Reverse ETL

ELT is the process in which data is consolidated by extracting data from several sources, transforming it, and then loading the data into data warehouses so it can be used for analytics. This process eventually moved on to a cloud data platform. Examples of such platforms are Snowflake, Google BigQuery, Amazon Redshift and others. The data that is gathered could then be used to run analytics and build out reports to help businesses make more informed decisions for their business.

It is absolutely possible to code your own data pipelines, but it is a complicated and time-consuming process that can take days or even a full week. Not to mention all the tools needed to make it happen. Most businesses can’t afford to wait that long to get their needed reports and analytics.

ETL Moves to ELT

The move from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) has taken the process to a new level making it much faster, more simplistic, and even more scalable. Cloud-native ELT is a more efficient and effective way to consolidate data. This is done by extracting it from source systems and then loading it into the cloud platforms so the data can then be transformed there using the power of those cloud data warehouses.

Since the transformation in ELT happens within the cloud data warehouse, the power and performance of the cloud can be taken advantage of by the user. This requires a cloud-native ELT solution.

And finally we have… Reverse ETL. What exactly is it?

Reverse ETL is a different kind of data pipeline that can get the valuable data which is stored in the cloud data system in the hands of more people within an organization. Data infrastructure and how it has been handled has mostly been the same since the 1970’s. Extract, transform, load (ETL). Data is extracted from a system that has recorded it, it is cleaned up and then transformed into a storage structure before being loaded into a warehouse to be stored.

Reverse ETL does the exact opposite of ETL. This allows companies to sync customer data from the database (warehouse) to a third-party application. This is where apps like Hubspot, Zendesk, Marketo, Salesforce, and many others come into play. Pre-built API integrations are included in a reverse ETL, allowing data teams to be able to do their jobs without the added challenge of building and maintaining them.

Reverse ETL is that link that was missing in a modern data stack which closes the customer data loop.

Reverse ETL in its basest form is an extension of ETL. It helps bridge the gap in getting data from a data warehouse into the hands of the people with an organization who can use it to help a business learn and grow.

Implementation

Doing it yourself or going with a commercial solution. Those are the options. Building it out yourself, DIY style, can be a good option, but may come with a lot of challenges and likely will take more time than you initially anticipate. A commercial solution will be a much easier and much quicker implementation process.

Implementing Reverse ETL is much like implementing ETL. Quite a few of the tools available that support ETL also support Reverse ETL. Beside supporting operational analytics, Reverse ETL can do quite a bit more.

Operational Analytics Enabled

Operational Analytics does one really simple thing that helps an organization in a really big way and that’s by taking a company’s data and putting it to work so the company can make better decisions moving forward. It helps improve a company’s efficiency and effectiveness.

Operational Analytics are enabled when a business is connected to its data warehouse through a modern data stack and reverse ETL. Operational analytics uses data to help build the day-to-day strategies for operating a business rather than only identifying long term trends and strategies. Let’s take a look at a few examples.

• At risk customers can be identified by searching customer usage data. Reports can be run in advance to notice when customers are at risk to churn so that immediate actions can be taken via special offers or other strategies to prevent the churn from happening. Data can also be used to help decide what kind of contact or offer a customer is more likely to respond positively to.
• Drive sales, create special offers, or utilize freemium models in order to draw in your ideal customer all using the data and reports generated through reverse ETL. Some customers love a great BOGO campaign, others love to shop when they know they can get free shipping or an exclusive gift. More detailed customer reporting comes in extremely handy in a lot of different ways.
• Build better marketing campaigns. This can be super personalized to target a specific audience. It can also help your campaigns to be intuitive enough to not bombard the same people over and over again.
• Better and more detailed customer details can help with creating more targeting email marketing, newsletter marketing, and even live chat marketing and customer service. Customers are more likely to click emails if they are super personalized and targeted to them.
• Customer support can provide better service when they have more detailed info on the customers that they are dealing with. Sending a peanut butter cookie pack as an apology is probably not a good idea if a customer has a peanut allergy. This can be avoided with in depth analytics at a customer service representative’s fingertips.

Data Teams Empowered

Marketing and customer service teams may benefit when companies use reverse ETL for their operational analytics, but they aren’t the only ones.

Data Teams who normally are just in charge of crunching huge stacks of data to get any and all insights they can in order to build endless reports to answer questions about what is going to happen to the business, can actually (finally) participate in creating a strategy for success instead.

In the past, data has only really been used to report what has already happened so that steps can be taken later to fix what didn’t work out or to continue utilizing whatever strategies did. The problem with this, however, is that by the time that data has been “reported” likely the stats and figures have changed again. Businesses and workers on the frontline remain several steps behind because of the lag in retrieving the data and the actual reporting of it.

Now that reverse ETL is being used, analytics do not only have to be used at what happened but can become a hub to allow businesses and teams to see what is happening in a more “real time” atmosphere. This allows companies to move quickly in their marketing, to identify and take advantage of trends, and so much more. With more detailed data available in a much more efficient way, data teams can spend their time building workflows that lead to quick actions. The business can be a data-driven company.

Conclusion

The fact of the matter is that moving data between data storage and applications can be a hassle for a lot of people, including app builders, IT guys, and engineers. But it is also a fact that we know businesses are at a huge advantage if they can get access to great data. ELT and ETL have been around for years and were begun out of necessity and have even evolved a bit, Event streaming and custom-built integrations are also available.

But business requirements for these data pipeline tools are more advanced and more demanding than ever. Until finally a new solution has emerged, and that is: Reverse ETL. A super simple solution to move data from a data warehouse directly to the cloud applications a company uses regularly. It really is that easy.