Presenting
We design decision-making tools.
By Gaurav Sinha
From
What charts show this data best?
To
What data relationships, user workflows, and interaction patterns am I working with?
In an industrial environment, you're never designing in isolation.
When you design a demand planning dashboard, you're actually designing within -
1.
Complex data models with multiple variables and confidence levels.
2.
User workflows that move from exploration to scenario testing to executive reporting.
3.
Data pipelines feeding different algorithms and simulations.
4.
Reusable components for parameter adjustment, scenario comparison, trend analysis.
And many more business constraints…
Why It Matters?
Early decision making
You understand data constraints before designing impossible interactions.
Scalability
Your designs work across different models, user types, and business contexts.
Better product development
You design interfaces that match how different users actually think and work.
The designers who understand business workflows become strategic partners.
What Architecture Means for Design
It's about how information is organized and how users move through it.
For our dashboards, this means -
How different data dimensions relate to each other (time, geography, product lines, scenarios, etc.)
The logical flow -
Raw Data ▹▹▹ Insights ▹▹▹ Decisions ▹▹▹ Actions
How different views connect -
Executive Summary ▹▹▹ Detailed Analysis ▹▹▹ Scenario Testing
Define Goals Upfront -
What decision is this supporting? (Strategic planning? Operational adjustments? Performance monitoring?)
What level of detail do different users need? (C-suite vs. operations managers vs. data teams)
How will this scale as data volume or user complexity grows?
What are the performance constraints?
Understanding Information Movement
It's about how information transforms as it moves through your dashboard system.
Raw Business Data ▹▹▹ Product Metrics ▹▹▹ Analytical Insights ▹▹▹ Scenario Outputs ▹▹▹ Actionable Recommendations
Each stage has different refresh rates, confidence levels, and user expectations.
Some data is real-time (current performance), some is calculated (insights), some is user-generated (scenario inputs) & some is predicted (projections).
Understanding data flow changes how you design.
Loading states
Real-time data needs different loading patterns than heavy simulations.
Error handling
What happens if scenario calculations fail vs. when live data is stale?
User feedback
How do users know if they're looking at fresh data or cached results?
Interaction timing
Can users adjust parameters while calculations are running?
When you understand this flow, you design interfaces that feel responsive and trustworthy, not just functional.
What Users Actually See
Interfaces are where your architecture and data flow become tangible for users.
Your interfaces should match how users think through problems.
Progressive disclosure
Start with the answer, allow drilling into details.
Context preservation
Users can see how their current view relates to the bigger picture.
Confidence indicators
Users understand data reliability and model certainty.
Action orientation
Clear path from insight to next steps.
Interface Design Principles
Cognitive load
Reduce mental effort needed to extract insights
Speed to insight
Fastest path to the key information
Trust building
Users understand what they're looking at and why it matters
Workflow integration
Fits naturally into how decisions actually get made
Good interfaces feel invisible - users focus on the business problem, not figuring out the dashboard.
Why Components Come Last
They're reusable patterns you identify after understanding your architecture, data flow, and interfaces.
Your interfaces should match how users think through problems.
Data visualization components
Chart types optimized for business metrics, trend indicators, scenario comparisons
Input components
Parameter sliders, scenario builders, filter controls that work across different models
Navigation components
Breadcrumbs, drill-down patterns, view switchers that maintain context
Feedback components
Loading states for heavy calculations, confidence indicators, data freshness signals
When you think in systems, you design components that accelerate future work while maintaining consistency across all dashboards.
Before Starting Any Design Work, Ask!
Start with: "What system am I designing within?"
What business decision does this support? How do different user types move through this analysis?
Who's the primary user and what's their decision-making process? What level of detail do they need?
What data feeds this? How fresh is it? What happens when calculations take time or fail?
What user patterns have we solved before that can be applied here?
The Meta-Insight
The goal isn't perfect system thinking from day one - it's building the habit of considering these interconnections before you start designing.
Did I make sense?
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