1. Choosing the Wrong DAM Product for Your Integration Needs
A seemingly obvious yet critical mistake is to select a DAM system that does not address your organization’s specific integration needs. I think it’s safe to say that most DAM products look very similar when you browse the vendor’s website. The reality is that DAMs vary widely in flexibility, data model configurability, scalability, and extensibility. Not all DAM systems offer the integration capabilities or modern APIs needed to seamlessly – and efficiently – fit into your MarTech ecosystem.
How Does This Happen?
Organizations often rush the requirement-gathering phase or focus on generic DAM features rather than precise integration needs during the RFP process. Compounding this issue, vendors may advertise “integrations” that are superficial or built on a fundamentally different understanding of what connecting a DAM to other platforms entails.
The Consequences
When a DAM lacks robust APIs or out-of-the-box (OOTB) integrations, teams often compensate with costly customizations. This leads to inflated budgets, reduced adaptability, and long-term maintenance burdens.
Mitigation Strategy
- Gather real requirements: Engage stakeholders across departments (e.g., product, content, and brand teams) early to define integration needs. Avoid limiting input to the one team that provides budget for the DAM purchase.
- Request a proof of concept (POC): During vendor evaluations, ask for live demonstrations of how their DAM integrates with your existing platforms. Find out whether their definition of “integration” aligns with your expectations.
- Evaluate APIs and connectors: Prioritize DAMs with well-documented modern REST/GraphQL APIs or pre-built connectors tailored to your specific integration need ( and make sure the plugin/extension actually addresses you need).
2. Over-Reliance on Custom Code for Integrations
When a DAM lacks native integration capabilities, organizations often turn to contractors or internal teams to build custom code. While this approach may solve immediate gaps, it introduces long-term risks, if not done right.
The Risks of Customizations
Custom code often locks organizations into rigid workflows, making future changes cumbersome. These integrations are frequently treated as one-off projects, leaving them undocumented, unmaintained, and outdated over time. Additionally, hidden costs emerge: internal teams drain resources through diverted focus, while external contractors demand clear accountability to avoid budget overruns.
Mitigation Strategy
- Stick to standards: Prioritize standard functionalities over custom code. Assess whether adjusting business processes slightly could eliminate the need for complex technical workarounds.
- Weigh “buy vs. build”: If custom development is unavoidable, decide whether to use internal teams or external integrators. While external providers often bring expertise, limit their numbers to foster knowledge sharing and long-term partnerships.
- Plan for long-term maintenance: Treat customizations as ongoing responsibilities. Ensure contractors document code thoroughly and include maintenance clauses in Statements of Work (SOW).
3. Making Poor Decisions About Integration Architecture
The architecture of your DAM integrations directly impacts scalability and operational efficiency. Many organizations default to outdated methods that struggle to meet modern demands.
Common Integration Approaches
File-based exports (CSV/XML/JSON).The DAM exports data periodically to a storage service where it is picked up by consuming systems. While functional, this approach is static and prone to delays. API-based one-to-one integrations on the other hand dynamically fetch data from the DAM but create “spaghetti architecture” as connections multiply. Modern event-driven architectures, however, use tools like Apache Kafka or Azure Service Bus to publish DAM events (e.g., metadata updates) to a centralized bus, allowing multiple systems to consume and process data in real time.
Why Event-Driven Architecture Is Superior
This approach eliminates point-to-point connections, simplifies scalability, and enables real-time updates. Producers (e.g., the DAM) need only one publishing mechanism, while consumers subscribe to relevant events without requiring changes to the core system.
Mitigation Strategy
- Use API-based integrations when possible, but avoid “spaghetti” connections by connecting a large number of systems to each other.
- Adopt event-driven architecture: Where feasible, implement this model to future-proof integrations, reduce latency, and streamline data synchronization across platforms.
- Whatever you do, think about the future impact of your connections. Avoid the risk of getting stuck in a dead end.
Conclusion
Integrating a DAM into your MarTech stack is about making strategic decisions at every stage. By avoiding these three pitfalls—choosing the wrong product, over-relying on custom code, and using the wrong architecture for your customizations—you can save frustrations, time and money. Success hinges on gathering precise requirements, rigorously evaluating integration capabilities during vendor selection, and embracing modern architectures.
