A common saying expresses how finite the average human’s time is on this earthly plane. It sometimes feels like it goes by in the blink of an eye. Well, during this modern era, the lifecycle of relevant, useful data goes by even quicker. It is a reality that even the most well-funded companies have issues maximizing the data they accumulate. However, fortunately, with modern problems arises modern solutions. Reverse ETL, ETL, and ELT are all modern methods of extracting value from data. Made possible by the new approaches that have emerged almost yearly from attempts to make the most of whatever a company’s data integration strategy happens to be. Presently there are more than a few ways to utilize data that has been collected:
- Data Ingestion – data is transported from various sources (databases, server logs, among others) into a storage medium.
- Data storage – a cloud-based solution that stores all the collected data sent from the data ingestion tool.
- Data transformation – Once raw data has been moved into storage, it will need to be transformed into user-friendly data models.
- Business intelligence/Data analytics – This is where data is analyzed, and dashboards are created for users to explore the data. Modern data analytical tools have also been designed for non-technical users, empowering domain experts to answer business questions without depending on developers and analysts.
- Data governance – Allows companies and organizations to keep track and make sense of their data which helps in data discoverability, quality, and sharing. Data governance also helps an organization stay legally compliant regarding data protection. Problems such as data breaches of sensitive data can be dealt with easily.
- Data orchestration – automating processes and building workflows within a modern data stack. Data orchestration allows data teams to define tasks and data flows with various dependencies.
- Data activation – democratizes data within the warehouse using reverse extract, transform, and load (ETL).
With reverse ETL, there is a slight change in how data is handled once it is within the company’s data warehouse. The data after going through the ETL or just as effective extract, load, and transform (ELT) is considered clean however can only be used for analysis and to gather limited insights. Rever ETL allows cleaned data from the warehouse to be copied to operational/business systems to power operations, forecasting, and other workflows. This expands access to business insights beyond the analytics team by removing silos. Providing context and enrichment to customers’ relationships with companies on platforms outside the regular data spaces. Reverse ELT not only helps companies to make decisions based on their data, but they also make those insights truly useful and actionable. Need proof? Hopefully, the following has enough to prove beyond a shadow of a doubt.
What Are The Ways Reverse ETL Improves Company Insights?
- Customer Service – Reverse ETL provides the high-quality data needed for companies to keep up with customer demands and quickly provide their best service. It helps the customer service teams log various customer interactions, which they could use to create relevant customer data they can then use on the spot.
- Detailed Logging – This helps to reference and understand which records failed to sync between the warehouse and the destination quickly. They also highlight successfully synced records to ensure accuracy and should boost confidence in the data the company is working with.
- Customer Expectations – It’s hard to create a personalized experience if companies aren’t working from solid, actionable data. Today’s customer has become accustomed to a greater level of personalization in their transactions than ever before. This is where Reverse ETL excels. It takes this data, enriches it, and provides insights directly to the teams that manage marketing, sales, and support which translates directly to improving customer experience.
- Usage Audit Logs – This allows teams to track reverse ETL usage from the user level. This helps companies understand exactly who made changes to any particular data model or sync configurations. It will also log any potential changes made by the company’s vendor support teams.
- Customer Experience – At every customer touchpoint, data can be collected. Reverse ETL makes accurate data accessible to every team within the company downstream, potentially improving the customer experience from the ordinary to the extraordinary. Remember, the whole point of data collection is to boost profits, and this can’t be done without customers who are content.
- Sync Alerting – As important as this is, it should never be limited to failures. Reverse ETL helps to initialize this particular action, including error messages, invalid or rejected records, and anything else that demands immediate action to fix the issue before it derails the whole operation. Before it begins to affect the customer.
- Better Marketing – A company needs to respond to new developments quickly with accurate information. A data-informed campaign can flex and provide the personalized experience today’s customers expect. Reverse ETL helps avoid missed opportunities by merging product, sales, and support data to drive customer segmentation.
