The foundation for any customer data use case is a comprehensive identity graph. From analytics to personalized recommendations, a solid identity graph is essential for enabling customer journeys and everything in between. However, building the identity graph is just the first step in turning your customer data into a competitive advantage.
One key aspect of using an identity graph successfully is the features built on top of it. These features, also known as traits, provide the details that describe individual entities included in the identity graph, such as users, accounts, households, or physical devices. By combining the identity graph and its associated features, you can create a robust customer 360 that can power numerous use cases for your business.
While it is possible to manually write SQL queries to output features for entities, this approach quickly becomes unwieldy as the complexity and quantity of features grow. Handwriting SQL queries for features can create a liability that hinders your ability to serve your business effectively. To overcome this issue, you need a tool that simplifies feature creation and allows for easy project scalability.
RudderStack’s feature builder is introduced as a solution to this problem. By using feature builder, you can generate and maintain features across any data set in your own data warehouse. It simplifies the process and eliminates the need for business teams to create features in silos, thereby ensuring that the data team maintains ownership of data models and feature generation, with the data warehouse serving as the central source of truth.
Feature builder also facilitates the creation of multiple feature tables on top of the same identity graph, allowing for bespoke data sets based on the same source of truth. This approach enhances data trust within the company, enabling teams to deliver reliable features to stakeholders more quickly and efficiently. Additionally, feature builder provides predefined features out-of-the-box, making it easier for teams to get started without having to build everything from scratch.
In summary, RudderStack’s feature builder offers a streamlined approach to feature creation and management, maintaining features with confidence and enabling teams to support stakeholders with reliable data for every use case. It also enhances the agility of AI/ML initiatives and has the potential to power Predictions products to train and deploy ML models.
In conclusion, RudderStack’s feature builder offers a comprehensive solution for managing and scaling features, enabling data teams to efficiently generate features in their data warehouse and seamlessly cater to changing business requirements. With a focus on data trust and transparency, the feature builder allows businesses to unblock and unlock every use case while adding velocity and potency to AI/ML initiatives.