Landscape & Data Analysis

 A landscape analysis is a structured look at what's happening in a given field, issue area, or geography — who the key players are, what approaches are being tried, where the gaps are, and what the evidence shows. It's particularly valuable when you're entering a new space, considering a strategic shift, or trying to make the case for investment in a particular area. Research and data from a landscape analysis is often used to inform other service areas, like career pathway and program development.

We work with a broad range of data types — program and administrative records, publicly available datasets, survey results, interview and focus group findings, and published research. We're skilled at bringing quantitative and qualitative information together into a coherent, usable picture.

We also maintain our own subscription licenses to the same powerful labor market intelligence and analysis platforms used by state agencies, giving our clients access to real-time insight, local granularity, regional trends, and historical context — the tools needed to make confident, data-informed decisions.

But data is only as good as what it captures. We design data collection and capture processes with equity at the center — prioritizing methods that meet people where they are, reduce barriers to participation, and ensure that the communities most affected by a program or policy are meaningfully represented in the data. Good data practice isn't just technical; it's relational.

Yes, we can help you organize, analyze, visualize, and interpret what you already have, and identify what additional information might be worth gathering.

 We work hard to make analysis accessible and actionable. Deliverables are tailored to your audience and purpose — that might mean an executive summary, a visual data brief, a set of slides for a board presentation, or a detailed report. We focus on clarity so that findings can drive decisions.

Equity in data isn't just a methodology — it's a mindset. We're attentive to whose data is being collected, whose perspectives are represented, how findings might be interpreted or used, and who ultimately owns the data and its story.

We disaggregate data to surface disparities that averages obscure, and we're transparent about limitations — including what the data doesn't or can't capture. But numbers alone rarely tell the whole story. Quantitative data shows us what is happening; qualitative data — interviews, focus groups, lived experience — tells us why, and often reveals strengths, assets, and context that charts and tables miss. We treat both as essential, and the combination as more powerful than either alone.

Throughout our analysis, we maintain an asset-based lens. Rather than leading with deficits, we ask what communities, participants, and programs are already doing well — and how systems and structures can be redesigned to better support people's success. The goal is never just to document gaps, but to generate insight that points toward change.