to make your data actionable through elite automation and organization.
Defining Reverse ETL
This process is quite complex. Even after that explanation, you may still be asking yourself, “what is reverse ETL?” Fortunately, we’re going to break it down for you.
Reverse ETL, which stems from traditional ETL, or “extract, transform, load,” helps operationalize data by pushing it back to third party systems like business applications. This lets your workers view this data straight from their workplace and automates it for action.
With traditional ETL, data must first be extracted from different sources, transformed and formatted for its target, and loaded into a storage area like a data warehouse. This process has been around since the 1970s and is commonly used to build a system of record for companies.
Both of these processes are used to move, automate, and store data. Yet, they accomplish these goals in opposite ways. As the name suggests, their processes are literally mirrors of each other.
Why Should You Move to Data Warehouses?
If your company already has its data stored in a warehouse, you may be wondering what the benefits are of moving it elsewhere. In theory, it may seem backward to move data out of a centralized location into a third party application.
Yet, without reverse ETL, your business’ core definitions only exist in your warehouse. They are essentially inaccessible to your workers; at least, not without considerable burden.
Your data warehouse works to reduce and eliminate data silos. Yet, by using reverse ETL, your data warehouse basically becomes a data silo itself. This significantly improves the efficiency of your data system.
Yes, many companies are using key definitions in SQL on top of their data warehouses; values like lifetime value, product qualified lead (PQL), marketing qualified lead (MQL), etc. They use these values to create reports and visuals in BI tools or SQl.
However, when you use reverse ETL, these insights are much more impactful, as they can be used in your everyday workings across every team in your business. From sales to marketing to finance, all your workers can benefit from this increased access.
Use Cases for Reverse ETL
Now that you understand the process, let’s talk about how reverse ETL is used in practical situations. There are an innumerable amount of applications for this method, but they can be categorized into three main groups: operational analytics, data automation, and data infrastructure.
Operational analytics deliver insights from analytics tools to business teams in their normal workflow. This allows them to make better decisions, based on hard data. With operational analytics, you don’t have to maneuver through multiple different platforms to view everything you need. It’ll all be in one, central location.
Data automation is used to make your data actionable. If you need specific data about a particular value or subject, you can use your operational system to pull that for you. Instead of having to manually analyze your data, your operational system will do that for you.
Data infrastructure lets you organize your data and display it cleanly. As source systems become more abundant, reverse ETL is growing as a general-purpose pattern in software engineering. Having strong data just isn’t enough; your data also needs to be organized well.