Telling a Story with
Information Architecture
An exploration of using story telling to improve IA, resulting in better way-finding and data comparison
Interviews
People
Internal
Brian Baker - Product Manager
Ben McAllister - Product Owner
Michelle Miesel - Building X - UX Lead
Alex Cummings - Service Operations Manager
Manojkiran Casilingam - Team Lead, ESG Managed Services
Utsav Mital - Service Operations Manager
Laura McConnell - Manager, ESG Reporting
External
Arlene Noriega, RPA - Property Management, NAD Group
Sophie Dajka - Property Management, NAD Group
Alex Lee - Manager, ESG Analytics and Sustainable Investing
Janessa Choong - Sustainability Analyst
Vlad Rudko - Principal Key Expert - Engineering
Curtis Flores- Principal Key Expert - Engineering
Michelle Pacheko - Manager, ESG Reporting
What We Heard
Data navigation issues: Users struggled to zoom in and out of data. They wanted to see the big picture, and then easily drill down to the root of any issues.
Rigidity: The software was too rigid. Some users had to export two reports into Excel just to create a third report that met their needs.
Navigation confusion: Many users found Stream hard to navigate. Some didn’t know where to go, and a few even requested features that already existed. They just couldn’t find them.
Work flow: The IA did not align with the work flow of many of our users. The information each persona needed was in many different places.
Card Sorting
Research Details
Given that Stream is a complex SaaS product, it couldn’t be tested with the general public. Imagine trying to figure out how normalized performance, NEUI, CUSM, and carbon emissions fit together without solid ESG knowledge. However, this posed a challenge: How do you conduct card sorting with users who are so familiar with the product that they might just organize the cards as they are now? “Oh, this card says ‘benchmarking.’ I know where that goes.”
To counter this, I focused on functionality rather than categories for the card titles. For instance, instead of labeling a card as "Waste Summary," I used “review a summary of details regarding waste output.”
Sample Size: 24 internal and external stakeholders.
Method: Open Card sort via Optimal Workshop
Cards: 40 cards representing existing pages and features in Stream.
Output: Dendrogram and Similarity Matrix
Results
Two categories emerged with 100% agreement.
Targets/Future Planning
GHG
Benchmarking emerged as a category with strong agreement at 55%.
Five additional categories formed, but there was less than 50% agreement across participants.
Analysis/Performance/Waste
Reporting/Surveys
Certifications
Data Input/Billing/Utilities
Buidlings/Configuration
New Approach
Direction
After conducting card sorting and reviewing competitive analysis, we noticed that all but one of our competitors, including Stream, approached information architecture (IA) through simple categorization. This "like goes with like" method has two key issues: it doesn’t truly consider user workflows, and it causes the navigation menu to expand as features are added
For instance, while a sub-meter is physically located in a building, the user shouldn't necessarily have to navigate to the building menu to input its data. There may be a more intuitive location for this function.
Stream's purpose is to enable users to understand how an asset or group of assets is performing. This requires more than categorizing data. It needs to tell a story.
Telling a Story
Looking at the interviews, card sorting, and the purpose of Stream, a story began to emerge. Our software weaves together data to answer key questions:
What is our impact?
How can we mitigate it?
What’s driving this impact?
What are the sources of our impact?
How can we communicate all of this?
The answers:
Emissions
Targets
Analysis and Data Management
Buildings
Reports and Surveys
New IA
Direction
Currently, Stream has:
8 main nav Items
31 secondary nav items
15 tertiary nav items
3 quaternary nav items
Streams new IA:
6 main nav items
13 secondary nav items
Intuitive Structure
As we took this back to our stakeholders, what we found was that this approach not only told a story, but that it aligned with the workflows of our users and personas.
Some users worked mainly on inputting data to ensure data completeness. Previously, these users would need to go 3-4 different pages spread out between main, secondary, and tertiary navigation items in order to manage this data. Additionally, those focused most on analyzing cost and consumption data had to go to various pages to look at waste, energy, benchmarking, normalized performance...This created not only a way-finding issue, but a data comparison pain point.
IA Cannot Fix Everything
During our interviews, we heard about a lot of issues that our users faced, Here are some of those again:
Data navigation issues: Users struggled to zoom in and out of data. They wanted to see the big picture, and then easily drill down to the root of any issues.
Rigidity: The software was too rigid. Some users had to export two reports into Excel just to create a third report that met their needs.
Navigation confusion: Many users found Stream hard to navigate. Some didn’t know where to go, and a few even requested features that already existed. They just couldn’t find them.
Work flow: The IA did not align with the work flow of many of our users. The information each persona needed was in many different places.
What IA Solves
Telling a story.
Improved way-finding.
Grouping information more intuitively.
What IA Doesn't Solve
Maximum click reduction
Improved data comparison.
Drilling in and zooming out
Increased flexibility of data visualization
Stream needs a dashboard. Not only does this allow the user to tell their own story, it meets two or our core needs: Reducing clicks and improved data comparison.
Stream has widgets for all types of data. The issue is that these widgets were extremely rigid and isolated. All of the widgets on the page were controlled by a global asset filter and secondarily by a page level filter. So, if you identified an issue in your monthly consumption and wanted to see what asset was the root cause, you could only do so by filtering down to each asset one at a time.
The images below demonstrate an increased ability to drill in and out of data by filtering directly in the widget and by creating interaction between the widgets.
Summary
By allowing our IA to tell a story and increasing the flexibility of our software we accomplished:
Click-reduction
Data visualization flexibility
Improved work-flows
Enhanced way-finding
User-friendly data comparison
Boosted drilling-in and zooming-out capabilities.
In-widget filtering will be used to increase the flexibility of data visualization and data comparison, Stream’s global asset filtering and on page filters severely limited the flexibility of data visualization within a widget but also in comparing data between widgets. In-widget filtering allows a user to adjust the data they see in each widget in order to compare different data sets without resetting the filtering for the entire page or opening up multiple tabs.
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