Cross-Product Unification

The final blueprints were written as reusable, experience-level guidance that teams can apply directly - complete with step-by-step logic, examples, and clear recommendations aligned with SUI and Magnetic.You can find them published on the Splunk UI Design system here.

I unified Splunk’s fragmented workflows by auditing product flows, identifying common patterns, and mapping the essential steps behind high-risk actions. This became a set of reusable system-level Blueprints that teams rely on to build consistent, scalable, accessible workflows.

Shifted the design system to experience-focused by creating reusable CRUD workflow blueprints that give teams a shared foundation for consistent, intentional experiences.

Reusable Workflows

Evolved the system from components to experiences, unifying product workflows

I unified Splunk’s fragmented workflows by auditing real product flows, identifying cross-product patterns, and mapping the essential steps behind high-risk actions. This became a set of reusable system-level Blueprints that teams rely on to build consistent, scalable, accessible workflows.

Overview

The problem

Across Splunk products, core workflows were being designed differently by every product team. Without shared standards or end-to-end guidance, designers and engineers were reinventing patterns, making inconsistent decisions, and spending extra cycles clarifying behavior and alignment with the Splunk design system team. This lack of unified workflow guidance slowed teams down, created experience fragmentation, and made it hard to maintain quality across Security, Observability, and Data Management products.

The solution

We expanded Splunk UI design system from a component-focused system into an experience-focused one by developing reusable workflow blueprints for Create, Read, Update, and Delete flows. These blueprints define the core steps, decision points, and interaction patterns teams should follow, giving product groups across Splunk a shared foundation for building cohesive, intentional experiences rooted in real workflows.

Process

Auditing flows across product teams like Security, Observability, and Data Management revealed that each team approached Common actions such as Create, Update, and Delete differently—using different steps, confirmation patterns, terminology, and levels of risk communication. This fragmentation made it hard to maintain predictable experiences across Splunk.

Analyzed product inconsistencies

Clear, verb-led triggers: Strong “Add provider” action placed near related content, making the entry point obvious and contextual.

Step-by-step structure: The complex flow was broken into guided steps with a Step Bar, reducing cognitive load

Organized data entry: Inputs are grouped logically, labels are consistent, and each step only shows what’s needed.

Robust confirmation: A clear summary step, and actionable next steps

Better table feedback: Success banner appears in context, helping users orient themselves and confirm the result.

Before

Product impact

After

Shifted our design system from component-centric to experience-centric, creating the first experience-level guidance in the design system.
Established standardization across Security, Observability, and Data Management, reducing duplicated design work and decision friction.
Increased product consistency across Splunk by standardizing the structure and logic of common enterprise workflows.
Improved cross-team alignment by giving designers, PMs, and engineers a single source of truth for Create, Update, and Delete flows.
Reduced design time and review cycles by clarifying expected steps, required decisions, and standard interaction rules.
Created scalable documentation that can support future workflows beyond CRUD.
Set a foundation for future automation (e.g., AI-assisted blueprint generation, pattern detection, etc.) by defining clear, reusable workflow structures.

Result

Leverage AI to ingest product specs, Figma flows, and component usage to generate first-draft blueprints - dramatically reducing synthesis time and giving teams a structured starting point instead of a blank screen.

AI generated blueprints

Scale

Enable AI to scan shipped designs to surface emerging patterns, spot inconsistencies, and flag deviations from the blueprint — allowing the system to evolve based on real product usage rather than static documentation.

Automated pattern detection & validation

Integrate blueprints directly into Figma or internal tooling so designers receive real-time suggestions (“The flow follows the Update Blueprint”) and automated checks for missing steps, unclear logic, or accessibility gaps.

Blue intelligence inside design tools

Extend the blueprint model to foundational experiences such as forms, onboarding, bulking actions, notifications, and permission flows - shifting Splunk UI from a component system to a comprehensive experience system.

Expanding into broader experience patterns

The problem

Without shared workflow standards, teams kept reinventing patterns - leading to inconsistent decisions, extra alignment work, and fragmented experiences across Splunk products.

The solution

Analyzed product inconsistencies
Discovery sessions + interviews
Mapped user flows
Established guidelines
Validated + shipped

The process

Result

Before

After

Shifted the system from components to experience-level guidance.
Standardized workflows across product areas, reducing duplicate work.
Increased consistency by unifying common enterprise flow patterns.
Improved alignment with a single source of truth for CRUD workflows.
Reduced design time through clear steps and interaction rules.
Created scalable docs for future workflow patterns.
Set the groundwork for future AI-driven blueprint automation.

Result

AI generated blueprints
Automated pattern detection & validation
Blue intelligence inside design tools
Expanding into broader experience patterns

Scale

Next project

CRUD Experience Models

System Level Heuristics

Journey Mapping

Discovery +
interviews

Mapped user flows

Established guidelines

Validated +
shipped

Analyzed product inconsistencies

Different ways to trigger an 'Add' action across products

Different ways to trigger an 'Add' action

Interview protocol + notes

Conversations with designers, PMs, and engineers surfaced shared pain points uncertainty around required steps, confusion about when to use modals vs. pages, inconsistent handling of destructive actions, and a lack of guidance for edge cases.

Ran discovery sessions + Interviews

Survey responses

Interview protocol +
notes
+
Survey responses

Using the patterns we saw, we created must/ consider rules for structure, decision points, error handling accessibility, and interaction consistency. These guidelines clarifies, the expected "shape" of each blueprint and removed ambiguity that previously cause design drift.

Established guidelines

The final blueprints were written as reusable, experience-level guidance that teams can apply directly - complete with step-by-step logic, examples, and clear recommendations aligned with SUI and Magnetic.

You can find them published on the Splunk UI Design system here.

Result

Established guidelines

Unclear triggers: The “create/add” entry point wasn’t clearly labeled, easy to find, or connected to the table it acted on.
High cognitive load: A complex, multi-step setup was shown in a single long form with little structure or guidance.
Inconsistent inputs: Fields weren’t grouped logically, labels varied
Weak confirmation: Success states were minimal, non-persistent, and didn’t summarize what was created or offer next steps.
Context switching: Redirects dropped users back into the table with little feedback or connection to their last action.

Product impact

Nishka.jiandani@gmail.com
Tel. (415) 610 6284

San Francisco, CA

Shifted our design system from component-centric to experience-centric, creating the first experience-level guidance in the design system.

Established standardization across Security, Observability, and Data Management, reducing duplicated design work and decision friction.

Increased product consistency across Splunk by standardizing the structure and logic of common enterprise workflows.

Improved cross-team alignment by giving designers, PMs, and engineers a single source of truth for Create, Update, and Delete flows.

Reduced design time and review cycles by clarifying expected steps, required decisions, and standard interaction rules.

Created scalable documentation that can support future workflows beyond CRUD.

Set a foundation for future automation (e.g., AI-assisted blueprint generation, pattern detection, etc.) by defining clear, reusable workflow structures.

Leverage AI to ingest product specs, Figma flows, and component usage to generate first-draft blueprints - dramatically reducing synthesis time and giving teams a structured starting point instead of a blank screen.

Enable AI to scan shipped designs to surface emerging patterns, spot inconsistencies, and flag deviations from the blueprint — allowing the system to evolve based on real product usage rather than static documentation.

Integrate blueprints directly into Figma or internal tooling so designers receive real-time suggestions (“The flow follows the Update Blueprint”) and automated checks for missing steps, unclear logic, or accessibility gaps.

Extend the blueprint model to foundational experiences such as forms, onboarding, bulking actions, notifications, and permission flows - shifting Splunk UI from a component system to a comprehensive experience system.