What is Data Silos?
Introduction to Data Silos
In the modern enterprise, technology is often purchased division by division.
- The Sales team buys Salesforce to track leads.
- The Marketing team buys HubSpot to track email campaigns.
- The Engineering team uses a PostgreSQL database to run the actual software application.
Each of these systems acts as a walled garden. The data inside Salesforce is isolated from the data inside PostgreSQL. This structural isolation is known as a Data Silo.
Data Silos are one of the most common obstacles to enterprise Business Intelligence. They prevent organizations from getting a complete view of their business, causing operational inefficiencies and gaps in decision-making.
The Cost of Data Silos
Data Silos cost a business in three main ways:
1. The Fragmented Customer Journey
If a customer submits a support ticket complaining about a bug, that data lives in Zendesk. If the Sales team doesn’t have access to Zendesk, they might call that same unhappy customer the next morning to upsell them on a premium software package. The customer may churn. Because the data was siloed, the right hand had no idea what the left hand was doing.
2. Competing Truths (Metrics Chaos)
When data is siloed, different departments build their own isolated reports. The Marketing team reports that the new campaign generated $100,000 in revenue based on their isolated Google Analytics data. The Finance team looks at the actual bank deposits in their Oracle system and reports the campaign only generated $60,000. Executive meetings turn into arguments over whose spreadsheet is correct, slowing decisions.
3. Engineering Debt
To solve silos, data engineers are forced to build brittle, point-to-point ETL pipelines. They write custom Python scripts to extract data from Salesforce and copy it into the Finance database. As the company grows, this creates a hard-to-manage “spaghetti” architecture of many fragile pipelines moving duplicate data across the network.
Breaking Down Silos: The Modern Architecture
Solving the Data Silo problem is a central goal of modern Data Architecture.
The Lakehouse Approach (Centralization)
A common strategy today is the Open Data Lakehouse. Instead of copying data between 15 different silos, the data engineering team sets up automated Change Data Capture (CDC) streams. Every time a row changes in Salesforce, HubSpot, or PostgreSQL, it is streamed with low latency into a centralized Amazon S3 bucket and stored as an Apache Iceberg table. The silos are consolidated. Enterprise data lives in one shared location, allowing analysts to join Sales data with Engineering data directly.
The Data Virtualization Approach (Federation)
For large enterprises where centralizing all data into S3 is not practical (due to regulatory compliance or legacy on-premises databases), the solution is Data Virtualization (using engines like Dremio). Virtualization leaves the data inside its original silo. Instead, it places a query engine over the top. When an analyst writes a query joining Salesforce and PostgreSQL, the engine translates the query, fetches the required data from both silos over the network, joins it in memory, and returns the unified result.
Conclusion
Data Silos are not just a technical problem; they are a symptom of fragmented corporate culture. Breaking them down requires a mandate from executive leadership to treat data not as the property of a specific department, but as a holistic, enterprise-wide asset. By using Lakehouse centralization or Virtualization technologies, organizations can break down these walls and enable the unified analytics that Artificial Intelligence and Business Intelligence depend on.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.