The 2-Minute Gist
Dashboards often fail because they focus on reporting rather than decision-making. Key takeaways:
- Problem: Dashboards are treated as collections of charts rather than decision flows.
- Solution: Focus on 'Performance Dimensions' rather than just KPIs.
- Outcome: Create dashboards that drive accountability and result in specific actions.
Today, dashboards are everywhere - on websites, in apps, across government programs, enterprises, startups, NGOs, and platforms of every size.
Yet, most dashboards end up being:
- Looked at occasionally
- Used mainly for reviews and presentations
- Ineffective when real, day-to-day operational decisions need to be made
Despite the explosion of data and dashboards, decision-making remains largely unchanged.
The reason is simple.
Most dashboards are designed to report data, not to drive decisions.
A monitoring dashboard should not be visualized as a collection of charts. Rather it needs to be visualized as a decision-support system.
In this blog, we’ll walk through a practical framework for designing monitoring dashboards that actually help teams act, intervene, and course-correct. It’s not theory, it is a compilation of our real-world operational experiences of having put together different MIS systems.
Why Most Dashboards Fail
Before discussing design, it’s important to understand why dashboards fail in the first place.
Common failure patterns include:
- Tracking too many KPIs without clarity on priority
- Showing metrics without targets or benchmarks
- Aggregated views that hide more than what they reveal
- Nothing on the dashboard to alert decision makers, everything looks "fine"
- No clarity on who is accountable for what
When dashboards are visualized in this manner, they end up becoming passive reporting tools rather than active management instruments.
While “What happened?” is important to know, but “What happened” should silently lead us to “What should we do next?”
A Monitoring Dashboard Is a Sequence not a collection. Effective monitoring dashboards are built on design principles for usability and purpose, not visual aesthetics alone.
A robust monitoring dashboard answers ten fundamental questions:
- What are we measuring?
- Why are we measuring it?
- How exactly is it calculated?
- At what level of aggregation does this need to be calculated?
- Over what time period?
- How should it be visualized / presented for effective understanding and decision making?
- How should someone be alerted on the dashboard?
- Who can see or act on this data?
- How can they have only access to the data that is relevant for their role?
- How long should data be retained?
- Who else can this data be shared with?
If your dashboard cannot clearly answer these questions, it is incomplete - regardless of how good it looks.
Step 1: Define the Objective of the Dashboard
Every monitoring dashboard must start with a clearly articulated objective.
Examples of poor objectives:
- “Track project progress”
- “Show KPIs”
- “Provide visibility”
Examples of strong objectives:
- Identify locations / departments falling behind monthly targets
- Enable early intervention in underperforming blocks
- Compare peer performance to drive accountability
- Trigger alerts when operational thresholds are breached
The objective determines:
- Which KPIs exist
- Which visuals are used
- Which alerts are configured
- Who needs access
- How often it should be updated
Without this clarity, dashboards drift into data clutter.
Step 2: Define Performance Dimensions (Not Just KPIs)
One of the most overlooked concepts in dashboard design is performance dimensions.
Performance dimensions define how success is interpreted, not just measured. These dimensions provide context.
Typical dimensions include:
- Target Achievement – Actual vs planned
- Performance Progression – Incremental or cumulative progress
- Trend Analysis – Historical movement and direction
- Peer Comparison – Relative performance across regions or units
For example:
1,000 user visits on a page mean nothing without knowing:
- Was the target 800 or 2,000?
- Is this improving or declining?
- How does this compare to similar pages / competitor pages?
Dashboards without performance dimensions leave decision-makers with inadequate information to make effective decisions.
Step 3: Translate Dimensions into Well-Designed KPIs
KPIs convert abstract dimensions into measurable indicators.
Good KPIs are:
- Specific
- Measurable
- Actionable
- Time-bound
But mature dashboard systems go further. They balance:
- Lagging KPIs (outcomes achieved)
- Leading KPIs (signals of future outcomes)
For example:
- Lagging: Number of completed site visits
- Leading: Average time between scheduled and completed visits
This balance allows teams to predict issues before they surface, rather than digging through the data for explanation afterwards.
Step 4: Standardize Metrics and Calculations
This is where many dashboards quietly break.
Two teams looking at the “same KPI” often see different numbers because:
- Calculations differ
- Data sources vary
- Edge cases aren’t defined
A robust dashboard defines metrics across three attributes:
- Measurement Domain
- Single-stage (e.g., number of visits)
- Multi-stage (e.g., project phases)
- Measurement Expression
- Count
- Percentage
- Ratio
- Index
- Measurement Calculation
- Formula
- Dependencies
- Source fields
What exactly is being measured?
How is it expressed?
How is it calculated?
This level of definition ensures consistency, scalability, and trust.
Platforms like ViewZen Analytics allow dashboard designers to standardize the metrics centrally, so every report, dashboard, and alert uses the same metric definitions.
Step 5: Decide the Right Level of Granularity
Granularity defines how deep you can drill down.
Typical levels include:
- By Location
- By Organization Hierarchy
- By Time
- By Phase and so on
More granularity enables:
- Targeted interventions
- Root-cause analysis
- Accountability
But excessive granularity without purpose leads to:
- Data overload
- Poor data quality
- Analysis paralysis
The right question is not:
“How granular can we get?”
But:
“At what level do decisions need to be made?”
Effective dashboards should allow users to progressively drill-down.
Step 6: Design the Time Dimension Carefully
Time is not just a filter; it’s a design decision.
Key considerations include:
- Measurement frequency (daily, monthly, quarterly)
- Trend windows (month-to-date, year-to-date)
- Snapshot vs recalculated metrics
One critical insight from real-world systems:
Historical KPI snapshots should be stored. Recomputing past KPIs from changing data is:
- Expensive
- Error-prone
- Often misleading
Mature platforms preserve KPI values at defined intervals, ensuring historical accuracy even as data evolves.
Step 7: Choose Visualizations That Reduce Cognitive Load
Good visualization answers questions instantly.
Examples:
- Line charts → trends
- Bar charts → comparisons
- Tables → detailed accountability
- Color cues → status at a glance
Bad visualization:
- Forces interpretation
- Requires explanation
- Looks impressive but slows decisions
The goal is not beauty; it’s speed and ease of understanding.
Dashboards should allow a decision-maker to identify:
- What’s wrong
- Where it’s wrong
- How severe it is
- quickly and effectively
Step 8: Build Alerts That Trigger Action
Monitoring without alerts is passive reporting. Alerts define:
- What constitutes risk
- When intervention is required
- Who needs to act
Effective alert systems use:
- Static thresholds (fixed benchmarks)
- Dynamic thresholds (peer-based, trend-based)
For example:
- Bottom 10% performers
- Sudden drops vs moving average
- Deviations from peer groups
Alerts should integrate across:
- Dashboards
- SMS or messaging platforms
This is where analytics becomes operational, not observational.
Step 9: Define Access, Governance, and Accountability
Dashboards expose power and power mandates governance.
Role-based access ensures:
- Leaders see aggregated views
- Managers see jurisdictional data
- Sensitive fields remain protected
Clear access control also enforces accountability - people are responsible for what they can see and act upon.
Step 10: Treat Dashboards as Living Systems
Finally, dashboards must evolve. As programs mature:
- KPIs change
- Targets shift
- New dimensions emerge
Designing dashboards as configurable systems, not fixed reports, is critical.
This is where analytics platforms like ViewZen Analytics matter, enabling:
- Metric redefinition without rework
- Scalable drill-downs
- Governance built into the data layer
Closing Thought
A monitoring dashboard is not about showing data.
It’s about enabling decisions at the right level, at the right time, by the right people.
When designed correctly, dashboards become:
- Early warning systems
- Performance accelerators
- Accountability frameworks
And that’s when analytics starts delivering real value.