Having just reviewed the 2024 Forrester Wave™ for Customer Identity and Access Management, I find their evaluation framework continues to exhibit a critical blind spot, particularly in the "Operability" category. While the report dutifully lists deployment options, monitoring, and reporting capabilities, its weighting fundamentally undervalues the raw, day-to-day engineering friction—or lack thereof—that defines a platform's true operational cost. This friction isn't captured by feature checkboxes; it's found in the granular details of lifecycle management, debugging, and system introspection.
For instance, consider the operational burden of a schema migration in PingDataGovernance versus a comparable policy update in a more modern, document-oriented approach. The former often involves precise, sequential SQL scripts with significant rollback complexity, while the latter might be a targeted API call or a declarative config push. Forrester's criteria might acknowledge both systems can achieve the outcome, but the operational cost differential is immense at scale.
A more meaningful operability assessment would require a benchmarking approach, measuring:
* **Time to diagnose a failed authentication flow:** This involves tracing a request across services, correlating logs, and inspecting state. Platforms with integrated, structured logging (e.g., OpenTelemetry ingestion directly into their observability stack) versus those relying on grepping disparate text files represent a chasm in operational maturity.
* **Cognitive load for a routine configuration change:** The number of distinct UI panels, API endpoints, and implicit dependencies that must be understood to safely deploy a new OIDC client with specific claim mappings is a quantifiable metric.
* **Infrastructure as Code (IaC) parity:** Evaluating whether every configurable element is available via a first-party Terraform provider or a truly idempotent API, not just a partial subset. A 20% IaC coverage creates more operational debt than having none at all, as it creates two parallel management planes.
The report's analysis of runtime performance is, frankly, superficial. Stating a product "supports high availability" says nothing about the operational complexity of a regional failover. A comparative analysis of the failure recovery time objective (RTO) and the required manual intervention steps would be far more revealing. In my own stress testing of similar systems, I've observed recovery procedures that require 7-8 manual steps under pressure versus fully automated failovers that complete within 90 seconds. This disparity is operational reality, not a feature bullet point.
Ultimately, the current methodology favors vendors with comprehensive feature lists over those investing in radical simplification of the operational model. This biases the market toward perceived "completeness" at the expense of long-term total cost of ownership. The community's shared experiences—particularly around upgrade procedures, hotfix application, and performance regression debugging—are a richer source of operability truth than any analyst's weighted scorecard. I am interested in others' direct experiences measuring or benchmarking these hidden operational costs within PingIdentity's ecosystem or compared to alternatives.
I couldn't agree more. This disconnect is prevalent in most vendor evaluations, and it's rooted in a methodological flaw. Analysts rely on vendor-provided demos and documentation, which showcase capability but not cost. They rarely, if ever, engage in longitudinal operational testing or gather standardized performance metrics for tasks like schema migration or cluster scaling.
Your example of schema migration versus policy update is perfect. In database benchmarking, we see this constantly. A "feature" like online schema change is listed, but the benchmarks don't measure the latency penalty during the operation, the additional I/O load generated, or the recovery time objective if it fails. The operational cost is hidden.
To your point about benchmarking, I'd add that any real operability metric must be *statistical*. You can't just ask "Can you do it?". You need to measure the 99th percentile time for a junior engineer to diagnose a failed auth request across a 100-node cluster, or the variance in policy propagation latency. Without this quantitative lens, operability remains a subjective checkbox.
Test it yourself.