A healthcare client held a wealth of data and a hard truth: almost no one in the organization could put it to work. Leadership wanted to change that. My charge was to lead the research and shape the UX strategy for a self-service analytics platform — bringing cloud analytics, machine learning, and AI together so insight reached every team, not just a handful of specialists.
The mandate was easy to say and difficult to earn: democratize the data, and make better decisions the default across the business.
At a Glance
The client had invested heavily in data but couldn't convert that investment into everyday value. Insight lived with a small group of specialists, while the teams who needed it most were left waiting, guessing, or working around the system entirely.
The real problem wasn't only technical. It was human and organizational: how people handed off information, how much they trusted it, and where confidence broke down between the people who created insight and the people who needed to act on it. My job was to make sense of that ambiguity and turn it into a strategy leadership could commit to.
I structured the engagement so a small team could move fast and still reach conclusions the client trusted.
Immersion. We started by listening. This phase surfaced how people actually experienced data day to day — where they hit walls, what skills existed across the workforce, and where the gaps were. We mapped the human terrain, not just the tools.
Two-day workshop. Access to senior stakeholders and consultants was limited, so every hour had to count. I designed two focused days to go deep on the data insight experience: how people sought information, what earned their trust, where confidence faltered, and what "good" would look like for teams doing this work.
Synthesis. We turned observation into structure — mapping the insight experience and the broader ecosystem around it. That ecosystem view mattered, because it showed not just where people struggled, but why, and what kept the friction in place.
Future-state story. From there, we moved into design. I identified the highest-value opportunity areas and brought them to life through a future-state journey map and a set of hero concepts. These weren't theoretical. They gave decision-makers a concrete picture of a better experience and a clear reason to build it.

Data Translation Journey Current State Journey Map
I led design research for the engagement and owned UX strategy and hands-on design across the future-state story and hero concepts. I managed two direct reports and partnered across a multidisciplinary team to keep the work aligned to both user needs and business goals.
My direct reports:
Cross-functional partners:
Much of the value I added came from connecting these very different perspectives — research, engineering, product, and executive priorities — into a single, coherent direction. I translated between the specialists who created insight and the teams who needed to use it, and I kept the C-suite sponsor confident that the vision was both ambitious and achievable.

Pre-Workshop: Synthesis of job scan and initial exploration of how traditional roles are morphing
This engagement is a good picture of how I lead. I take an ambiguous, high-stakes problem — one that's part technology, part organization, part human behavior — and turn it into a strategy a leadership team can act on.
For the client, that meant a credible vision for democratized data and a defined data translator role to carry it forward. For me, it reinforced a conviction I bring to every product and team I lead: the value of data, AI, or any complex system is only as real as the experience built around it. My job is to make that experience clear, usable, and worth trusting — and to lead the people who bring it to life.


The current state journey map is the representation of the consultant's journey from their point of view. It is a tool for understanding the state of affairs by identifying interactions between journey actors, places, artifacts, and perceived pain points and moments of delight.
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