The ServiceNow Data Silo Problem 

Did you know that data silos aren’t just an inconvenience but a trillion-dollar problem? A recent IDC study estimates they cost the US economy alone $3.1 trillion annually. Another study by Experian found that 40% of business-critical data is locked in silos, preventing organizations from making data-driven decisions.

What does this mean for ServiceNow users? 

A ServiceNow data silo limits visibility across teams and systems. Take an IT Service Management (ITSM) team handling 50,000+ tickets annually. Each ticket holds insights into recurring issues, user pain points, and resolution times.

Business leaders and decision makers rely upon such insight to improve ITSM systems and processes, to understand how resolution times impact customer retention, to make informed decisions about resource allocation and more. 

When key data supporting such decisions exists within the ServiceNow silo, most business leaders and decision makers will lack the familiarity with the platform, or even access to retrieve it.

Those that are able to access such data lack the ability to combine it with data and insight from other sources in support of more comprehensive analysis. 

And it’s not just business leaders and decision makers. Modern organizations benefit when the workforce at large are empowered to retrieve the data and information they need to carry out their duties effectively. 

While ServiceNow offers some options to overcome its silo issue (like the Integration Hub), seamless large-scale data sharing remains a challenge.

This article explores the signs that indicate ServiceNow’s data silo is limiting your organization’s potential; the reasons why ServiceNow is prone to poor data availability issues; and what you can do to improve the availability of ServiceNow data around the enterprise. 

The ServiceNow Data Silo Problem: Common Symptoms 

When ServiceNow data is siloed, organizations experience the following common problems:

• Reporting and analytics suffer due to outdated or incomplete data, leading to poor decision-making.
• Making ServiceNow data available in external systems requires additional, often manual effort.
• API-based integrations that are not fit for purpose fall short of an organization’s required throughput for transferring data out of the platform, and/or degrade the platform’s performance. 

What’s Behind the ServiceNow Data Silo Problem?

Despite being one of the most powerful ITSM platforms, ServiceNow was designed primarily as a system of record—optimized for internal workflows rather than seamless enterprise-wide data exchange. Here’s why that creates challenges:

1. API-First Design: Sounds Flexible, But Comes with Limits

APIs provide controlled, structured access to ServiceNow data, but they aren’t inherently built for high-frequency, high-volume data movement.

Rate Limits & Throttling

ServiceNow enforces API rate limits to prevent excessive platform load. For example, many instances cap REST API requests at 100,000 per hour.

This is sufficient for routine data exchanges, but if your enterprise processes millions of transactions daily, you’ll quickly hit these limits, causing delays in data flow and operational bottlenecks.

While it’s possible to request higher limits or request custom rate limit configurations, they are there to prevent excessive platform degradation and many users will notice their instance slowing before the rate limit is even met.

Performance Bottlenecks

Every API call consumes system resources. If you’re syncing 500,000+ records in real-time, it can impact ServiceNow’s ticketing workflows, slow down table searches and reporting, and disrupt SLA tracking.

Performance related issues can escalate quickly when multiple, competing API calls, supporting multiple point to point integrations are involved. 

2. Transactional Architecture: Works for Workflows, Struggles with Real-Time Data Movement 

ServiceNow’s transactional database architecture is ideal for structured workflows but poses challenges for real-time data replication.

Batch-Oriented Exports

For example, let’s say your enterprise wants to push live IT incident data to a BI tool for real-time analytics. With ServiceNow’s batch-oriented exports, you’re typically looking at updates every 24 hours—or, if you push it, every few hours.

That might be fine for historical reporting, but if you need instant insights into security threats, asset performance, or service outages, those delays are a significant roadblock.

Challenges with Data Volume Scaling

ServiceNow’s default export methods (like CSV dumps or XML feeds) become impractical when handling millions of records. They may introduce latency, increase system strain, or require extensive processing and/or manual work.

3. Single-System Optimization: Great for ServiceNow, Not So Great for Data Sharing

ServiceNow is designed as a single-system platform, prioritizing stability, workflow execution, and in-platform data integrity. That’s great if your operations live entirely within ServiceNow, but when you need to share data with external systems, you start facing challenges.

Then, there’s integration complexity. Unlike platforms built for open data sharing, ServiceNow doesn’t provide effortless, plug-and-play integrations.

Instead, IT teams are often forced to rely on custom scripting, third-party connectors, and continuous maintenance just to keep data flowing between systems. This adds technical debt, increases costs, and makes scaling a real challenge.

How to Deal with The ServiceNow Data Silo Problem

ServiceNow users have two primary options to eliminate data silos and build a unified ecosystem:

1. Point-to-Point Integrations: The Traditional Approach

Point-to-point integrations, whether API-based or ETL-based, are among the most common ways to connect ServiceNow with external systems.

On paper, they seem simple—ServiceNow’s Integration Hub offers pre-built API connectors for dynamic data transfers, while ETL tools help move data in bulk. But in reality, each integration requires its own configuration, monitoring, and maintenance, making this approach increasingly difficult to scale.

APIs are best for real-time data exchange, while ETL works well for batch data movement. If you’re handling just a couple of integrations, either method can work. But when enterprises need to connect multiple systems and maintain high availability, point-to-point integrations introduce several challenges:

Scalability Challenges

• API-based: Every new system requires its own API connection. If you’re integrating ServiceNow with 10+ platforms, that means managing and troubleshooting 10+ separate API connections—each with unique authentication, rate limits, and failure points. The complexity grows exponentially. 
• ETL-based: ETL processes require scheduled batch jobs for each system. As the number of integrations grows, so do the processing time and infrastructure requirements. Running multiple large-scale ETL jobs can quickly strain backend resources and delay data availability.

Performance Strain

• API-based: Every API request consumes ServiceNow’s processing power. High-frequency API calls can slow down ticketing, workflows, and automation. In extreme cases, excessive API traffic leads to timeouts or system failures.
• ETL-based: Since ETL jobs transfer large volumes of data at scheduled intervals, they can overload ServiceNow’s database during execution. If too many ETL jobs run simultaneously, they can impact system performance, cause long query times, and even lock critical tables, leading to delays.

Ongoing Customization & Maintenance

• API-based: APIs require frequent updates to stay compatible with changing ServiceNow versions. Whenever ServiceNow or an external system updates its API, integrations must be modified to prevent failures. Over time, this leads to significant maintenance overhead.
• ETL-based: ETL scripts and transformation logic must be updated whenever data models change. If ServiceNow or a connected system modifies its schema, data mappings and transformation rules must be adjusted. This can create significant technical debt and require continuous developer effort.

Ultimately, while point-to-point integrations might seem easy at first, they become harder to manage at scale. Enterprises dealing with complex ServiceNow ecosystems often find themselves trapped in a cycle of troubleshooting and maintenance, leading to inefficiencies and growing costs.

2. Pub/Sub Model: A Scalable Enterprise Solution

Unlike API-based integrations, a Publish-Subscribe (Pub/Sub) model like Perspectium’s offers a more scalable approach, especially for enterprises dealing with millions of records daily. 

Instead of making direct API calls for each integration, Perspectium pushes ServiceNow data to a message broker that multiple external systems can subscribe to, to retrieve updates in real-time. This ensures smooth, continuous data flow without overloading ServiceNow’s infrastructure.

With this approach, enterprises can:

• Eliminate point to point bottlenecks and maintain ServiceNow’s performance.
• Handle 20M+ records per day, making it ideal for large-scale data replication.
• Ensure real-time data availability across analytics platforms, data lakes, and business applications.
• Reduce integration complexity, compared to managing multiple point to point connections.

Perspectium: The Ultimate Solution for Large-Scale Data Replication and Integration

Breaking down data silos isn’t just about integration. It’s about ensuring your organization has real-time, secure, scalable access to business-critical data.

With Perspectium’s Pub/Sub architecture, enterprises can replicate millions of records daily without straining ServiceNow, in support of a truly connected, data-driven organization.

As both a partner and service provider to ServiceNow (the ITSM organization is also a customer of Perspectium’s), Perspectium facilitates integrations that allow users to replicate large data volumes off-platform to external data repositories, improving data accessibility and availability.

Perspectium eliminates data silos by ensuring real-time access to critical information across teams. This enables seamless data streaming in support of advanced reporting, analytics, BI, AI, compliance and more.

By integrating ServiceNow with Perspectium, you can:

• Enable real-time insights:  Replicate ServiceNow data within your preferred reporting, analytics and business intelligence solutions.
• Support AI initiatives: Make ServiceNow available for training AI models, or utilise it within third-party solutions with their own built in AI capabilities. 
• Scale effortlessly: Replicate 20M+ records per day across multiple solutions, without straining ServiceNow’s performance.
• Ensure rock-solid security: As well as not relying on API – a common target for cyber criminals – to transfer data out of ServiceNow, data is encrypted at rest and in transit.
• Prevent data loss: Queuing data transfers within a message broker means that data is not lost in transit if there happens to be an outage to the source or target during the transfer of data.
• Eliminate integration complexity: Perspectium’s end-users do not have to maintain multiple point to point integrations, and Perspectium implements, maintains and provides on-going support for its solutions.
• Stay within ServiceNow’s ecosystem: Perspectium is natively installed within ServiceNow, meaning end-users manage everything from ServiceNow’s native interface with no additional learning curve.

Want to take control of your data and free it from its silo in ServiceNow? Contact us today.