UX/UI Dashboard Audit: Turn “nice charts” into decisions
If your company already has dashboards and the data is correct, yet decisions still stall, you don’t have a data problem—you have a dashboards audit problem. A focused UX/UI dashboards and reports audit makes visualizations actionable: it removes noise, clarifies “good/bad” at a glance, and shows the next step for each role.
Why a dashboards audit (not “redesign for prettier colors”)
A reports audit is about actionability—how fast a user can answer: Are we on plan? If not, why? What do we do next?
Typical signs you need an audit now: absolute numbers with no status; one overloaded dashboard used for four scenarios; a “filter zoo” with no role presets; conflicting metric definitions; users exporting to Excel or pinging analysts for routine answers.
Outcome: one screen → one decision. Plan/YoY/trend are visible, owners are clear, and the next click leads to the driver.
Who benefits (and how)
- CEO/GM: a single “Plan vs Actual” page with YoY and trend arrows. Example for a 100-person SaaS: MRR +2.9% vs plan (green), NRR 108% (green), Churn 3.1% (amber).
- Sales: pipeline quality by segment with win rate, stage conversion, AOV, owner-coded colors, drill to reps.
- Marketing: Cohort LTV vs CAC with green/amber/red and prescriptive prompts (e.g., “Cut Display −20% (ROAS 0.7); scale Brand +15% (ROAS 4.2)”).
- Finance: one definition of Revenue, one FX policy, one close cutoff.
- Analytics/DE: fewer repeat tickets because the UI answers them.
What we actually do (method)
We run a structured UX/UI dashboards and reports audit:
- Role interviews to map real decision moments (e.g., “Mon 9:00 budget reallocation”).
- Inventory & navigation map of all dashboards.
- Usage analysis (logs & interviews) to find dead screens and stalls.
- Heuristics & metric review with our Audit Metric API checklist (each KPI must show good/bad via plan/YoY/trend and lead to a next step).
- Two scores per dashboard & role: Actionability (0–100) and UX/UI (0–100).
- Three change options: Minimal (now), Medium (2–4 weeks), Strategic (quarter)—we document what to change and why.
We deliver recommendations, prototypes, and standards
Case: SaaS company (100 people) unifying BI
Stack: Snowflake DWH; Looker + Tableau → moving to Power BI.
Landscape: 54 dashboards, 472 visuals, 59 users. 10 used daily, ~30 weekly; the rest rare/archived.
Pain: inconsistent metric names and colors, heavy pages, conflicting definitions; despite “a lot of data,” decisions were slow; the data team was overloaded.
Findings: Sales Overview had no plan/YoY (Actionability 41/100); MRR chart showed absolutes only; 12 filters defaulted to “All”; “Revenue” meant Gross on one page and Net on another.
Guidance we delivered:
- Minimal: remove 8/14 charts; add Plan vs. Actual, YoY, 7/28-day trend; split into Status and Drivers.
- Medium (2–4 wks): three scenario-specific dashboards (Presentation / Ops / Personal), role presets; glossary v1 for MRR, NRR, Churn, AOV, CAC, LTV.
- Strategic: dashboard brandbook (colors by function: Sales=green, Marketing=blue, Support=purple, Finance=gold), 20 Figma templates (Executive, Sales Ops, Marketing ROAS, Support SLA), KPI ownership and adoption plan.
Results (client implemented our guidance):
WAU +72% at 30 days, +118% at 90 days; Time-to-Answer “Why under plan last week?” 27→9 min (30d), 5 min (90d); repeat analyst tickets −38% (30d), −55% (90d); BI NPS +17 points (90d).
What you get (tangible)
A written report with annotated screenshots, per-dashboard fixes, a 10–20 item quick-wins backlog for 2–4 weeks, a light KPI glossary (owners/refresh cadence), and Actionability/UXUI scores by dashboard and role so progress is measurable.
Scope reminder: recommendations only
Tool-specific dashboards & reports audit (Power BI, Tableau, Metabase, Apache Superset, Looker, Omni)
Below is a compact dashboards audit overview by tool—useful for SEO and, more importantly, for mapping the exact checks we run.
Power BI dashboards audit
Where it shines: robust DAX model layer, role-aware slicers, drill-through, bookmarks for scenario toggles, strong theming.
Common pitfalls we fix: 15+ visuals on a page; cross-highlight chaos; slicers with no sensible defaults; inconsistent DAX naming; unreadable small fonts.
Our checks: report/page/visual interactions; Plan vs Actual cards with thresholds; drill-through paths; measure naming ([m_…]), display units/time intelligence; role presets with persistent filters; accessibility (12–14px min).
Quick win: replace 6 KPI tiles with one Status card (plan/YoY/trend), add a Drivers pane (Top-3 variances), and use bookmarks to switch Presentation vs Ops layouts.
Tableau dashboards audit
Where it shines: fast exploration, visual grammar, device layouts, dashboard actions.
Pitfalls: overuse of “Show Me” defaults; heavy quick filters; wrong LOD for grain; no device layouts; color clash.
Our checks: LOD correctness for KPI grain; Context vs Extract filters; Dashboard Actions vs basic filters; device layouts; Performance Recording for slow sheets.
Quick win: move 8 quick filters into 3 context filters + 2 high-value actions; add a YoY delta band and a goal line on KPI charts.
Metabase dashboards audit
Where it shines: lightweight, friendly query builder, Segments/Metrics, pulses.
Pitfalls: everything is ad-hoc SQL; no central metrics; global filters mis-mapped; time-zone confusion.
Our checks: adoption of Segments/Metrics; parameterized SQL vs GUI queries; dashboard-level parameters; caching TTL; naming/collections; pulses for alerts.
Quick win: promote 6 recurring SQL snippets into Metrics, wire them to a single dashboard filter, and add a weekly pulse for out-of-band alerts.
Apache Superset dashboards audit
Where it shines: open-source, SQL Lab, native filters, tabs, good at large datasets.
Pitfalls: mixed dataset definitions; no certified metrics; filter components not bound; role sprawl.
Our checks: certified datasets/metrics, native filter bindings & cross-filters, tab structure, cache configuration, RLS, feature flags consistency.
Quick win: certify KPIs in the semantic layer, add a Native filter bar with role presets, and split a mega-page into tabs (Status / Drivers / Details).
Looker reports audit
Where it shines: LookML semantic layer, governed Explores, scheduling, stable definitions.
Pitfalls: Explore sprawl; PDTs failing; dimension/measure drift; Looks used as dashboards; weak naming.
Our checks: LookML consistency (joins, refinements), PDT health, Explore curation, dashboard vs Look usage, field naming conventions, schedule hygiene.
Quick win: consolidate 4 overlapping Explores into 1 governed Explore; rebuild “Looks” as true Dashboards with tile-level plan/YoY/trend and role defaults.
Omni dashboards audit
Where it shines: modern UX, strong metrics-layer philosophy, fast iteration for analysts and business users.
Pitfalls: ad-hoc metrics outside the layer; joins vary per dashboard; role presets missing.
Our checks: centralized Metrics definitions vs ad-hoc fields, join integrity across dashboards, parameter usage, filter presets, schedule/alert hygiene.
Quick win: migrate top-5 KPIs into the metrics layer, enforce uniform joins, and add role-based presets so Sales/Marketing land on different defaults.
How a reports audit changes the day-to-day
Before: Monthly Sales shows “$1.2M.” Meetings start with “is that good?” and end 30 minutes later with “pull me a segment cut.”
After: Sales Ops — Daily shows Revenue vs Plan with thresholds; a callout explains “Close rate −2.1pp in SMB East”; the next click drills to Deals with >15% discount. No analyst ticket required.
Packages & pricing
Express Audit — free, ≤48 hours → 3–5-page memo, top-10 quick wins, preliminary score.
Baseline Audit — 1 week, $2–5k → 15–25-page report, per-dashboard fixes, 10–20 quick wins, glossary v1, scores; 2 weeks Q&A.
Extended Audit — 2–4 weeks, $5–10k → usage analysis, new IA, 3–5 Figma prototypes, light BI design-system, measurement plan; 4 weeks Q&A.
Comprehensive Audit — 4–6 weeks, $10–20k → dashboard brandbook, 10–20 Figma templates, KPI governance, 6–12-month roadmap, adoption comms; 6 weeks Q&A.
Out of scope (all packages): implementation, migrations, ETL/modeling, performance tuning, security/DevOps/licensing, legal/compliance.
Measuring impact & guarantees
30/90-day targets: WAU +30–60% / +50–100%, Time-to-Answer −60–80%, repeat tickets −40–60%, BI NPS +15 pts.
Risk-reversal: Baseline includes 10+ concrete improvements for a key dashboard—or we extend the report free; guidance to reach ≥70/100 UX/UI on agreed screens; start with the free 48-hour express audit and stop with no obligation.
Why Data Never Lies
A dedicated dashboard design team trained on Power BI, Tableau, Apache Superset, Metabase, Looker, Omni; our internal UX/UI school (3+ years); industry templates for SaaS, e-commerce, manufacturing; and a method built around Actionability and UX/UI scores—so your dashboards audit leads to faster, better decisions rather than prettier screenshots.
Find out if your system is set up right
Book the free 48-hour dashboards & reports audit. We’ll review 2–3 dashboards, score them, and hand you a concrete 10-item quick-wins list your team can implement immediately.