Role:
Product Strategist, & Product Concept Designer
Date:
Oct 2018
Problem
In 2018, we collaborated with a leading aviation OEM manufacturer whose internal data unit, C3P0 Labs, aimed to leverage Big Data Analytics, Artificial Intelligence, and Machine Learning to enhance airline safety operations through a futuristic data platform.
A major Asian airline was a part of this initiative, with plans to expand to 90+ other airlines, the project faced significant challenges. The primary hurdle was unclear MVP requirements and data governance issues.
How to pick the right MVP to build a data product and scale it to a massive data platform?
Process
The idea was to develop an MVP tailored to the needs of the Asian airline first and then scale the data platform to create a comprehensive data ecosystem for all airline clients. Here’s how I approached this challenge — planned structured collaboration, starting with product workshops and followed by data discovery workshops involving both Safety and Flight Operations departments to gather key insights and process intersections:
Step 1: Hero’s Journey Workshops
Identify the key persona in the process and map out problem space specific process challenges and friction points. Map available data streams. Establish success criteria.
Step 2: Value Matrix Development
Focus on high-impact business use cases. Generate, map and prioritize. Align with stakeholders.
Step 3: Prototyping
First, create critical assumptions for validation. Build quick prototypes to align with stakeholders and adjust based on inputs.
Step 4: MVP Carving
Choose high-value features from the prototyping activity. Validate viability and feasibility from the data stream mapping. Estimate development needs for the MVP.
Step 5: Establish Key Metrics
Key dimensions we looked at are safety benchmarking and improvements, efficiency gains over previous process, and engagement metrics.
Step 6: Strategic Roadmap
Key highlights were: Planned value releases after synthesizing feedback, strategic enhancements at critical milestones, and multi-airline expansion.
Product
Stakeholder workshops revealed pain points such as a lack of actionable insights and difficulty in navigating disparate data sources. The product design began with detailed persona creation, focusing on the operational and analytical challenges of safety officers and flight operations managers.
The platform's navigation was structured around three primary tasks:
Monitoring safety metrics (e.g., G-force predictions).
Conducting in-depth data analysis with chatbot/LLM assistance.
Data monetization with the marketplace to extend value delivery.
Wireframes of the dashboard were iteratively tested with users. The platform adhered to modern design principles, incorporating clean lines, intuitive icons, and responsive elements for cross-device compatibility.
The design of the data platform was meticulously iterative. The key feature for the MVP was to solve the "hard landings" challenge for the airline (which was incurring millions of dollars of cost). Leveraging a vast network of data sources—including sensor data from aircraft engines, weather patterns, historical landing data, location-specific variables, and airport-specific metrics—the platform reimagines how data can be used to predict, analyze, and prevent hard landings.
Product
Here’s the breakdown of the product design vision, aligned with some of the UI screens provided below. This is not the MVP. This is the ultimate product vision to secure funding for the MVP version and beyond.
Real-Time Analytics Dashboard
Provides comprehensive G-force predictions using sensor and external data, with visualizations like heatmaps and trend graphs. The intuitive design ensures quick identification of high-risk situations for operational teams.AI-Assisted Chatbot (LLM Integration)
Simplifies navigation and analytics with natural language queries, helping users identify risk factors or data trends effortlessly. The conversational interface bridges technical gaps, enhancing usability.Data Marketplace
Enables monetization by allowing third-party developers to build and sell analytical data products. Organized screens, search functionality, and privacy compliance promote seamless collaboration and informed decisions.Predictive Insights Module
Delivers actionable alerts and mitigation strategies for potential hard landings, integrating recommendations directly into user workflows for proactive decision-making.Cross-Source Data Integration
Aggregates data from diverse sources like sensors, weather, and airports, providing clear, layered visualizations that simplify complex datasets for holistic analysis.Customizable User Interface
Offers drag-and-drop widgets, custom themes, and role-based layouts, empowering users to tailor dashboards for greater efficiency and satisfaction.
Result
The deployment of the MVP had the following yield:
Enhanced Safety Performance: By providing real-time monitoring and predictive insights, airlines could proactively address safety issues, potentially reducing hard landing incidents by up to 23%.
Operational Efficiency Gains: Predictive maintenance and streamlined operations could lead to a 4% reduction in unplanned downtime, resulting in substantial cost savings.
High User Adoption: With an intuitive interface and tools that directly addressed user needs, we anticipated high engagement rates, with over 90% of targeted users adopting the platform within the first six months.
Reflection
Reflecting on past projects, I see two perspectives: the successes that make me happy and the insights I've gained now that could have enhanced my approach in the past.
Some things that I'm happy about:
This is one of the most iconic manufacturing juggernaut brand in the world. I'm grateful that I got to work on building data platform strategy and long-term product vision. Personally, this project underscored the importance of persona development, human-centered design and iterative development with quick feedback. Post-launch, integrating advanced analytics like prescriptive analytics could further enhance decision-making capabilities for users.
Here's how I'd have done it differently today:
From a product manager's viewpoint, clearer initial alignment on data governance could have accelerated the project. Establishing data ownership agreements earlier would have streamlined development and built greater trust among stakeholders. Additionally, exploring partnerships with other technology providers might have introduced innovative features more rapidly. There were strict limitations but in hindsight I could’ve creatively solved it better today.
*LLM Disclaimer: In 2018—the pre-LLM era—"chatbot" and "conversational UI" were the dominant terms used to describe human-machine natural language interactions. I've used "LLM" in the title since the core principles of interface design remain largely unchanged. Whether we're invoking a bot, LLM, or AI agent during a task, the fundamental interaction patterns stay consistent.
Creative Confidentiality: In the spirit of professional discretion and digital camouflage, some client identities have been subtly transformed into their alter-ego personas. This ninja-like name-swapping applies exclusively to projects completed as an external design mercenary. For all other showcased works, brand and product names remain true to their original, registered identities.
Authenticity Stamp: Every pixel, wireframe, approach, and design concept you'll discover here is 100% crafted by the hands (and occasionally bulletproof caffeinated brain) of Naren Katakam. No design outsourcing, no smoke and mirrors—just pure, unadulterated creative craftsmanship.