Data Marketplace · Halliburton

01 — Problem & Context

Fragmented data across the organization

Halliburton's geoscience teams relied on scattered internal systems to find, request, and access critical data sets. There was no unified platform — engineers spent significant time locating data instead of using it.

The goal: design a centralized marketplace where teams can discover, preview, and request access to internal data products — with clear ownership, metadata, and governance built in.

02 — Process

From discovery to delivery

1.0

Stakeholder Interviews

Mapped pain points across data producers, consumers, and governance teams.

Interview synthesis

2.0

Information Architecture

Defined data taxonomy, navigation model, and search patterns.

IA documentation

3.0

Wireframes & Flows

Core user journeys — browse, search, preview, request access, manage datasets.

Lo-fi prototype

4.0

Usability Testing

Validated flows with internal users across multiple business units.

Usability report

5.0

Hi-fi Design

Final screens following corporate design system with custom components.

Hi-fi prototype

6.0

Hand-off

Developer specs, component documentation, interaction guidelines.

Figma handoff

03 — UX Strategy

Phase 1 — MVP

UX strategy is a long-term plan that connects user insights, product vision, and business objectives into a coherent direction for designing and improving the user experience.

Vision	Provide a user-friendly means of accessing curated, trusted data, reports, models, and AI Agents that are a part of Halliburton's Data Ribbon.
Problem	Users cannot efficiently work with data because it is difficult to find, difficult to access, and not easy to compare across multiple sources.
Success looks like	Data engineers use the dashboard daily as the primary entry point to find, validate, request access, and consume data assets within minutes — not days or weeks.

Persona groups

G1

Data Scientist / Engineer

Browse data products, fast and relevant search, preview data, request & obtain access quickly.

G2

Business User

Find existing reports (PBI, Tableau, Spotfire, Databricks) that answer questions from trusted data.

G3

Data Product Owner

Manage data product info, approve/reject accesses, define level of access.

G4

Data POD Lead

Oversee data products across teams, monitor quality and access patterns.

UX Goals

Validate whether users can find the right Data Product quickly
Check if users understand Data Product cards and details
Evaluate how easily users can request access
Validate clarity of access statuses and permissions
Test understanding of View vs Edit access levels
Validate that product owners can easily add existing products

Core user scenarios

Search and discover relevant data products
Filter and narrow down data products
Open dataset detail page and understand if it's usable
Request and get 1-click access directly from dataset page
Track access request status
Compare data sets
Add and manage data products
Manage access levels

UX Principles

Reduce user's cognitive load
Clarity and Simplicity
Visual Hierarchy
Consistency
Feedback and Interactivity
Efficiency and Speed
Accessibility and Readability
Desktop first

UX KPIs & Business GoalsNDA

Efficiency	Search time: from weeks to under 5 minutes. Access request time: from months to under N days.
Adoption	% of engineers actively using the marketplace. % of data products onboarded.
Quality	User satisfaction score (CSAT). % of users who find the right product on first search.
Business	1) Reduce search time. 2) Reduce access approval time. 3) Increase trust. 4) Support all user journeys.

Protected under NDA

Roadmap — 7 phasesNDA

Phase 1	Discovery — UX strategy, Data architecture map
Phase 2	User research — Personas, UJM, User flows, JTBD
Phase 3	Market analysis — Competitor analysis
Phase 4	Prototyping — Wireframes, Mockups, Design system
Phase 5	Validation — Usability testing (2 rounds)
Phase 6	Design hand-off & dev collaboration
Phase 7	Post launch research & improvements

Protected under NDA

Key deliverablesNDA

Protected under NDA

04 — UX Strategy

Phase 2 — Trust, Social & AI-Powered

Phase 2 evolved the marketplace from a browsing tool into an intelligent, trust-driven data discovery platform powered by AI.

Vision	Transform the Data Marketplace from a browsing tool into an intelligent, trust-driven data discovery platform powered by AI.
Problem	Users can find data but cannot judge if it's reliable, relevant, or safe to use. Discovery is manual, trust signals are absent, and social knowledge is invisible.
Success looks like	Engineers and business users describe what they need in plain language, receive ranked trusted results in seconds, and make access decisions confidently.

Structural changes

Phase 1 → 2

Main page becomes Explore page (browse + filters, unchanged). New Main page = AI-powered Home page with large search bar + 6 personalized recommendation boxes. Intent-first entry point.

UX Goals

Reduce time to find the right data product from weeks to minutes
Give users confidence to trust data before requesting access
Make approval decisions faster for owners
Surface peer knowledge that currently lives only in people's heads
Support both technical (G1) and non-technical (G2) user journeys

AI-powered featuresNDA

AI Home	Natural language input — user describes what they need. AI response + ranked dataset cards.
Recommendations	6 personalized boxes: Continue exploring, Most trusted, New since last visit, Pending action, Colleagues using, For you.
AI Ranking	Relevance + Quality score + Certification status + Access availability

Protected under NDA

Feature clustersNDA

Trust	Quality score, Certified badges, Lineage visualization, Approval workflow
Social	Reviews & Usage tab, Peer recommendations, Owner reputation
Governance	Sensitivity classification, auto-tagging, Adoption analytics
AI Discovery	NL search, Hybrid AI response, Personalized recommendations
Billing	Subscription model, Plan comparison, Transparent pricing

Protected under NDA

Success metricsNDA

Efficiency	Search-to-access conversion ↑, time to find dataset ↓, approval time ↓
Adoption	% engineers using marketplace ↑, certified vs uncertified ↑
Trust	Quality badge CTR ↑, preview-to-request conversion ↑
Social	Reviews per dataset ↑, peer recommendation CTR ↑
Billing	Plan upgrade rate ↑, checkout completion ↑, cart abandonment ↓

Protected under NDA

05 — Wireframes

Defining the structure

Lo-fi wireframes defined the core layout before any visual design decisions were made.

Dashboard — search and filter flow

Metadata overview, preview, and access request

Request flow with justification and routing

Data owner view — manage and monitor

Protected under NDA

06 — Mockups

High-fidelity screens

Final designs built on top of the corporate design system with custom marketplace components.

AI-powered home — search and recommendations

Browse with filters, cards, certification badges

Data catalog — detail view

In-app request with approval status

Protected under NDA

07 — Outcome

Shipped to internal teams

The Data Marketplace was delivered to Halliburton's internal engineering teams in 2026. The platform unified data discovery across the organization and introduced a governed access request workflow for the first time.

Specific metrics and adoption data are confidential under NDA.

08 — Reflection

Looking back

What I'd do differently

Involve data governance stakeholders earlier in the process. Their requirements surfaced late and required reworking the access request flow mid-project.

What I learned

Enterprise data products need trust-building at every layer — from metadata quality to access transparency. Users won't adopt a tool if they can't trust the data inside it.