COMPANY

Hewlett Packard Enterprise

TOOLS

Figma, Jira, Confluence

Google Sheet,

TIMELINE

~11 Weeks

PRODUCT

DSCC's - Data Ops Manager & Block Storage

ROLE

Lead Product Designer. I was responsible for the entire design lifecycle, from discovery and strategy to final UI/UX, stakeholder management, and collaborating with cross-functional teams.

PLATFORM

Web

PRODUCT OVERVIEW

HPE Data Services Cloud Console (DSCC)

It is a cloud-based management platform that provides a unified, SaaS-driven control plane for hybrid cloud storage. It simplifies how organizations deploy, manage, and monitor storage infrastructure across on-premises and cloud environments. With DSCC, IT teams can automate provisioning, gain real-time insights, and centrally enforce data policies — all through a single, intuitive interface that delivers a true cloud experience for storage operations.

HPE Data Ops Manager (DOM)

It is a key application within DSCC, focuses on optimizing data operations by providing deep visibility, analytics, and intelligent automation. It helps users monitor system performance, understand capacity trends, and manage data protection and workload placement efficiently. Together, DSCC and Data Ops Manager enable seamless, intelligent, and scalable management of enterprise data from edge to cloud.

PROBLEM STATEMENT

As a designer working on the HPE's Data Ops Manager product, I was leading the design strategy by aligning the feature requests with users expectations and designing for the following problem space:

Problem:

Cloud storage customers face increasing ransomware threats, leading to data loss and operational downtime. Manual remediation is slow and complex. An AI Chatbot in needed to identify and access the security vulnerabilities for the security admins and empower them to perform quick remediation actions as they can manage the security posture of the entire cloud storage fleet efficiently.

SOLUTION

Solution:

Designed Genie: AI chatbot to automate and simplify ransomware detection and remediation, providing real-time recommendations and data recovery options. Redesigned the Insight Card within DOM dashboard to provide a quick overview of the security posture of the entire cloud storage fleet.

OUTCOME

how you got this outcome? how do you know ?

USER IMPACT:

Shipped a feature that reduced time-to-remediation by 70% and improved customer confidence.

Decreased support tickets by 45%. Reduced workload on security teams, enabling focus on strategic initiatives.

90% positive feedback from beta users on the ease of use.

BUSINESS IMPACT:

Enhanced Cloud Security Credibility in the industry and market.

Competitive Advantage: Positions the cloud storage product as a security-first, user-friendly platform—helping attract and retain more enterprise customers.

Gave our sales team a new, powerful feature to differentiate the product in a crowded market.

INITIAL RESEARCH & DISCOVERY

Feature as per Product Requirement Doc (PRD):

'Genie- an AI-powered chatbot' a new feature that needs to be designed and integrated into our DSCC's Data Ops Management Dashboard. Genie aims to empower cloud security administrators to rapidly detect, triage, and remediate ransomware threats within their cloud storage systems, minimizing downtime and data loss.

User Research:

To understand the problem in detail, I conducted interviews with cloud admins, IT security managers, and customer support representatives to analyze their pain points, workflows, and current challenges with security threats.

Interview Key Highlights :

Current ransomware identification and remediation process takes hours if not days.

Ransomware Threats gets lost in long lists of notifications. Critical info is buried within flows.

Comprehending the threat scope is cumbersome.

Manual search and scan of the last good know snapshot takes forever.

Compilation of Report in addition to other steps is crucial in the recovery and remediation process.

What Current Manual process looks like?

Current Dashboard Experience

Issues Card

Missing Insight information at the Dashboard level.
Issue card is too generic and needs categorization since forever.

Current Insight too hidden

To access critical health insights, navigate to the individual system's detail page and select the "Insight" tab. It is limiting.

DEFINING & ALIGNING THE PRODUCT

Refined Requirements (PRD) & Setting the Product Strategy :

Collaborated closely with the Product and Engineering stakeholders to critically review and refine the initial PRD.

My goal was to transform high-level objectives into concrete, actionable requirements so that would direct the design process and align the entire cross-functional team on a shared vision.

Decide on a narrow, prioritized list to tackle for our minimum viable product (MVP) for the first phase.

Leveraged 'Why-How-What' statements to ensure each requirement directly linked back to a user problem and a clear business value.

Original Version: 'AI chatbot will detect ransomware.'

Revised Requirement Version :

'The system shall automatically detect known and suspected ransomware activities in cloud storage [WHAT], so that Cloud Security Admins can immediately assess threats [WHY], enabling rapid response to protect critical data [HOW].'

IDEATION :

I established the Information Architecture, providing a foundational blueprint for the project's content and structure. This process engaged stakeholders from the outset, ensuring their perspectives were incorporated, which resulted in a collaborative and transparent design cycle and a stronger working relationship.

INFORMATION ARCHITECTURE :

DESIGN EXPLORATION

Following the information architecture phase, I explored and presented two design options to stakeholders. This provided a clear comparison of approaches, leading to a confident final decision that supports user and business goals.

Exploration 1:

👍🏼 Insight Card Implementation: The dashboard's dedicated 'Insight' card, featuring critical health information segmented into Performance, Capacity, Protection, and Hardware, has received strong positive feedback from stakeholders.

👎🏼 For enhanced fleet health monitoring, additional key indicators should be integrated. It was also noted that the current placement of "Genie AI" could be refined to better communicate that its function for the MVP is limited to the Insights section, rather than suggesting a global application.

Exploration 2:

👍🏼 Stakeholders provided overwhelmingly positive feedback on the health indicators presentation. The clear, segmented view of critical health information, including incident data for Performance, Capacity, Protection, and Hardware, was particularly valued for its clarity and insight.

👍🏼 The integration of Genie AI into the Fleet Health Assessment card has been positively received.

👎🏼 The naming of the Fleet Health Assessment requires further consideration. Additionally, a visual reorganization of the layout is needed to enhance clarity.

FINAL DESIGN VERSION

The final design was developed by synthesizing the most effective elements from the initial design options and incorporating positive feedback from stakeholders.

✨ Final Design:

HIGH FIDELITY DESIGN

Designed the final UI/UX, including a dashboard with real-time threat visualizations and an embedded chat window.

PROTOTYPE

SUMMARY & RETROSPECTIVE :

Overall, I really enjoyed designing the Solution Builder and am proud to have successfully shipped the Beta version. I enjoyed contributing to the end-to-end design process, learning more about our users and their needs for the network design space, and collaborating with product, engineering, and other stakeholders.

Measuring Success and Impact

GenAI feature has reduced time-to-remediation by 70% and improved customer confidence.

HPE stakeholders are highly enthusiastic about this project. Genie AI is shaping up to be the first AI chat interface across DSCC services portfolio, this new offering is already leading the way.

Currently collecting customer feedback and satisfaction through a CSAT feedback survey. We are also currently measuring and analyzing adoption, conversion, drop-off, and time on pages.

Lessons Learned

Early and frequent stakeholder buy-in is critical for a fast-moving project.

Simplification is the key to designing for complex topics like cybersecurity; it builds trust and encourages user adoption.

A strong design-led strategy can unite a cross-functional team and create a shared vision for success.

Challenges

Technical Complexity: The security and remediation process involves highly technical jargon (e.g., "remediate," "snapshotting," "quarantining").
Used plain language and provided inline tooltips for technical terms.

Tight Timeline: The product had to be shipped in a tight timeline to capitalize on a market opportunity.
I focused on the core user flow, prioritizing the most critical features for the MVP.

We adopted a "ship and iterate" mindset, pushing non-essential features to a later release.

Too frequent requirements changes initially by product team that led to multiple rounds of negotiations.

Next Steps

For the next steps, the Genie AI chatbot project will continue to have phased releases to onboard more HPE products and service offerings. We are continuing to explore adding visualization of critical Capacity savings data to allow users to efficiently manage their systems.