Product Manager - Data Product Hub (IBM)

As a Product Manager, I work on IBM's Data Product Hub - a governed marketplace that accelerates AI adoption by making enterprise data discoverable, trusted, and ready to use.

Highlights: Year: 2023 - Present

  • Owned GTM & Pricing Strategy: Defined SaaS motions, freemium models, and roadmap features to drive adoption and measurable growth.

  • Instrumented Product Success: Built metrics and analytics to track usage, trial-to-purchase conversion, and feature engagement.

  • Amplified Voice of Customer: Engaged enterprise customers across 10+ countries, turning feedback into actionable feature improvements.

  • Shaped Product Roadmap: Prioritized features and guided releases from MVP → MLP, balancing business impact, governance, and customer needs.

The Problem

Enterprise teams struggled to share and reuse data safely.

Fragmented catalogs, unclear ownership, and heavy governance overhead created friction, slowed AI initiatives, and blocked analytics.

My Approach to the Solution

  • I facilitated cross-functional workshops and regular stakeholder syncs to align priorities across product, engineering, sales, and field teams.

    Leveraged impact vs. effort matrices and RICE scoring to enable data-driven roadmap decisions.

    Created clear decision frameworks (build vs buy, time to value, total cost of ownership) that balanced governance requirements with business objectives, ensuring alignment and buy-in across the organization.

  • Engaged directly with enterprise customers through structured discovery sessions, guided demos, and prototype walkthroughs.

    Collected feedback through interviews, usage telemetry, and surveys, then iterated solutions to address friction points.

    Turned skeptical users into advocates by showing measurable ROI, incorporating their input into the product, and maintaining regular follow-up.

  • Analyzed patterns in usage telemetry, support tickets, and feature requests by meeting enterprise customers across Canada, US, UK, Ireland, Italy, Germany, Australia, Middle East, New Zealand, Singapore, and Malaysia.

    Synthesized insights into user journey maps, personas, and prioritization frameworks, which guided feature design and marketplace evolution.

    Partnered with UX and analytics teams to continuously validate assumptions with data, ensuring that the platform met diverse regional requirements and workflows.

  • Defined the platform ecosystem to createa MVP → MLP (Minimum Lovable Product) journey.

    Organized work into sprints and iterative releases, monitored engagement via product metrics, integrated guided learning paths and interactive demos, and used agile, OKRs, and product-led growth success metrics (north star) to validate assumptions and ensure timely delivery at scale.

Want to see some of my creations?

Solution Brief
Product E-Book
My Demos (Data Sharing)
Product Vision

The Result

A successful launch and adoption of a governed data marketplace where teams can:

Publish and manage data products with clear ownership

Discover trusted data through rich metadata and collaboration

Request and manage access with built-in governance.

Screenshot of a web page titled 'Data Product Hub' featuring a search bar, guided journey link, and three data product categories: Insurance Prospective, Mortgage Analysis, and Sales and Marketing, each with descriptions and subscribe links.

Key Takeaways

  • A 3D digital illustration of stacked cardboard boxes covered in binary code, representing data storage or digital information, with a pattern of 0s and 1s on the boxes.

    Governance is a growth enabler

    Embedded governance scales trust across teams, regions, and workflows, accelerating adoption without slowing innovation.

  • Colorful globe of Africa and parts of Europe and Asia in a dimly lit room.

    Customer insights drive evolution

    Global engagement informs roadmap and feature design, ensuring product decisions reflect real-world workflows and regional nuances.

  • Close-up of a computer screen displaying colorful lines of code on a dark background, with focus on programming functions in JavaScript.

    Iterative execution maximizes impact

    MVP → MLP releases validate assumptions, capture early feedback, and balance enterprise readiness with user delight.