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Data Visualization & Analytics UX

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Services

Data Visualization & Analytics UX

Data Visualization & Analytics UX

We design dashboards and analytics experiences that help people see what changed, understand why it matters, and know what to do next without drowning in charts.

We design dashboards and analytics experiences that help people see what changed, understand why it matters, and know what to do next without drowning in charts.

Minimal analytics interface with a rising line chart and a yellow highlighted insight

More data is not the same as more clarity.

Analytics products often grow one chart at a time. Every metric competes for attention, filters feel unpredictable, and users lose context as they move from an overview into detail. We start with the questions people need to answer and the decisions the product needs to support, then shape the hierarchy, visualizations and interactions around those jobs.

Moktiv campaign analytics dashboard with engagement trends and audience breakdowns

What you get

Decision-View Mapping

Analytics Information Hierarchy

Dashboard and Report Layouts

Chart and Table Selection

Filters and Time Controls

Drill-Down and Exploration Flows

States and Data Confidence

Responsive Analytical Views

Accessible Visualization Patterns

Visualization Component Rules

Clarity Before Charts

We map agreed metrics and user questions to the views and actions they need before choosing a chart. That keeps every analytical view tied to a real decision instead of a decorative visualization.

Exploration Without Losing Context

Filters, comparisons and drill-downs should help users investigate without forcing them to remember where they started. We prototype the full overview-to-detail journey with realistic data and states.

A Visualization System That Scales

Reusable rules keep charts, legends, tooltips, annotations, comparisons and responsive behavior consistent as metrics and product areas grow.

How it works

Analytics UX Process

Discovery
Architecture
Design
Validation

Analytics UX Process

Discovery
Architecture
Design
Validation

Analytics UX Process

Discovery
Architecture
Design
Validation

1. Discovery

We align on users, questions, signals, actions, data constraints and the moments where confidence matters most.

2. Architecture

We define the overview-to-detail hierarchy, comparisons, filters, analytical view transitions and progressive disclosure before visual styling begins.

3. Design

We design charts, tables, tooltips, filters, drill-downs and responsive states in realistic flows so the interaction can be reviewed before development.

4. Validation

We test comprehension, exploration, keyboard and responsive behavior, then document reusable rules and implementation-ready specifications.

Featured Case

See how we designed Moktiv, an influencer marketing platform with campaign intelligence, performance signals and budget visibility.

Moktiv

Plexable’s published Moktiv case shows a high-density command-center interface that brings campaign intelligence, performance signals and budget context together. The experience uses KPI summaries, time-series views and audience breakdowns to support scanning and deeper investigation.

Moktiv campaign analytics dashboard with engagement trends and audience breakdowns

B2B SaaS

B2B SaaS

Product Type

Product Type

Stockholm, Sweden

Stockholm, Sweden

Location

Location

Campaign Analytics

Campaign Analytics

Core Experience

Core Experience

KPI + Trends

KPI + Trends

Visualization

Visualization

Budget Visibility

Budget Visibility

Decision Context

Decision Context

Audience Data

Audience Data

Supporting View

Supporting View

Stronger analytics UX gives your product

  • Clear
    Hierarchy

    Separate signals, context and actions

    Give primary metrics a clear place, keep supporting context available and make the next analytical action visible.

  • Direct
    Exploration

    Move from overview to detail

    Preserve the current comparison and filter context as people investigate the data behind a signal.

  • Consistent
    Patterns

    Keep analytics behavior consistent

    Use repeatable chart, filter, legend, tooltip and state patterns across product areas.

  • Responsive
    Data Views

    Preserve meaning on smaller screens

    Adapt dense views without hiding the comparisons, labels and actions users rely on.

  • Implementation
    Confidence

    Give engineering clear rules

    Handoff includes states, responsive behavior, interaction notes and reusable visualization specifications.

Data visualization for every level of detail

Since 2008, trusted by brands that scale.

STC Logo

STC

,

Speedi Logo

Speedi

,

Motori Logo

Motory

,

POSRocket logo

POSRocket

,

Telenav

Moktiv Logo

Moktiv

,

GIANT Protocol logo

Giant

,

VIBES

VIBES

Vibes

,

And many more growing teams.

Are they trustworthy?

Theclearestproofiswhatclientssayaftertheworkships.
FAQ
FAQ
FAQ

Frequently Asked Questions

  • Yes. We can review the current experience, identify where hierarchy and interactions break down, and redesign the highest-impact views without forcing a complete product rebuild.

  • This service focuses on product strategy, UX, UI, prototyping, validation and handoff. BI-platform implementation or data engineering requires a separate, explicit scope.

  • Multi-Role Architecture defines roles, permissions, views and system structure. Data Visualization & Analytics UX defines how metrics, charts, comparisons, filters and analytical exploration work inside those views.

  • Yes. We can extend an existing system with visualization components and interaction rules, or define the analytics patterns that a broader design-system engagement should include.

  • Yes. We account for smaller screens, keyboard use, focus, contrast, clear labels, summaries and alternative ways to understand information where the product and data allow it.

  • The handoff can include validated flows, component states, interaction notes, responsive behavior, data-state coverage, chart rules and implementation-ready design specifications.

  • Yes. We can review the current experience, identify where hierarchy and interactions break down, and redesign the highest-impact views without forcing a complete product rebuild.

  • This service focuses on product strategy, UX, UI, prototyping, validation and handoff. BI-platform implementation or data engineering requires a separate, explicit scope.

  • Multi-Role Architecture defines roles, permissions, views and system structure. Data Visualization & Analytics UX defines how metrics, charts, comparisons, filters and analytical exploration work inside those views.

  • Yes. We can extend an existing system with visualization components and interaction rules, or define the analytics patterns that a broader design-system engagement should include.

  • Yes. We account for smaller screens, keyboard use, focus, contrast, clear labels, summaries and alternative ways to understand information where the product and data allow it.

  • The handoff can include validated flows, component states, interaction notes, responsive behavior, data-state coverage, chart rules and implementation-ready design specifications.

  • Yes. We can review the current experience, identify where hierarchy and interactions break down, and redesign the highest-impact views without forcing a complete product rebuild.

  • This service focuses on product strategy, UX, UI, prototyping, validation and handoff. BI-platform implementation or data engineering requires a separate, explicit scope.

  • Multi-Role Architecture defines roles, permissions, views and system structure. Data Visualization & Analytics UX defines how metrics, charts, comparisons, filters and analytical exploration work inside those views.

  • Yes. We can extend an existing system with visualization components and interaction rules, or define the analytics patterns that a broader design-system engagement should include.

  • Yes. We account for smaller screens, keyboard use, focus, contrast, clear labels, summaries and alternative ways to understand information where the product and data allow it.

  • The handoff can include validated flows, component states, interaction notes, responsive behavior, data-state coverage, chart rules and implementation-ready design specifications.

Make your product data easier to understand and act on.

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Contact

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Start a project

Tell us what
you’re building.

By submitting, you acknowledge our Privacy Policy and ask Plexable to respond. Your use of this website is governed by our Terms of Use.

Start a project

Tell us what
you’re building.

By submitting, you acknowledge our Privacy Policy and ask Plexable to respond. Your use of this website is governed by our Terms of Use.