USER EXPERIENCE RESEARCH AND VALIDATION
QUALITATIVE / QUANTITATIVE RESEARCH
Methods to gather insights and data-driven validation for informed, human-centered decision-making.
The Challenge
In collaboration with the data analysis team at the Burning Glass Institute, the project goal was to determine a successful data-backed scoring methodology and data tools for non-degree credentials in a U.S. marketplace of over 1 million credentials and nearly 60 million credential providers. This was the initial discovery phase of the project.
Users / Audiences
The project involved 4 large target markets for the EQOS Quality Signal: educators, employers, funders, and policy makers with workers/learners as the main, but silent beneficiary of this work.
Scope and Constraints
With guidance from JFF subject matter experts (SMEs) we were able to prioritize our initial scope to focus first on educators and employers. We set aside funders and policy makers for a later project phase due to timeline and budget constraints.
Organizations
EQOS, JFFLabs, Burning Glass Institute (BGI), Gitlab (funder)
My Role
UX Design Lead, Product Manger
Date / Duration
Jun 2023 – Aug 2024
My Process
I began with desktop research.
I started by consuming recent data and publications produced by JFF and their partners and shared publicly to define and validate a series of problem statements and outline an interview plan. This work also informed the decisions we made regarding scope and constraints.
We talked to internal and external subject matter experts (SMEs).
Once our market targets were refined, I led a series of interviews with internal SMEs and education and employment organizational contacts and partners in the field. I reviewed and adjusted our interview plan as we gained learnings and understood the market in more depth and detail.
Outcomes and Learning
We learned the effective market targets were not aligned with our initial hypothesis.
Through our conversations we learned the real consumers of the EQOS Quality Signal would most likely be education and employer intermediaries like training and talent acquisition service organizations, not educators and employers directly. Additionally employers were clearly the power brokers and the best initial targets—through their intermediaries—for effect adoption and system change. We shared our learnings with our funder and project partners then refined the direction of our interview plan for the next phase of the project.
This work was the also the basis for our initial use cases informing what quality signal tools we would build and who it would serve in what way.
The Connecticut Project
Lead by The Connecticut Project and the Connecticut Office of Workforce Strategy, the project's goal was to ask JFF to provide recommendations for a data sharing model for workforce information across Connecticut’s ecosystem to improve data insights and strategic planning.
The Challenge
I was tasked to analyze a set of interviews with over 50 individuals from various sectors of the Connecticut state government system to help inform the project team. My goal was to identify the data processes, systems, challenges and opportunities that spanned across diverse groups with varying agendas.
Users / Audiences
Although recommendations made through the project were designed to inform stakeholders directly, actions taken based on these recommendations would be felt by consumers of state programs and services including job seekers, workers, employers, and state clients.
Scope and Constraints
I had a window of 4 months to analyze video interviews, transcripts, and shared documents with an ask from the main project team to provide insights early and often.
Organizations
JFF, CT Office of Workforce Strategy (OWS), CT Dept of Labor (CTDOL), The Connecticut Project (strategic advisor and funder)
My Role
UX Research
Date / Duration
Apr 2024 - Aug 2024
The Process
I began with a quick round of desktop research to understand the players.
It was crucial I have understanding of the hierarchy within state of Connecticut government and how participants from the different groups we interviewed would interact. I needed to be clear on the variety of perspectives represented in the information I was analyzing.
I reviewed and annotated the interviews and other information.
I uploaded videos, transcripts, and documents into the UX research tool, Dovetail, which allowed me to create a series of tags for systems, tools, data methods, challenges and highlight opportunities aligning with project objectives.
I organized learnings for the team.
I provided the project team with rolling analysis of interviews within Dovetail as well as compiled analysis insights into a document.
Outcomes and Learning
I was able to provide valuable insight to the team.
The analysis highlighted key challenges and opportunities, providing valuable insights that informed the larger team’s decision-making process throughout the project and served as the foundation for a series of phased recommendations in the final proposal.
We learned the largest challenge to overcome for the next phase of the project would be internal politics among state groups along with siloed processes and data systems that would be difficult to integrate.
