Measurement & Reporting

Nexthink Dashboards

A Nexthink dashboard that nobody opens is worse than no dashboard: it creates the illusion of measurement while producing no decisions. Building dashboards that get used requires starting with the audience and the question they need answered, not with the data you have available. This guide covers how to build dashboards that become part of how your organization operates, not artifacts it ignores.

4
Distinct dashboard audiences: each needs a completely different view
NQL
Powers custom widgets: query any platform data in real time
Live
Widgets update in real time from Collector telemetry: no manual refresh
Shared
Dashboards can be published org-wide or scoped to specific teams

Most Dashboards Are Built Backwards

The most common dashboard failure pattern starts with a tour of available widgets: "here's what we can show" becomes the organizing principle, and the result is a screen of metrics that nobody specifically asked for and nobody knows how to act on. Dashboards built this way get looked at once during a demo and never again.

The right sequence is the opposite: start with the stakeholder, identify the decision they need to make or the question they need to answer on a regular basis, then find the one or two metrics that most directly inform that decision. Everything else is noise. A dashboard with three metrics the right person checks every morning is more valuable than a dashboard with thirty metrics the right person never opens.

The test for a useful dashboard metric: Can you describe what you would do differently if this number went up versus down? If the answer is "nothing" or "I'd look at other data": the metric shouldn't be on the dashboard. It might belong in an ad-hoc investigation, but not in a dashboard that someone is supposed to monitor.

Four Dashboards for Four Audiences

Every organization needs at minimum four separate dashboards: one for each distinct audience. Combining audiences in a single dashboard produces a view that's too granular for executives and too abstract for ops teams. Build these as separate, purpose-built views from the start.

Executive / Leadership

CIO, CISO, IT VP: strategic view, not operational

Executive dashboards answer one question: is the employee digital experience improving over time, and how does it compare to baseline? Executives don't need to know about individual devices or specific incidents: they need trend lines, program-level health scores, and evidence that IT investment is producing measurable experience outcomes.

The fatal mistake is giving executives operational data. A table of devices with low DEX scores is not an executive view: it's a technician view. Translate everything into business impact and program direction.

  • Overall DEX Score: trending up/down over 30/60/90 days
  • % of fleet above acceptable experience threshold
  • Ticket volume trend: is automation reducing load?
  • Spark first-contact resolution rate
  • Top 3 experience detractors: in plain language, not metric names
  • AI tool adoption rate: if AI Drive is licensed

IT Operations

IT managers and senior engineers: fleet health, active issues

IT operations dashboards focus on fleet health right now: what's degraded, what automation is running, and where manual attention is needed. This is the dashboard that gets checked at the start of each shift and after major changes. It should surface actionable conditions, not just metrics.

The key difference from an executive dashboard is specificity: ops teams need to know which devices are affected, not just the percentage. Surface device lists, not aggregate numbers, for anything that requires action.

  • Devices below performance threshold: with drill-down
  • Active software crashes: by application, last 24 hours
  • Flow execution summary: workflows run, success rate, failures
  • Collector health: coverage percentage, offline devices
  • Patch compliance rate: by OS, by department
  • Security posture flags: devices out of policy

Service Desk

Tier-1 and Tier-2 agents: ticket context and resolution

Service desk dashboards focus on what agents need to handle the current ticket queue more effectively. These are often best delivered through Amplify (embedded in the ITSM tool) rather than as a standalone Nexthink view, but a queue-level dashboard gives supervisors visibility into agent performance and ticket patterns.

Keep service desk dashboards specific to the last 24–48 hours. Service desk teams operate in the present tense: what's happening now, not what happened last quarter.

  • Top reported issues: by category and volume, rolling 24h
  • Spark resolution rate vs. escalation rate
  • Mean time to resolve: by issue category
  • Devices flagged for known issues: proactive context for inbound tickets
  • Application performance alerts: what's affecting the most users right now

DEX Program Management

DEX leads and IT program managers: program KPIs and initiatives

DEX program dashboards track the outcomes of deliberate improvement initiatives: a hardware refresh, a software migration, an Engage campaign program, a new automation rollout. These dashboards are about change over time and whether specific programs are delivering the expected experience improvements.

This audience needs before/after comparisons more than current state. Build dashboards around milestones and initiative timelines, not just point-in-time metrics.

  • DEX score by department/location: to identify cohort gaps
  • Engage campaign sentiment trends: linked to specific initiatives
  • Hardware refresh impact: experience score before/after device replacement
  • Software adoption rate: new tool rollouts
  • Proactive vs. reactive remediation ratio
  • Initiative milestone tracking

Widget Types and NQL-Backed Customization

Nexthink Infinity provides a library of pre-built widgets for common metrics, and NQL-backed custom widgets for anything the standard library doesn't cover. Understanding when to use each determines how quickly you can build dashboards and how flexible they can be.

Metric widgets

Display a single number: a DEX score, a device count, a percentage. Best for headline stats at the top of a dashboard. Can show trend arrow (up/down vs. previous period) without requiring a trend chart.

Bar / column charts

Compare a metric across categories: DEX score by department, crash rate by application, device count by OS version. Best for "which is highest/lowest" questions. Limit to 8–10 categories maximum.

Trend lines

Show how a metric changes over time. Essential for executive dashboards and program tracking. Most effective at 30–90 day ranges: shorter ranges create noise, longer ranges lose relevance for operational audiences.

List widgets

Show a ranked or filtered list of devices, users, applications, or events. Essential for operational dashboards where "which specific devices" matters. Support drill-through to device details without leaving the dashboard.

Donut / pie charts

Show proportion: percentage of fleet above threshold, share of tickets by category. Use sparingly; they become unreadable with more than 5 segments. Prefer bar charts when comparing more than 3–4 categories.

NQL custom widgets

Write an NQL query and bind the results directly to any widget type. Enables any metric the standard library doesn't cover: custom experience scores, cross-entity aggregations, time-windowed analyses. Requires NQL proficiency but adds unlimited flexibility.

When to use NQL vs. standard widgets: Start with standard widgets: they're maintained by Nexthink, require no query authorship, and update automatically when the data model changes. Move to NQL custom widgets when you need a metric that's specific to your environment, a cross-entity aggregation the standard library doesn't support, or a time comparison that standard widgets can't express. NQL widgets are powerful but require maintenance when the platform data model evolves.

Why Dashboards Stop Getting Used

These are the patterns that consistently produce dashboards that look complete in a demo and become irrelevant within a month. Most are avoidable if you catch them during design.

Showing everything that's available

A dashboard with 30 widgets signals that nobody decided what matters. Users scan it once, find nothing immediately actionable, and stop returning. The visual complexity signals "comprehensive" but the message received is "noise."

Fix: Limit to 5–7 widgets per dashboard. If you need more, split into separate audience-specific dashboards.

Using technical metric names with non-technical audiences

"Collector heartbeat rate" and "NBI score" mean nothing to a CIO. Executives disengage immediately when dashboard labels require decoding. This is the single most common reason executive dashboards get abandoned.

Fix: Rename every widget title and label using business language. "% of employees with a good digital experience" instead of "devices above DEX score threshold."

No baseline, so trends are meaningless

A DEX score of 6.8 is good, bad, or neutral depending entirely on whether that's up from 5.2 or down from 8.1. Without a baseline and trend, any metric is an orphaned number with no interpretive value.

Fix: Every key metric should show at minimum a 30-day trend and a comparison to a baseline period. Establish baselines before launching improvement programs.

Operational data in an executive view

Showing executives a list of specific devices with low scores sends the message "this is a monitoring tool for technicians." Executives interpret this as seeing the tool's output, not the program's results. It undermines confidence in the DEX program itself.

Fix: Executive views should contain only aggregate percentages, trend lines, and program-level summaries. Device-level data belongs in operational dashboards.

Metrics with no owner and no action

If no specific person is accountable for a metric and there's no defined action when it moves out of target range, the metric is decoration. Monitoring without ownership produces reports nobody acts on.

Fix: Before adding a metric to a production dashboard, define: who owns it, what the target range is, and what action is triggered if it falls outside that range.

What Makes a Dashboard Durable

Dashboards that stay relevant past the initial launch share a set of design characteristics that aren't about aesthetics: they're about how the dashboard fits into the team's actual workflow.

One question per dashboard

Design each dashboard to answer a single, well-defined question. "Is the fleet healthy right now?" is an IT ops dashboard. "Is our DEX program improving?" is a program management dashboard. They require different data and different audiences, and mixing them produces neither well.

Match the time horizon to the audience

Executives think in quarters. Operations teams think in hours. Service desk agents think in minutes. Set your default time windows accordingly: and make sure the window is obvious on the dashboard face so the audience isn't guessing whether they're looking at today's data or last month's.

Always show direction, not just value

A single number with no comparator requires the viewer to remember the previous value. Every key metric should show its direction (up/down arrow) and either the change amount or the comparison period. This works whether the metric is improving or worsening: direction is the signal.

Make the threshold visible

If a metric has a target threshold (e.g., DEX score above 7.0 = good), show the threshold on the visualization. A trend line that sits at 6.8 communicates very differently when the viewer can see the target is 7.0. Without the threshold, viewers have no frame of reference for interpretation.

Involve the audience in design

Run a 30-minute session with one representative from each target audience before building. Ask: "What question do you check the answer to most frequently?" and "What's the first thing you'd want to know if something went wrong?" These answers define the dashboard, not the tool's capability inventory.

Scheduled review cadence

A dashboard without a defined review cadence becomes background wallpaper. Assign each dashboard a review schedule, weekly for operations, monthly for executives,and make that review a calendar event with an owner. The review meeting is where dashboards produce decisions.

Related Topics

NQL is the query language behind custom dashboard widgets. If you want to go beyond the standard widget library and build metrics specific to your environment, the NQL Guide is the natural next step.

NQL Guide Amplify Engage Use Cases

Dashboard design guidance on this page reflects practitioner experience and Nexthink official documentation for the Infinity platform. View full references →