From the data you already collect to the move you should make next.
Stratum runs the full analytics lifecycle on the parking and mobility data you already collect, reading it in, structuring what happened, forecasting what’s coming, and weighing what to do against the constraints you actually operate under. Reporting and forecasting are the floor. The recommendation is the point. Here’s how it works, end to end.
Analytics lifecycle · Descriptive · Predictive · Prescriptive · Recommendations
The analytics lifecycle you know, built to end in the move.
Every analytics platform walks the same path: pull the data in, describe what happened, predict what’s coming. That’s the standard lifecycle, and Stratum works across all of it, as a layer on the systems you already run. You can take the whole picture or just the last stage. Because that last stage is the difference: describing and forecasting tell you what’s true; the harder question is what to do about it, weighed against the permits, leases, and revenue you’re accountable for. That’s the step Stratum is built to take: a move you can defend.
It starts with the data you already collect.
Most of your parking data already exists; it’s just scattered. PARCS in one system, permits in another, revenue in finance, occupancy in a sensor platform, event and academic calendars somewhere else, none of them speaking to each other. Stratum connects to the systems you already run (PARCS and access control, permit and parking-account systems, LPR and occupancy sensors, financial systems, and shift, event, and seasonal calendars) and resolves those disconnected feeds into one continuous picture: occupancy, permit status, revenue, and access, normalized to the same lots, hours, and rate classes. Breaking the silos isn’t the headline (it’s table stakes), but nothing above it is trustworthy without it. No rip-and-replace, no new hardware. As connected-vehicle and telematics data come online through the Park.Easy ecosystem, they slot in as another feed: more signal in, sharper everything downstream.
stratum / connected sources
synced
transactions · entries
synced 2m ago
accounts · tiers
synced 12m ago
counts · dwell
synced 1m ago
schedule · capacity
synced 1h ago
What happened, without the manual assembly.
From those feeds Stratum builds the descriptive layer: occupancy by lot and by hour, dwell and turnover, utilization against what was actually sold, revenue by product and segment. It’s the reporting most teams still rebuild by hand every cycle; here it’s standing, current, and consistent. Table stakes, and the base everything above it stands on.
What’s coming, grounded in your calendar.
The predictive layer projects demand and overflow forward, by lot and by time-of-day. It doesn’t forecast on history alone: it reads what actually moves your demand: an event on the calendar, a shift change, a flight bank, a seasonal swing, a construction closure. A forecast that sees a sold-out event or a Monday-morning peak coming beats one that only knows last month’s counts. Still table stakes, but grounded ones.
This is where the work pays off.
Reads and forecasts set the table. Here Stratum weighs the move against everything it touches and hands your team something it can defend: the reason the platform exists.
A recommendation is a case, not a guess.
Every recommendation starts from one question: what changes, and what does it touch? Stratum weighs the candidate moves against the constraints you actually operate under: a bond covenant, a lease guarantee, a permit policy, the debt service on the garages, a service level you’ve committed to. Each option comes back scored on projected impact and effort, carrying the numbers and the reasoning that justify it, so the one you choose is one you can defend, whether that’s a budget meeting, a board review, or a public hearing. It surfaces moves your team hadn’t lined up, and it never takes the decision: it makes the case, you make the call.
See the move play out before it’s real.
Before you commit, run the move as a scenario. Stratum projects the effect across the things that interact (occupancy, overflow, service levels, and the revenue line) so you see the second- and third-order consequences, not just the first. Close a deck and watch where the overflow lands; move a rate and see the revenue and the access tradeoff together; shift capacity for a peak event and see which lots feel it downstream. Compare two or three approaches side by side, and walk into the decision with the tradeoffs already mapped instead of argued.
Product questions
Dashboards and reports tell you what happened, and Stratum does that too, as its descriptive layer. But it doesn’t stop there: it forecasts what’s coming and, above all, recommends what to do about it, weighed against your real constraints. The dashboard is the floor; the recommendation is the point.
Fast, because there’s nothing to rip out. Stratum reads the systems and data you already have, so the first recommendations come from your existing feeds: no new hardware, no year-long rollout. It deepens as you connect more.
No. Stratum is the advisor, not the actor: it does the analysis, brings back the moves worth making, and shows the reasoning behind each. The decision, and the authority for it, stays with your team. It makes the case; you make the call.
Everyday operational calls and higher-stakes ones: permit allocation against the schedule, rate changes, lot closures and construction phasing, event and move-in plans, enforcement posture, mode-shift tradeoffs: anything where the move touches occupancy, revenue, access, and the obligations you’re accountable for.
No. Stratum is a layer on top of what you already run (PARCS, permits, LPR, financials, calendars), not a replacement. It’s vendor-neutral and works alongside the tools you already trust.
See it run on your own numbers.
Bring one real decision you’re facing (a rate change, a lot closure, an event plan) and the data you already collect. We’ll run it through the lifecycle and show you the move, grounded in your constraints.