In the 1980s, the concept of the data warehouse emerged, an IT architecture that enabled data in company operational systems to be moved into a different database that would enable better analysis of the data for decision-making purposes. The process of getting data from business systems into the data warehouse was termed ETL — extract, transform, load. Data extracted from operational data sources (such as account and billing systems), transformed to be in a form more suitable for analysis, and loaded into a data warehouse for analytical and business intelligence applications.
Despite decades of use, numerous data warehouse initiatives have not gained traction with staff. A major reason for this is that staff use one system for decision-making and analysis, and a different system for everyday operational work. In recent years, some organizations have started to include a decision-support capability in their operational systems, so-called 'operational analytics'. This involves taking data from a data warehouse and loading it into the business systems where it will be most useful for personnel. This process is now called Reverse ETL.
Reverse ETL is the process of copying data from a data warehouse into business applications like a CRM, marketing automation, or billing system that business teams use.
The purpose of Reverse ETL is to deliver insightful information, in near real-time, to the operational systems that people use for daily business activities. In the data warehouse, data can be analyzed for insights into customer behavior and to make data-informed decisions. On the other hand, operational analytics means providing data in operational systems that empowers staff when engaging with customers and improves the customer experience. An example is an ETL from multiple AMS systems into a data warehouse, and then a Reverse ETL into Salesforce to have a complete, up-to-date list of customer interactions across the business.
Loading data from a data warehouse back into production systems has some challenges.
There are Reverse ETL tools that enable the mapping and transforming of data to populate target fields by using a graphical interface. These tools also provide out-of-the-box connectors to numerous systems, and have tools for easily building and maintaining APIs. Data transfers can also be initiated by triggers or events. In addition, these tools can monitor the status of transfers and generate alerts if they need attention.
But companies that want to implement Reverse ETL applications find that it is another item in the software stack and they have to create a new separate team to use and maintain it. A more suitable solution would be a single application to manage the data pipeline for both ETL and Reverse ETL.
In addition, Synatic can orchestrate ETL and Reverse ETL processes depending on time-sensitive or event-driven triggers. This allows sequences of processes to be coordinated from one governing platform. An example is data extracted for the same client from a homeowner policy database and a vehicle policy database. The data can be extracted and consolidated, and then aggregated into the built-in data warehouse to gain a single view of the customer. When an insurance agent needs the complete customer view in their CRM system such as Salesforce, Synatic can trigger an activity to populate that data into the CRM where the agent can use the information.
With the Synatic DIH, teams can analyze consistent and reliable data using business intelligence tools with a data warehouse. What's more, the same data can be loaded into an operational system to assist with real-time decision-making to provide a better customer experience using the business systems that staff rely on. With its ETL and Reverse ETL capabilities, Synatic allows companies to get the right data, to the right person, at the right time, empowering them to use their data in a whole new way.
If you want to learn more about how you can leverage the power of Reverse ETL to power your various systems, contact Synatic today.
