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Impacting a Fortune 500 Enterprise with UX Research & UX Design
Our mission: A 10% increase in data literacy across 38,000+ employees
Overview
Tools
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Adobe Figma
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Microsoft Excel
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Mural
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Willis Tower Watson
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HTML/CSS/Javascript
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Adobe Illustrator
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Storyline 360
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Degreed
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Workday
Team
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1 UX Researcher
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1 Senior UX Researcher
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1 UX/UI Designer
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1 Instructional Designer
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1 UI Developer
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1 Program Manager
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1 Product Owner
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1 Scrum Master
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Timeline
Overall: 13 Months
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Research | Analysis | Personas
6 months
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Design | Testing | Development
7 months
My Role
UI Designer | Mixed-methods Researcher | Wireframes | User Testing | Workshop Facilitating | Product Development | Cross - functional Collaboration | Presentation to Stakeholders.
Background
USAA has over 38,000 employees in over 1,000 different job roles, each with their own behaviors around how they handle data. With a priority on data strategy, leadership recognized that in order to remain a data mature and compliant, a learning experience on data skills needed to be customized to all employees. As a result, USAA created a small design team, called the Data Literacy team, and tasked them with building a data literacy learning experience with the goal of a 10% increase in data literacy.
Problem
The Data Literacy team needed to determine the following:
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How to measure an increase in data literacy.
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How to identify the data related learning needs across 1,000+ job roles.​
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How to create customized learning for 38,000+ employees.
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How to create a seamless learning experience for users.
Solution
Our team successfully accomplished the ask. A multi-platform data literacy learning solution was:
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Researched, designed, and developed.
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Made available to the enterprise.
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Customized to different data behaviors exhibited among different job roles.
The Final Product
A customized data literacy learning experience
Research
Strategty
With our eyes on the goal of a 10% increase in data literacy, we needed to understand what that measurement meant. Our business intelligence Analyst began with creating an index to address methods of measurement. While there were more than four measurement methods indicated on the data literacy index, the learning experience I focused on honed in on these key areas:
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What are USAA's employees' current data skills and behaviors?
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What data skills and behaviors are expected of employees in their job roles?
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How can employees ensure their data skills are meeting expectations?
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How can employees learn new skills if they wish to seek promotion or take on more data related responsibility?
Hypothesis
All USAA employees consume data to some capacity. But not every employee builds models with data, makes business decisions with data, or creates systems and controls to protect data. However, looking through the various job roles at USAA, we hypothesized that any employee can technically exhibit any data behavior at anytime. This Venn diagram came out of this hypothesis, and was referenced throughout the project.
To put this to the test, we knew that if this hypothetical persona ecosystem was going to depict all USAA employees, we would need to hear from a large number of employees if we were going to gain insights that accurately depicted these personas.
Live 400-Participant Virtual Focus Group
Since our team only consisted of 2 UX Researchers, we partnered with Willis Tower Watson to use their live moderated virtual focus group tool. with this focus group we recruited the participants, and provided a list of questions. We were able to facilitate and interact with our participants during the focus group by modifying the questions based on their responses live! This 45-minute focus group provided us with a year's worth of quantitive and quality data.
However, it was not until after the focus group ended that we realized that the data from the focus group we would be aggregated and not sorted by the hypothesized persona groupings. As a result, we received the raw data from the focus group, needed to separate the data by personas, and perform analysis on each one.
Once the persona data was added to the raw data from the focus group, our Senior UX Researcher went out an unexpected leave of absence for 6-months. leaving my co-worker to move our efforts forward.
Analysis
Quantitive Analysis
In the questions asked within the focus group, we really wanted to understand how our participants work with data. So we presented them with a list of descriptive words and asked them to select five that best represents how they work with data. We then asked the same question a few more times but with different words for participants to select from. Several questions were asked using this method. Using Excel, we took the raw data and generated these charts for each persona to communicate the findings.
Likert Analysis
We also asked the focus group participants about their feelings towards working with data. The participants could answer these types of questions using the following Likert scale for each persona.
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Strongly Disagree ( Red )
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Disagree ( Orange )
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Neutral ( Gray )
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Agree ( L. Blue )
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Strongly Agree ( D. Blue )
Qualitive Analysis
The list of questions for the focus group also contained several short-answer questions where the participants could answer however they wanted. After separating the raw data by persona, Our team brought the data into a Mural board to conduct synthesis. While we received all sorts of responses for each question. There were some underlying themes and trends to be uncovered. To identify these themes, I conducted thorough synthesis of each question through two rounds of affinity mapping.
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The first round included duplicate answers to see how often the same answers came up.
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The second round did not include duplicate answers to see how often the same theme arose.
Learning-Needs Analysis
After key findings were identified among all three analysis methods, our Instructional Designer took a look at the descriptions of each skill proficiency level within all of USAA's Data and Analytics job roles and mapped the key research findings to the skills and skill levels that best describe the data behavior exhibited within each persona. The Instructional Designer, then began to curate learning materials for each of the skills identified.
Personas
In this project we used personas not in the traditional design sense, but rather as a way to group job roles that perform similar behaviors.
With all of the analysis completed, I took all of the key findings and created persona artifacts in Adobe Illustrator to be used for:
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Stakeholder Communication
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Curation of learning materials
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Design of the customized data literacy learning experience
Outcome
The Grand Unveiling.!
The dat literacy learning plan was designed to meet the business goal of increasing USAA's 38,000+ employee enterprise's data literacy by 10% while also meeting the user's need of a customized learning experience to fill data skill gaps across 1,000+ job roles. To meet user's needs, we changed the language around the personas to levels of data literacy.
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In addition to this, we created an example pre-assessment to help the user identify which skills they can skip over learning about wile also directing them towards learning materials for skills they did not score well in. We also created an example post-assessment to measure how much they have learned so far.
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Unfortunately, our stakeholders deprioritized assessments. As an alternative to pre-assessments, our team provided the user's with a lookup table to identify which data literacy levels were recommended that they pursue based in their job roles.
Measurements
While the measurement strategy did change mid-project, the goal of the 10% uplift in data literacy across the enterprise remained. The data literacy learning plan was not required learning but it was optional for employees. At this point, with the data literacy learning experience completed and available and began providing weekly updates on user engagement.
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The stakeholders new goal for 2023 was to have 75% of the enterprise complete any type of data learning with 25% of the enterprise specifically completing of the learning plan.
Next Steps
With the ambitious goal of almost 10,000 employees completing the data literacy learning plan by the end of 2023, the next steps for the team are to begin a marketing strategy considering the data literacy learning plan as additional learning options. The roadmap for the data literacy project at USAA shows that the project is expected to wrap up in 2024.
Promotional Variation for Enterprise Email Announcement
