Monitoring Software for IT Companies: What to Track
Monitoring software for IT companies works when it tracks the work that actually produces value — project activity, code review turnaround, support load, and delivery — and fails when it tracks keystrokes and mouse movements. I manage engineering for a software company with roughly 30 developers across product, infrastructure, and customer support tiers, and I have run experiments with nearly every category of monitoring tool on the market. The short version: for knowledge workers who ship code, dashboards full of activity minutes are noise, while a few well-chosen signals tell you almost everything about how the team is doing.
The trap of tracking keystrokes for developers
Developers are the worst possible fit for keystroke-level monitoring, and I can demonstrate why with a concrete example from our own team. A senior engineer spent three days last quarter working through an authentication bottleneck. Her activity report from a trial monitoring tool showed 12 percent active time in the editor — an obvious "red flag" to a manager who does not write code. What the report could not see was that she spent most of those three days reading an open-source library's source code in a browser, drawing a sequence diagram on a whiteboard, and thinking about a state machine on a walk. The feature shipped on schedule, with half the defect rate of the previous quarter's average.
That is the core problem: for developers, idle time in the editor is often the most productive time of the day. Any monitoring setup that penalizes it will train engineers to fake activity, which destroys the signal you actually want. If your tool's default dashboard would flag that engineer as unproductive, the tool is wrong, not the engineer.
What we actually track: project tools, not people
After that experiment, we rebuilt our approach around data that originates in tools the team already uses, with an activity layer on top for visibility into work across those tools. Today we track five things:
- Pull request cycle time. Time from branch creation to merge. This catches review bottlenecks better than any activity graph.
- Review depth and turnaround. How long reviews sit before feedback, and whether feedback comes back within a working day.
- Sprint commitment accuracy. Planned versus completed points, which shows whether planning is realistic before you blame anyone.
- Support ticket load and resolution time. For the engineers who staff support rotations, this is the productivity metric that matters.
- Application-level activity by project. Which projects and tools get time, at the level of "spent four hours in the IDE on project Delta," not "clicked 2,400 times."
The monitoring software fills in the gaps between tools — for example, an engineer who is waiting on infrastructure may look idle in the repo but is visibly active in a deployment console. Combined with the version control history, that gives us a picture of a day that matches what the engineer would describe, which is the test of a good system: the data should surprise nobody who knows the work.
Scenario: a sprint that went sideways
Here is the most instructive case we have had. A seven-person team was on a six-week sprint to rebuild our billing integration, and by week three, delivery had slowed to a crawl. The pull request data showed two things: one reviewer was taking an average of 31 hours to respond, and three engineers had long blocks of time marked against a project that had been deprecated a year earlier — they were still updating code that no longer mattered because an API contract change had never been communicated.
No amount of screen monitoring would have surfaced either problem. Review cycle time and project-level time reporting did. We moved the reviewer off a competing initiative, killed the deprecated project work, and the team recovered the lost week and a half by the end of the sprint. The lesson I keep repeating to executives who ask for "visibility into what the developers are doing": the monitoring question is not what they are doing, it is what the system is doing to them — and tool-level data answers that question directly.
Support engineers and on-call visibility
The second scenario involves our support tier, eight engineers who split on-call rotations with product development. Before we added monitoring, we had a recurring complaint from support that product engineers were not pulling their weight on customer issues. We tried to arbitrate with memory and it did not go well. We pulled a month of data instead: time logged against support tickets, after-hours activity during on-call weeks, and escalation response times. The numbers showed the rotation was actually lopsided in the other direction — two support engineers were quietly covering gaps created by vacation conflicts, and product engineers were handling a disproportionate share of escalations that should never have left support.
The data settled the argument in one meeting. We rebalanced the rotation, introduced an official handoff process, and the complaints stopped. For IT companies, monitoring's highest-value use case is resolving disputes about workload with evidence rather than emotion. That works because the metrics are tied to work artifacts — tickets, commits, reviews — that everyone agrees count as work.
Dashboards that managers actually read
A final piece of advice from years of watching dashboards go stale: keep the management view small. We show leadership exactly three numbers a week — delivery trend, review turnaround, and support backlog. Everything else lives in team-level views where engineers can see their own data and their own flow. When a manager asks to see an individual's minute-by-minute timeline, I treat that as a coaching problem, not a monitoring problem.
If you are selecting monitoring software for your IT company, evaluate it against the question of whether it measures work artifacts or just keystrokes. We landed on WorkAuditor, a cloud-based employee monitoring software for Windows and Mac, because its activity and application-level reporting pairs with project data without trying to score how fast someone types. The tool should extend your existing workflow signals, not replace them with invented productivity scores.
What would your team's pull request and ticket data say about your sprint right now — and would you believe it if you saw it on a dashboard?