IBM — Operations Analytics

A tool for IT operations professionals.

Roles

Design Team Lead
UX Design
Page Copy
Information Architecture

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

Scope Product lead + engineering + researcher support | Sprint-based delivery | Log analysis workflow, scenario-based investigation paths, intervention actions and next-step clarity

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

Image of an empathy map.

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

“It's the little issues that go un-detected that really come back to bite you.”

— Interview subject

Image of a proto-persona

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

“I've got so much going on, if you can help even a little in staying ahead, that would be huge.”

— Interview subject

An image of an As-is scenario flow diagram.

This is an initial as-is flow diagram done for Operations Analytics. We began by looking at a typical data center tech's course of action during their workday.

“Keeping the data center running is Job One.”

— Interview subject

An image of an To-be scenario flow diagram for MVP.

We did a to-be flow for our offering MVP. We knew that the MVP would be less than ideal but would get the tech working in a slightly different way.

An image of an To-be scenario flow diagram for the complete offering.

We also did a to-be flow for our offering in its mature or complete state. We were confident that this offering would change the way such data center techs did their work.

Key decisions

Summary: scenario workflows, actionable signals, sprint validation rhythm

Design the workflow around scenarios, not raw log volume

DecisionStructured the experience around investigation scenarios that move from signal to action

TradeoffDid not expose every log capability in MVP, focused on the most actionable paths

WhyOps users needed speed and credibility under pressure, not a toolbox that required interpretation

ResultEarlier detection and clearer intervention paths validated through scenario flows

Make "what do I do next" unambiguous

DecisionTurned monitoring signals into explicit next steps for investigation and intervention

TradeoffInvested in guidance and workflow clarity over purely visual log exploration

WhyReactive workflows escalate incidents; actionability creates proactive intervention

ResultStakeholders reported clearer paths to act on early signals

Establish a sprint-aligned validation rhythm to protect quality

DecisionCoordinated design delivery and validation each sprint before handoff to engineering

TradeoffLess speculative design, more incremental progress tied to real feedback

WhyComplex domain plus delivery pressure can push unvalidated choices into production

ResultReliable design-to-delivery cadence and grounded decisions across sprints

Outcomes

Summary: earlier detection, more proactive action, validated each sprint

Earlier

Detection

Proactive action

Validated

Each sprint

Time to detect
Baseline: Ops teams were reactive and anomalies surfaced after escalation to incidents
Change: Log analysis made pattern recognition faster and directly actionable
Evidence: As-is vs to-be scenario flows validated with target users, plus MVP-to-mature comparison
Proactive interventions
Baseline: Monitoring was reactive and intervention came after impact
Change: MVP gave administrators a reason to investigate early signals before failures
Evidence: Quote synthesis, scenario validation sessions, and launch stakeholder feedback
Design-to-delivery reliability
Baseline: Delivery pressure in a complex domain risked unvalidated UX decisions reaching engineering
Change: Sprint-aligned design and validation rhythm established across the workstream
Evidence: Backlog refinement cadence with the offering manager plus researcher-supported validation

What changed in the product

Monitoring signals were made actionable, reducing ambiguity at the "what do I do next" moment
The investigation and intervention path was clarified so administrators could act on early signals

How we measured

User interviews and empathy mapping established the as-is workflow. Scenario validation sessions confirmed the to-be model. The MVP launched on IBM Bluemix and later evolved into a key IBM Z-series monitoring tool

Operations Analytics MVP product screens.

This humble application MVP went on to become a key IBM Z-series server monitoring tool.

Get in touch

I'd love the opportunity to discuss how my skills and experience can align with and support your organization's goals.

Contact me