Why Retrofits Fail: Building True Accessibility in Analytics

We don’t need another reminder that accessibility is important. We know. Most product teams do.

The real question isn’t “should we care about accessibility?”

It’s: What does accessibility actually look like inside a complex, interactive analytics platform?

And maybe more importantly: Why does it still get bolted on at the end — and break?

This isn’t a checklist conversation. It’s a product one. A UX one. A system design one.

And if you’ve ever tried to make a dashboard usable for people with visual or motor impairments after the fact, you already know:

Retrofits fail.

The Complexity of Analytics Is Both the Problem and the Opportunity

Analytics products are not like websites or marketing pages. They’re built for power users, data exploration, speed, and customization. But that power also creates a deep challenge for accessibility:

• Dashboards are dynamic: content loads, refreshes, shifts.
• Interaction patterns vary across screens, making it harder for users who rely on consistency.
• Visualizations are interactive but often lack semantic meaning for screen readers or keyboard users.
• Export formats are inconsistent and often unusable for assistive tech.
• “Insight” is layered, filtered, and conditional — not just static text.

So when teams try to apply accessibility after building all of that complexity, they run into a wall.

Because accessibility isn’t about fixing pixels. It’s about how the system is built, from platform to implementation.

Where Retrofits Fall Apart

Retrofits almost always start the same way:

A deadline. A compliance check. A VPAT request. A procurement blocker.

Then the scramble begins — last-minute fixes, workarounds, and tech debt:

• ARIA labels added to buttons that were never meant to be labeled.
• Keyboard support patched into unfocusable components.
• Alerts hacked in with assumptions about visual hierarchy.
• Automated tests passed — but real usability still broken.

This isn't just inefficient — it's unsustainable, because every patch introduces risk. Every fix requires rework. And worst of all, the product remains fragile and incomplete for the people who need it most.

Accessibility Is a Product Capability, Not a Feature

You can’t meaningfully support accessibility in analytics if you treat it like a UI problem.

You have to treat it like a capability — like performance, like scalability, like governance

That means building accessibility into every layer of the product,  not just the UI:

• Design systems with semantic components, color contrast rules, and keyboard logic by default.
• Engineering workflows with accessibility tests, predictable focus behavior, and preview environments for assistive tech.
• UX processes that design for screen reader flows, dialog consistency, and error messaging that actually works.
• Roadmaps that treat accessibility like performance or scalability — a quality standard, not an afterthought.

Accessibility Debt Is Real — and it Compounds

Accessibility debt doesn’t show up in logs.

It shows up in the user experience — and in support tickets from people trying to do basic things like apply a filter, navigate a dashboard, or export a report.

And the longer you defer fixing it, the harder it gets.

Because accessibility debt, like technical debt, compounds:

• Small inconsistencies in interaction patterns confuse users.
• Reused components without roles break entire workflows.
• Workarounds create exceptions, not systems.

The fix?

Don’t bolt accessibility on.

Build it in.

What Accessibility Looks Like at GoodData

At GoodData, accessibility is embedded in how we design, develop, and scale our analytics platform.

It’s not a layer we apply, it’s a standard we build against.

Here’s what we prioritize across the product:

• Component-level accessibility: Every interactive element is built with accessibility in mind — from semantic structure and keyboard support to ARIA attributes — starting at the design system level.
• Structured, predictable navigation: Users can move through dashboards, filters, and menus with clarity — whether using a keyboard, assistive tech, or screen reader.
• Live feedback that works for everyone: Dynamic updates (filters, errors, confirmations) are clearly communicated, without disrupting context.
• Accessible exports: PDF reports support structure and formatting aligned with PDF/USA standards, enabling customers to meet best practices for assistive technology.
• Inclusive testing: Every release goes through manual QA, including screen reader and keyboard-only testing, to ensure accessibility holds up in real-world use cases.

Where We Continue to Invest

Accessibility isn’t a milestone. It’s a core part of maintaining product quality — just like performance or reliability.

We continue to evolve and refine accessibility across the platform, with focused attention on:

• Making charts more usable for assistive technologies, with clearer summaries and better interaction support.
• Refining high-interaction areas to reduce friction for users with cognitive and motor impairments.
• Improving focus management and navigation patterns across complex dashboard workflows.

These aren’t feature flags or isolated improvements.

They’re part of how we keep accessibility aligned with product evolution and user expectations.

Final Thought

If you’re building analytics products, you’re building tools that drive decisions.

Decisions that affect people, products, revenue, operations.

That means the people using your platform — all of them — need to understand the data in front of them, navigate it confidently, and act on it without friction or frustration.

Accessibility is how you make that possible.

Not because of a deadline.

Not because of a regulation.

But because it’s the right way to build.

At GoodData, we’re not retrofitting accessibility.

We’re treating it like what it is: a capability that makes analytics better. For everyone.