Autonomous Data Platform (ADP)

Problem / Opportunity

As enterprise platforms increasingly rely on AI agents and automation, organizations face a critical challenge: users must trust automated decisions before they are willing to grant those systems greater autonomy.

The Autonomous Data Platform initiative focused on creating an AI-powered operating model where autonomous actions are visible, explainable, and actionable. The opportunity was to help customers transition from manual operations to AI-assisted and eventually autonomous workflows while maintaining transparency, governance, and human oversight.

My Role

I was responsible for defining the experience architecture and interaction patterns that became the foundation of the Autonomous Data Platform.

Key responsibilities included:

• Designing user experiences across multiple autonomous product domains

• Creating trust-building interaction frameworks

• Developing approval, recommendation, and confirmation workflows

• Establishing consistent AI interaction patterns across products

• Driving alignment between product strategy, engineering, and UX

• Translating abstract AI concepts into practical administrative workflows

My work helped shape how customers understand, monitor, and control autonomous decision-making within the platform.

Output

Designed core platform experiences including:

Autonomous Cloud Compute Management

• Recommendation-driven compute optimization workflows

• Historical-context insights

• Human-in-the-loop approval mechanisms

• Autonomous action confirmations

Intelligent Query Optimization

• Query analysis and optimization recommendations

• Before-and-after performance comparisons

• AI-assisted tuning workflows

Cost Intelligence & FinOps Automation

• Cost and consumption dashboards

• Units-to-cost visualization controls

• Budget guardrails and alerts

• Usage intelligence experiences

Governance & Trust Systems

• Agent transparency feeds

• Notification center

• Governance dashboards

• Explainability patterns

• Trust-stage controls (Observe → Assist → Autonomous)

Natural Language Experiences

• Cross-platform command bar

• Conversational administration workflows

• Platform-wide discovery and recommendations

A major outcome of the project was establishing a reusable interaction model:

Alert → Recommendation → Context → Action → Confirmation

This pattern became a foundational design framework across multiple autonomous capabilities.

Customer Impact

• Increased visibility into AI-driven system actions

• Reduced uncertainty around automation through explainability and approval workflows

• Created transparent pathways toward greater autonomous operation while preserving user control

• Improved discoverability and understanding of platform recommendations

Business Impact

• Established foundational UX patterns for Teradata's autonomous platform strategy

• Reduced design fragmentation through reusable experiences shared across epics and product areas

• Positioned the platform to support multiple personas, including administrators, FinOps leaders, data stewards, and business users

Success Indicators

• Unified autonomous interaction framework adopted across multiple epics

• Creation of transparency, governance, and explainability patterns

• Successful integration of monitoring, optimization, governance, and FinOps experiences into a cohesive platform vision

• Alignment of autonomous capabilities around trust-first design principles

Final Result

The Autonomous Data Platform established a new model for enterprise AI experiences by making autonomous actions understandable, transparent, and user-controlled. Rather than treating AI as a black box, the platform empowers customers to observe recommendations, understand reasoning, and progressively increase automation with confidence.