IBM Operations Analytics

My role Design team lead responsible for coordinating sprint design delivery, partnering with the Offering Manager and Engineering to refine the backlog, and supporting UX design and validation through iterative feedback cycles

Challenges and constraints Complex technical domain, high-stakes operational workflows, limited time to go from concept to MVP, and the need to balance speed with credibility for expert IT users

Impact Helped launch an MVP on IBM Bluemix by keeping design and delivery aligned to sprint cadence, validating direction with users, and shaping an experience that enabled earlier detection of anomalies and proactive intervention

Skills demonstrated Design leadership, sprint-based delivery coordination, backlog refinement with stakeholders, UX in high-stakes technical domains, user validation, MVP shaping for credibility and speed

Identifying issues before they become catastrophes.

Operations Analytics helped system administrators detect abnormal behavior in data center logs early enough to prevent outages. Users uploaded log files and used the tool to spot patterns that pointed to failing hardware or performance bottlenecks.

Our primary user was Jim, a system administrator tasked with maintaining seamless operations across the infrastructure.

As Design Lead, I focused on team execution and decision-making, keeping design aligned with sprint delivery and user validation:

• Backlog Management: Partnering with the Offering Manager and Development team to refine and prioritize user stories.
• Sprint Planning and Delivery: Ensuring designs were assigned to the team, completed within the two-week sprint cycle, and delivered on schedule.
• User Validation: Working closely with our user researcher to validate designs with real users and incorporating their feedback into iterative improvements.

An MVP of the product was successfully launched on the IBM Bluemix cloud catalog, providing IT professionals like Jim the tools to safeguard their systems and prevent potential failures

After conducting user interviews, we built empathy maps from which we extracted user insights.

Once we had clear insights and a good understanding of our target user, we created a simple proto-persona.