Industry

Banking

Client

Lakeside

Services

Product Design

Date

August 2025

Turning arbitrary data into actionable next steps

Turning arbitrary data into actionable next steps

Lakeside Software builds tools that help large organizations detect and resolve IT issues.

Much like a car dashboard, Lakeside used 1,000s of sensors to give IT teams deep visibility into their EUC environments and surface system signals such as high CPU usage, application crashes, and disk failures.

This helps detect and resolve IT issues across thousands of employee devices — reducing downtime and help desk volume.

While some sensors identify root causes, many only surface symptoms — leaving IT specialists to interpret fragmented data and determine what actions to take.

Over a three-month engagement, our team redesigned the experience to transform overwhelming telemetry into a guided, trustworthy workflow that supports faster decision-making and clearer outcomes.

Despite its powerful data engine, the product experience worked against its users.

Internally, teams referred to the interface as a “data dumpster.” Instead of enabling proactive IT, it slowed teams down and fragmented decision-making.This caused:

Internally, teams referred to the interface as a “data dumpster.” Instead of enabling proactive IT, it slowed teams down and fragmented decision-making.This caused:

Internally, teams referred to the interface as a “data dumpster.” Instead of enabling proactive IT, it slowed teams down and fragmented decision-making.This caused:

Wavering trust in accuracy: limited transparency into how health scores were calculated reduced confidence in the system.

Declining ROI for customers which contributed to churn among long-term clients.

23 generative and formative interviews with highly technical IT specialists were conducted to ground the redesign in real workflows.

23 generative and formative interviews with highly technical IT specialists were conducted to ground the redesign in real workflows.

3 things were clear:

3 things were clear:

Complex data needs to be meaningful, not just visible.

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Lakeside needed to shift from a toolbox of features to a guided, actionable workflow.

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Building transparency into the Health Score was vital to customer trust.

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Make complex data meaningful

Make complex data meaningful

During user testing, we found that users could only see a high-level summary across the entire organization (e.g., “15,000 devices experiencing high CPU”), or a single device view deep inside the Resolve tool.

During user testing, we found that users could only see a high-level summary across the entire organization (e.g., “15,000 devices experiencing high CPU”), or a single device view deep inside the Resolve tool.

During user testing, we found that users could only see a high-level summary across the entire organization (e.g., “15,000 devices experiencing high CPU”), or a single device view deep inside the Resolve tool.

What was missing was a mid-level view — the ability to see impacted devices as a group, identify patterns, and understand likely causes without investigating machines one by one.

We redesigned the mid-level data layer that clustered affected devices by shared attributes and behaviors. Instead of “1,000 devices with high CPU,” users could now see patterns like devices affected by a specific background process or clear employee actions tied to each cluster.

We redesigned the mid-level data layer that clustered affected devices by shared attributes and behaviors. Instead of “1,000 devices with high CPU,” users could now see patterns like devices affected by a specific background process or clear employee actions tied to each cluster.

We redesigned the mid-level data layer that clustered affected devices by shared attributes and behaviors. Instead of “1,000 devices with high CPU,” users could now see patterns like devices affected by a specific background process or clear employee actions tied to each cluster.

We redesigned the mid-level data layer that clustered affected devices by shared attributes and behaviors. Instead of “1,000 devices with high CPU,” users could now see patterns like devices affected by a specific background process or clear employee actions tied to each cluster.

We redesigned the mid-level data layer that clustered affected devices by shared attributes and behaviors. Instead of “1,000 devices with high CPU,” users could now see patterns like devices affected by a specific background process or clear employee actions tied to each cluster.

We redesigned the mid-level data layer that clustered affected devices by shared attributes and behaviors. Instead of “1,000 devices with high CPU,” users could now see patterns like devices affected by a specific background process or clear employee actions tied to each cluster.

From toolbox to guided workflow

From toolbox to guided workflow

The product was organized as a collection of standalone tools rather than around user goals. Users had to interpret which tool to use, decide what actions to take and track progress manually.
Advanced users could tolerate this model, but less experienced or time-constrained users became overwhelmed and frequently lost context by jumping between tools.

The product was organized as a collection of standalone tools rather than around user goals. Users had to interpret which tool to use, decide what actions to take and track progress manually.
Advanced users could tolerate this model, but less experienced or time-constrained users became overwhelmed and frequently lost context by jumping between tools.

The product was organized as a collection of standalone tools rather than around user goals. Users had to interpret which tool to use, decide what actions to take and track progress manually.
Advanced users could tolerate this model, but less experienced or time-constrained users became overwhelmed and frequently lost context by jumping between tools.

Rather than oversimplifying the domain, we designed a guided workflow layer that preserved flexibility while providing structure. This created continuity across complex investigations without constraining expert workflows.

Building trust in the Health Score

Building trust in the Health Score

The Health Score strongly influenced how IT teams prioritized work, yet many users did not trust it. Changes to the scoring model often felt opaque and disconnected from real-world impact.
Users wanted visibility into what influenced the score, confidence that actions meaningfully affected outcomes and the ability to tailor what “healthy” meant for their environment.

The Health Score strongly influenced how IT teams prioritized work, yet many users did not trust it. Changes to the scoring model often felt opaque and disconnected from real-world impact.
Users wanted visibility into what influenced the score, confidence that actions meaningfully affected outcomes and the ability to tailor what “healthy” meant for their environment.

The Health Score strongly influenced how IT teams prioritized work, yet many users did not trust it. Changes to the scoring model often felt opaque and disconnected from real-world impact.
Users wanted visibility into what influenced the score, confidence that actions meaningfully affected outcomes and the ability to tailor what “healthy” meant for their environment.

After many iterations we redesigned the Health Score experience to emphasize clarity, transparency, and control. This reframed the score from a mysterious metric into a decision-support tool users could trust and understand.

What We Included

01

Clarity

Clear breakdowns factors affecting the health score

02

Visibility

Visibility into how actions impacted the score over time

03

Future customization

Foundations for future customization and user-defined thresholds

In three months, the Method team transformed a fragmented, data-heavy product into a guided, insight-driven platform.

In three months, the Method team transformed a fragmented, data-heavy product into a guided, insight-driven platform.

By introducing mid-level data visibility, designing workflows around user goals instead of tools and making system health transparent and trustworthy we reduced cognitive load, improved decision clarity, and rebuilt confidence in the platform’s value.

The redesign positioned Lakeside to retain enterprise customers, modernize their product experience, and support more proactive IT operations at scale.