AI developers face many challenges that can be easily addressed by embedding a semantic layer in the data platform. For example, imagine a developer building a model to better predict customer churn. They reach out to the sales and finance departments to obtain datasets containing customer sales history, along with information on payment status, contracts, and other financial metrics.
The model heavily depends on the concept of an “active_customer” and finds this column present in both the sales and financial datasets. After a quick inspection, it becomes clear that the definition of “active_customer” in the sales dataset is inconsistent with the definition used in the financial dataset. Which one should be used for the model? Since assumptions can’t be made, it’s now up to the developer to uncover the business logic behind the “active_customer” column in both datasets to determine which definition is appropriate for the model. It turns out that the “active_customer” column in the sales dataset is more akin to “active_customer_or_prospect,” as it considers whether customers have active contracts and includes current prospects based on the sales phase. In contrast, the financial dataset strictly counts a customer as “active” only if there is an outstanding contract.
A semantic layer, which is a logical abstraction layer that sits between raw data and end-users of BI tools, translating complex technical data structures into meaningful business terms, can help in this instance. By defining terms like “active_customer” and maintaining a single, consistent definition in one place for all teams to use, it provides a unified, consistent view of data, simplifies data access, and ensures that everyone is on the same page when analyzing data. In the absence of a semantic layer, AI developers will struggle to establish a consistent system of business metric definitions.
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