The Enemy of Holistic Insights

A company’s marketing team uses Marketo for campaigns and stores campaign performance data there. The sales team uses Salesforce to track leads and opportunities. The support team uses Zendesk for customer tickets. The product team uses Mixpanel for user behavior analytics. Each of these systems contains valuable data, but the data is isolated, structurally incompatible, and accessible only to the team that owns the specific application.

These isolated pockets of data are called data silos. A data silo is a repository of fixed data that remains under the control of one department and is isolated from the rest of the organization.

Data silos prevent organizations from answering holistic business questions. To answer “Do customers who submit support tickets have a higher churn rate?”, an analyst must extract data from Zendesk (support tickets), extract data from the billing system (churn status), manually match the users across the two datasets (often using Excel or ad-hoc scripts), and compute the result. This process is slow, error-prone, and cannot be automated easily for continuous monitoring.

Why Data Silos Form

Data silos rarely form maliciously; they are the natural byproduct of organizational growth and SaaS adoption. Departments purchase specific SaaS applications to solve their immediate operational needs. These applications are designed to optimize their specific workflows, not to share data seamlessly with the rest of the enterprise. Furthermore, departmental structures, budget isolation, and differing technical capabilities naturally lead to teams managing their data independently.

Over time, this results in conflicting truths across the organization: the marketing silo reports 500 new leads, the sales silo reports 300 new opportunities, and the finance silo reports 100 new paying customers, with no clear linkage between the numbers.

Breaking Down Silos with the Lakehouse

The historical solution to data silos was the enterprise data warehouse: extract all data from all silos and load it into a central relational database. This often failed due to the rigid schema requirements of warehouses and the massive effort required to model every department’s data into a single, unified enterprise schema.

The data lakehouse provides a more agile approach to breaking down data silos:

1. Centralized Storage, Flexible Schema: Data from all silos (SaaS APIs, relational databases, flat files) is ingested in its raw form into the Bronze layer of an Iceberg lakehouse on cheap object storage. The lakehouse accepts unstructured and semi-structured data immediately, eliminating the bottleneck of upfront schema design.

2. Incremental Conformation: Instead of a massive enterprise modeling effort, data engineers incrementally clean and join the data in the Silver layer as business use cases demand. Zendesk tickets and Salesforce accounts are joined using dbt models to answer specific analytical questions, creating connected data products without waiting for a complete enterprise model.

3. Federated Access: For operational databases that cannot be easily ingested, query engines like Dremio provide data federation, allowing analysts to query across the lakehouse and operational databases simultaneously, bridging physical silos through logical integration.

Learn More

To dive deeper into these architectures and master the modern data ecosystem, check out the comprehensive books by Alex Merced available in our Books section.