Once data is normalized and reconciled, FogLifter® surfaces cost drivers, ownership patterns, service relationships, spend anomalies, modernization signals, and risk context in a way teams can actually use.
Strong analytics depend on strong relationships in the underlying data. Here's what FogLifter's analytics surface when the records are validated and correlated.
Most platforms show cost of assets — the per-unit price of a server or gigabyte of storage. FogLifter surfaces cost of service: the underutilization (customers routinely discover they're running 25–30% cloud utilization), the labor required to maintain optimal performance, KPI discrepancies, and the financial impact of delays. One customer engagement identified $3.5M in annual cost avoidance by connecting spend to service context for the first time.
Analytics that connect asset records to application ownership, compliance posture, and lifecycle status — not just whether an asset exists, but what it supports and what happens if it fails. One customer discovered 8 petabytes of forgotten storage: an MRI application configured 10 years earlier to write six copies of every scan. No single tool had connected the storage allocation to the application config to the cost — FogLifter's cross-domain correlation surfaced it.
Shared assets — where multiple business units consume the same application, storage, or data — are among the hardest allocation problems. FogLifter correlates across dimensions that wouldn't be immediately obvious: network endpoints, server access patterns, software license utilization and geographic access points. A healthcare customer with 52,000 production servers moved from peanut-butter-spread cost allocation to evidence-based consumption attribution.
FogLifter tracks how records change across ingestion cycles — measuring deltas in compute, storage, and service footprint month over month. One customer was routinely disputing 3,000 to 5,000 servers every billing cycle — the same arguments recurring each month. Once FogLifter tracked trends over time , both parties could see whether a variance was new, recurring, or already resolved — and disputes that festered for weeks started resolving in the same cycle.
FogLifter's analytics layer doesn't force every audience through the same dashboard. Each role enters through a view designed for the questions they're actually asking — all built on the same validated data underneath.
"The CIO Heat Map has a couple of very interesting elements. We isolate cost of assets — which is where TBM tools live — and layer on cost of service, which is where they don't. That's where the real insight is."
Most platforms can chart what's in the system. FogLifter charts what's been reconciled, correlated, and confirmed — which is what makes the output defensible.
Every metric traces back to the validated records behind it — not an aggregate that hides the gaps. The Validation View lets both parties comment, flag, and resolve discrepancies on the same record. At one healthcare customer, this turned a monthly dispute over thousands of servers into a same-cycle resolution process where evidence replaced argument.
Cost, asset, ownership, service, and risk views are connected through the ontology — so a cost spike can be traced to the service, the asset, and the owner in the same view. At one customer site, this correlation spans 70,000+ servers, 27 PB of storage, 3,300 applications, and SLA measurement across 3 suppliers — producing $55M+ in cumulative savings.
FogLifter tracks how records change across ingestion cycles — so teams can see whether a variance is new, recurring, or resolving. This is how billing disputes decline by 85%: not by hiding the differences, but by providing the history and context that lets both sides agree on what's real, what's expected, and what needs to be fixed upstream.
These are real discoveries from FogLifter deployments — the kind of insight that only surfaces when analytics run on validated, cross-domain data.
A customer was using an application from a major MRI manufacturer. Somebody had configured it 10 years earlier to write six different copies of every scan — a workaround for a network latency problem that had long since been resolved. No individual tool connected the storage allocation to the application config to the cost. FogLifter's cross-domain analytics surfaced it as an outlier in a routine capacity review.
Multiple customers moving to cloud for elasticity were running a quarter to a third of their provisioned capacity. The whole point of cloud was an elastic environment that expands and contracts — yet nobody had connected utilization data to service ownership and contract terms. FogLifter's analytics turned a vague "we should optimize" into an actionable, evidence-backed right-sizing plan tied to specific services and owners.
A customer purchased 1,200 Microsoft SQL Server licenses. FogLifter's software license utilization analytics went beyond "how many did we buy?" to "how many are actively used, by which applications, supporting which services?" The gap between purchased and consumed was significant — and invisible until analytics connected license records to actual application usage patterns across the environment.
Analytics is one layer. The pages below cover integrations, security, and AI — each built on the same validated foundation.
How FogLifter connects to CMDB, billing, cloud, infrastructure, and ticketing systems.
Enterprise access control, auditability, and governed data handling across the platform.
Natural-language questions grounded in FogLifter's ontology and validated data.
