Reverse ETL empowers data teams to easily prep bulk data, move it outside the cloud data warehouse, and operationalize it for their organization. Data is helpful, but its actual value is only realized when it is made actionable.
For years, organizations have used data transformation to clean and prep data for business intelligence and reporting. As organizations transition to a cloud-first approach for analytics, many have begun to explore their first data science projects to seek a competitive edge, stand out from the crowd, and find that unique, previously-not-exploitable business insight to find lost treasure.
Within current data practices, we are beginning to see the last mile of analytics as operational analytics, where ‘insight meets action.’
Getting Operational Insights
The ease of loading data from any source in a couple of clicks is commonplace now we are seeing business demand to solve the ‘actionable insights problem. The end goal for businesses is no longer just about getting the data into a BI dashboard, and now, it becomes more about developing insights that can be pushed back into the business applications from whence it came.
The analytics-ready data of the data platform, when synchronized back into the business layer, is quickly driving a revolution in the way we use ETL, moving from a tool mindset to a platform mindset, where a single investment in a data pipeline can deliver more than one outcome.
Extract Transform Load
Data processes such as ones that perform Extract Transform, Load (ETL) capabilities break down data silos and allow data scientists to more easily access and analyze data and ultimately turn that data into actionable business results. ETL has long been the necessary first step in the data warehousing process that lets you make real-time changes and decisions.
Extract Load Transform
The process Extract Load Transform (EL(T)) is the process a data pipeline uses to replicate data from a source system into a target system such as a cloud data warehouse or data lake to be transformed to create a view of a given data asset: be it customer, product, contract, employee, or patient.
But even with ELT, many companies have hit a barrier with their essential data being stuck in data warehouses, so they need another evolution of the process to break the cycle.
ETL or ELT process has advanced again to make data operational and achieve that greater business goal.
Reverse ELT
The term “reverse ETL” – operational data analytics is all about pushing curated datasets from the analytics environment back out to operating systems for real-time action or customer experience.
The operational analytics process allows decision-makers to achieve truly actionable data, interact with customers, and make changes that will instantaneously improve their business. When enriched analytics data is synchronized back to operational systems, the extract, load, and transformation processes are not just simply “reversed.”
By enabling the flow of data from the data platform to the people who need it, everyone in an organization will be empowered to make better business decisions at the moment.
For example, a restaurant chain can glean more information about a customer than ever before, including their habits and food preferences. This innovation could help them revamp customer service efforts and cater to customer desires, generating more repeat business and beating out their competitors.
How to get started with reverse ELT
Since implementing sync back is similar to implementing ETL or ELT, organizations don’t have to start from scratch; existing ETL tools already support reloading of data as well – making the transition that much easier. Many areas of the organization benefit from actionable data:
Sales: Moving customer data back to a CRM system can offer greater insight to amplify sales campaigns.
Marketing: Email campaigns should be automated to allow more personalized customer data access. That will enable businesses better access to subscriber preferences, make real-time alterations to campaigns and generate meaningful results.
Customer service: Pulling information out of a data warehouse to make it actionable can help the customer services team offer better customer service at any given moment and prioritize support tickets better.
Conclusion
When the world’s workforce is backed with current, updated data, they can deliver a more seamless and personalized customer experience. No matter what cloud data platform or vendor organizations use, they can all benefit from a bi-directional ETL approach to get data insights out of cloud data warehouses faster and back into the hands of business leaders and decision-makers.
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