Data Crossings participants are a uniquely prepared talent pool — undergraduates and working adult learners from non-STEM backgrounds who've spent a full academic year building applied data science and AI skills alongside the business acumen to use them well.
Technical capabilities. Participants can perform exploratory data analysis on real-world datasets, build interactive dashboards and data visualizations, apply statistical and machine learning models, work with large language models, and run specialized analyses including geospatial (GIS) and network analysis. They've completed hands-on cybersecurity training through the KC7 Foundation and understand the ethical frameworks that should guide responsible AI deployment. Using intuitive, plug-and-play tools like Orange, Pajek, Neo4j, and Obsidian, they can deliver insights without requiring your team to invest in heavy technical onboarding.
Domain expertise. Because participants come from fields like healthcare, marketing, manufacturing, public service, and the liberal arts, they bring something pure technologists often can't: an existing understanding of your world. They've been trained to translate domain problems into data questions, and data answers back into business action.
Superskills. Every participant is trained in human-centered design thinking, problem framing, teamwork, written and verbal communication, critical thinking, and ethical decision-making. They've completed a team-based capstone project, presented findings to stakeholders, and learned to collaborate across disciplines and generations.
ExLENT participants arrive ready to contribute on day one: to a customer analysis, a predictive pilot, a dashboard, a process automation, or whatever real question your business needs answered.
In its inaugural 2025-2026 year, ExLENT Data Crossings participants led nine applied capstone projects, each scoped, executed, and presented to stakeholder audiences under Lucy Family Institute faculty supervision. The range of work demonstrates the program's breadth.
On the technical and operational end, participants built production-grade tools: a Python/PuLP linear-programming optimizer for a small-batch bakery, a FAISS-based document-grounded chatbot with bilingual support, safety escalation, and ISO 9001/OSHA-aligned governance for a manufacturing floor (judges' 1st-place selection), and an R-based demand-forecasting model with lagged regression for the northern Indiana RV industry.
On the enterprise and finance end, participants tackled real corporate decision problems: a variance-bridge analysis decomposing material-cost changes into volume, mix, and rate components at both part and customer granularity, and a five-phase Claude-enabled procurement transformation proposal for a regional metal-stamping and fabrication manufacturer, complete with workflow mapping, ERP integration plan, and risks-and-limitations framing.
On the public-policy and social-impact end, participants applied data science to questions of equity and governance: a network-science analysis of $27.9B in humanitarian funding across 13 SWANA countries, surfacing concentration, localization gaps, and structural single-points-of-failure via a live interactive dashboard; a tract-level analysis of socioeconomic and racial disparities in St. Joseph County integrating five public datasets; and an interactive four-panel dashboard analyzing Indiana's Choice Scholarship Program against statewide ILEARN academic outcomes.
Rounding out the cohort, a communications-led capstone produced seven publication-quality interview-based profiles of fellow participants' work, demonstrating the program's design hypothesis that domain experts can use AI as a "maker" to produce deliverables grounded in their existing professional craft.
Across all nine projects, participants demonstrated the full applied-project arc: framing real problems with stakeholders, selecting and executing appropriate methods, building working deliverables, and presenting findings with explicit attention to ethics, limitations, and real-world consequences.