The world of modern business relies heavily on data. Data is something that is created with every interaction in the digital age, and it’s important for businesses to be able to not only aggregate but integrate this data into a location like a data warehouse. The purpose of this data is to create systems and tools that various departments can use to make meaningful, data-driven solutions. The end result of data-driven solutions is to better understand the customer and improve their experience.
Improving customer experience is one of the most valuable things that modern businesses can do. Not only does this secure existing customer and further loyalty, but it also helps marketing teams understand how to bring in potential customers. One of the ways that this happens is through a company’s ability to analyze and use data wisely and effectively.
One important aspect of this process that can bring a lot of benefits to a company, is data enrichment. If you have been curious as to what data enrichment is, and what steps you need to take to improve your data in this way, here is everything you need to know.
1. What Exactly is Data Enrichment
The first step to data enrichment is to learn what it is and why it’s important. When it comes to some of the many problems that face the modern digital age, data is created from various disparate sources is one of them. The more data that is created, the more this volume grows, and the more difficult the job of retrieving the data and making it accessible.
This is where the work of the modern data stack comes into play. The modern data stack gives companies a chance to capture data from disparate sources and integrate it into one central location. The cloud-based data warehouse is at the heart of what makes the modern data stack and is a very important component for companies to utilize their data.
The main tool used for decades when it comes to the integration of data into the data stack is ETL. ETL stands for Extract, Transform, and Load. This process can take data that is created from a variety of disparate sources and bring them into one centralized data warehouse. This is also one of the easiest ways to understand what data enrichment is.
When various SaaS tools create data that is pulled into a data warehouse, or CRM, it can create views of customers that are one-dimensional. For example, a CRM like Salesforce can be a source of truth for a marketing company however it only shows one view of the customer. Unique event data for the exact same customer might be kept in the data warehouse. If a data team was to take data from the warehouse, such as Redshift, and sync it with the CRM (Snowflake) this could create a more robust view of the customer.
Streamlines Customer View
Because data enrichment works at taking valuable data from various sources and creating one profile view of the customer, it can streamline what could otherwise be a tedious task. Data enrichment takes existing data and appends it with other existing data in one centralized location so that teams can have the best possible information.
2. Pick Your Data Enrichment Tool
When it comes to picking the tool for data enrichment, you have a few options to choose from. The most popular data enrichment tools are:
- CDPs
- iPaaS
- Reverse ETL
CPDs or Customer Data Platforms, allow for data aggregation which is slightly different from integration or enrichment. However, CDPs allow for data to be pushed into various tools thanks to the fact that they automatically integrate with other APIs. Because of their native 3rd-party API integration, pushing data out to tools where it can be naturally enriched is one of CDPs advantages.
iPaaS, or Integration Platform as a Service, as the name implies focuses on integrating data from point to point. This is great for automating the integration of data from one system to the other, however, it suffers when it comes to showing a full 360 view of the customer.
Reverse ETL is a powerful data enrichment tool as it natively integrates with your data warehouse as the name would imply. This tool takes advantage of cloud-based data warehouses and allows for data to be pushed out to end sources. This not only moves data out of the warehouse to departments that need it, but it enriches the data as it does.
Data that returns to the warehouse from its end sources has been enriched and is even more useful for analytics.
Conclusion
Finding ways to actively enrich your data is something that the modern data stack will continue to evolve in. With CDPs, iPaas, and Reverse ETL, companies can start to focus on data enrichment that can meaningfully impact their view of the customer and increase their insight.