What I like best about the ServiceNow Workflow Data Fabric is its ability to unify data from multiple systems into a single, real-time layer without heavy data movement. It enables smarter, context-aware workflows, reduces silos, and helps organizations make faster, data-driven decisions while maintaining strong governance and security. Review collected by and hosted on G2.com.

One drawback of ServiceNow Workflow Data Fabric is that it can be complex to implement and requires a good understanding of data architecture and integrations. Initial setup, mapping, and governance policies can take time, especially in large enterprises with many legacy systems. Additionally, performance may depend on external system responsiveness, and licensing or scaling costs can become high as usage grows. Review collected by and hosted on G2.com.

I like how ServiceNow Workflow Data Fabric removes the biggest bottleneck in automation by dealing with disconnected data effectively. I appreciate the unified data approach that doesn't require heavy integration work and offers real-time data access for workflows. I love how it shifts the mindset from 'integration-first' to 'workflow-first' and decouples data from systems. It significantly reduces long-term technical debt and enables true end-to-end automation. ServiceNow Workflow Data Fabric is a strong enabler for AI, which is often underrated, and it improves the experience for developers and admins. The best thing is how it delivers visible business value quickly. Review collected by and hosted on G2.com.

The initial setup was moderately complex and wasn't plug-and-play. We had to define data models, integrations, and governance upfront, which was a bit challenging. There's also complexity in initial setup that could benefit from more guided setup, templates, or low-code onboarding flows. Troubleshooting becomes tricky when data flows across multiple systems. I think there should be better fallback mechanisms or offline data handling, more intuitive governance dashboards and policy automation, and better visualization of data flows and business impact. Review collected by and hosted on G2.com.

I love that ServiceNow Workflow Data Fabric has made managing data across different sites so much easier by enabling us to pull real-time data directly into our workflows without needing to build complicated and fragile connections. I appreciate how it has cleared our technical backlog and significantly accelerated our AI tool rollouts. What I like most is the speed of deployment, as we no longer spend weeks or months building custom integrations for each project. It works especially well in feeding our AI and GenAI initiatives, turning AI insights into actual results and allowing our AI to initiate workflows automatically by being securely connected to our live data. We've also seen great results integrating it with Microsoft 365 and Teams to automate our communication loops. Review collected by and hosted on G2.com.

The results are great, but the setup and learning curve are quite steep. It’s not a 'plug-and-play' tool. You really need a dedicated team that understands both your old data systems and ServiceNow to get it working correctly. It takes a lot of careful planning at the start. Review collected by and hosted on G2.com.

I like that ServiceNow Workflow Data Fabric provides a unified and real-time view of data across multiple systems, making it much easier to access accurate and up-to-date information without switching between different tools. This improves visibility and allows for faster, more informed decision-making in day-to-day IT operations. I appreciate how it enables automation, with real-time data allowing workflows to trigger actions instantly such as incident creation, compliance checks, and remediation tasks. This reduces manual effort and speeds up issue resolution. Overall, it simplifies complex integrations, improves efficiency, and makes workflows more scalable and reliable. Review collected by and hosted on G2.com.

While ServiceNow Workflow Data Fabric is a powerful solution, there are a few areas that could be improved. One challenge is the initial setup and configuration. Integrating multiple data sources and defining relationships between them can be complex and requires a good understanding of the platform. Performance can also be a concern when dealing with large or highly dynamic datasets. In some cases, real-time data fetching may introduce slight delays, especially when multiple systems are involved. Troubleshooting issues can be time-consuming as well, since it may not always be clear whether a problem is coming from the source system, integration layer, or the platform itself. Additionally, the learning curve for advanced features can be steep, and more structured documentation or real-world examples would make adoption easier. Overall, improving usability, performance optimization, and troubleshooting visibility would make it even more effective. Review collected by and hosted on G2.com.

I really appreciate the ServiceNow Workflow Data Fabric's Zero-Copy technology, which allows me to virtualize data from platforms like Snowflake and Azure without moving massive datasets, maintaining data integrity and easing my workload. The Knowledge Graph feature is also valuable because it understands relationships between users, assets, and tasks, adding context to automation. When combined with RaptorDB Pro, the system feels cohesive and high-performance, making it possible to scale complex AI-driven projects without the constant 'integration tax' disrupting my workflow. I also like the ease of connecting with major data platforms using pre-built adapters, which speeds up the connection phase. Review collected by and hosted on G2.com.

The biggest hurdle for me has been the steep learning curve and setup complexity. Even though it's marketed as a low-code solution, you really need to have your organizational data architecture perfectly mapped out before you start. If your internal data governance is messy, the Data Fabric won't fix it—it might even make the gaps more obvious. It’s also not a budget-friendly option for smaller projects; the licensing costs for things like RaptorDB Pro and specific connectors can add up quickly, making it feel like a 'big enterprise only' luxury. Review collected by and hosted on G2.com.

I use ServiceNow Workflow Data Fabric for building automations, which truly replaces manual work. I really like its ease of use and how automated workflows are the most valuable aspect. They allow me to automate processes and run scripts, consolidating everything in one place and retiring redundant systems. Integrating with Active Directory to build groups in ServiceNow that are dynamically kept up to date is another benefit that deeply impacts my workflow. Overall, the automated workflows stand out as invaluable to my work. Review collected by and hosted on G2.com.

there may be a few bugs, failing to autosave for example Review collected by and hosted on G2.com.

What I like about ServiceNow Workflow Data Fabric is that it provides several advanced capabilities for bringing data from different systems—such as CRM, ERP, and databases—into a single place without physically moving or migrating that data. This supports a no-code approach, reduces overall complexity, and makes real-time access to information much more hassle-free. At the same time, the unified data remains readily available across multiple workflows and AI agents, which helps organizations automate processes more efficiently. Review collected by and hosted on G2.com.

What I dislike about ServiceNow Workflow Data Fabric is that, because it pulls data from multiple systems, it can sometimes feel slow or run into performance issues—especially when the source systems aren’t optimized. In the early stages, it also takes an experienced architect with a strong understanding of the overall systems landscape, data sources, and technical architecture to set it up properly. On top of that, it’s generally viewed as a premium, high-cost solution compared with other competitors in the market. Review collected by and hosted on G2.com.

In my opinion its particularly about its ability to seamlessly connect data from multiple enterprise systems without forcing data replication. The zero-copy approach gives me real-time, trustworthy data directly in workflows, which significantly improves decision-making speed and reduces integration complexity. It also adds governance and business context, making data immediately usable for automation and Al-driven workflows. Review collected by and hosted on G2.com.

its full value is not always easy to unlock without strong data maturity and proper configuration. Initial setup, governance alignment, and understanding how to model data correctly can be complex, especially for teams new to ServiceNow integrations. There is also a learning curve for non-technical users to fully understand how data is contextualized across systems. Review collected by and hosted on G2.com.

I like that once it's configured, the data just flows into workflows without needing a lot of extra logic. It makes building cross-team workflows easier because the data is already available in a more consistent way. Also, it fits nicely into the existing ServiceNow setup, so we didn’t have to change too much. It feels pretty powerful for automation use cases. Just the ease of configuring workflows once it's figured. Review collected by and hosted on G2.com.

Setup was a bit confusing at the beginning, especially around data mapping and figuring out the right sources. The documentation helps, but still had to try a few things before it worked. Troubleshooting is also not very clear; when something breaks, it's not always obvious where the issue is. Would also be nice if performance was more predictable when dealing with larger data sets. A few how-to guides would've helped, right now everything is to be pieced together by the user. It would help if there were more real-world examples in the docs, not just basic ones. Some kind of validation or preview before activating data mappings would also be useful; we ended up catching mistakes only after workflows started behaving oddly. Better visibility into data refresh timing would be nice too, sometimes it's not clear how fresh the data actually is. Review collected by and hosted on G2.com.

I appreciate ServiceNow Workflow Data Fabric's capability for real-time access to external data through its 'Zero Copy' architecture, which eliminates the risk and cost of data duplication. It's incredibly helpful because it provides a unified view of data from systems like Snowflake or SAP, avoiding duplication and stale information. The integration with RaptorDB Pro is another highlight, as it accelerates transaction processing by over 50%, making reconciliation and reporting much faster. I also like how it natively integrates with platforms like Snowflake, Databricks, and Google BigQuery, seamlessly accessing live data. It's notable how it connects to ERP systems like SAP and Oracle via Integration Hub, automating settlements and reconciling records efficiently. Review collected by and hosted on G2.com.

The setup for the Knowledge Graph remains highly complex, often requiring specialized architects to map disparate data sources before you can actually use it. Additionally, 'Zero Copy' queries can occasionally suffer from latency when pulling from massive external datasets compared to the speed of locally stored records. The initial setup for ServiceNow Workflow Data Fabric is generally considered complex and carries a steep learning curve, as it requires a high level of architectural planning to map disparate data sources effectively. While pre-built 'Zero Copy' connectors for platforms like Snowflake simplify the technical linking process, defining the Knowledge Graph to ensure data context remains a time-consuming task for most teams. It loses two points because the high license cost and the complex, architect-level setup make it a heavy lift for many teams to get off the ground. Review collected by and hosted on G2.com.