Navigation

Redesign

lead product designer

data platform linking the built world to the connected world

Problem

The information architecture has many layers. The depth of the IA forces users to navigate to a building before they can access datasets.

Once at the building level, users navigate via the Onboarding Guide, a proof of concept feature intended to be a one-time. Once onboarding completed, users established a habit of finding data through the Onboarding Guide navigation.

By flattening the IA and refactoring the navigation, we enabled users to navigate to datasets before a building, giving users access to portfolio-level aggregations of data, as well as buildings.

starting information architecture

Starting Navigation Elements

understanding

In discovery, we conducted a competitor analysis, diagrammed competitors IA, utilized secondary research identifying task based navigations vs content based navigation. Once we identified tasks within the software, we conducted a use case workshop with stakeholders in order to understand each personas use case within the software, and which tasks are utilized most.
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User Personas

Asset Manager

Oversees portfolio(s) of buildings (100+ buildings)
Approves budgeting requirements
Reports to Ownership about portfolio performance and needs

Property Manager

Manages 1-3 buildings
Oversees day to day operations of the building.
Reports to Asset Manager about building performance and needs

Building Engineer

Works on 1 specific building
Oversees Utilities, maintenance, and operations of the building
Works with Property Manager to identify projects needed to improve the building

Third Party Consultant

Works on 1 Building or entire Portfolio
Usually a Sustainability Consultant reporting on building performance
Needs access to granular data about buildings across a portfolio

Portfolio Tasks

Building Tasks

Setup Tasks

Task Use Case Workshop

What we learned

The mental model used to establish the navigation of the current interface coincides with how we as an company understand and utilize data to improve services, but our users have a building-agnostic mental model of the data provided; they are looking for specific pieces of data.

ideation

Using what we learned in Discovery, I diagrammed flattened IA versions and iterated navigations that might embody how the IA diagrams organize data. Throughout this process, we maintained communication with stakeholders, incorporating feedback.
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Through this process, we were able to identify an IA that brought datasets to the forefront of navigation rather than buildings. The best two navigations reflecting the IA data hierarchy were selected for testing.

Information Architecture iterations

Navigation sketches & lo-fi wireframes

validation

Hypothesis

A flatter information architecture will result in improved findability of actions. Findability is defined as a user's ability to find what requires the user's attention. We also want to learn if our hypothesis improves:‍

effectiveness, a user's ability to find an item when given a task
efficiency, a user's subjectively percieved effort to accomplish something

Methodology

A/B test of black and white wireframes utilizing the same IA but different UI: horizontal vs left hand navigation.
10 Participants split between Group A and Group B were asked to complete the same 5 tasks in the same order.
Participants were evaluated on ability to complete the task (pass/fail), time to complete the task, and asked to rate the ease of completing the task.

research artifact

research synthesis

final design

Ultimately, we were able to design a navigation that allowed users find datasets quickly and take action. The implemented state is a hybrid approach between the old mental model of data organized by building and the new mental model of buildings organized by data.

In order to fully implement the new mental model, each data set must be refactored to accommodate portfolio level aggregation of data. Thus, we incorporated a "Building Selection" tab in order to give time to understanding each data slice and the actions users may take from aggregated data.

After implementation, we saw improved usability: users spent 50% less time navigating to the datasets they came to find.

user outcomes

Users have access to portfolio-level data

Users have access to actions earlier in their workflows

External users will experience onboarding as a feature of the software.

Landing page

Building documents + Onboarding Guide

expanded nav - reports

collapsed nav - reports

Customer quote

"When I was in this morning I realized it was a little easier for me to find what I was looking for ahead of our meeting. We liked the way the software showed before, but this is even better."
-Asset Manager

navigation pattern

Learnings & Next Steps

Several data slices require further investigation in order to aggregate data effectively at the portfolio level.
Now that users are able to find data they are looking for, how might we empower our users to understand that data and take action in bulk?
Onboarding is attached to one building, how might we streamline that process for new users?