hollandkevint
@hollandkevint
Public Skills
data-team-positioning
by hollandkevint
Position data teams as strategic partners, not order-takers. The organizational "why" behind doing discovery work. Use when discussing team positioning, value exchange, demand shaping, escaping the order-taker trap, or when someone asks "how do we stop being order-takers?" or "how does the data team become strategic?"
data-consumer-discovery
by hollandkevint
Discover what internal data consumers actually need. Adapted Mom Test and JTBD for data teams. Use when conducting user research, interviewing stakeholders, gathering consumer requirements, running discovery sessions, or when someone asks "what do they need?" or "how do I figure out what to build?"
data-product-validation
by hollandkevint
Score whether a data product idea is worth building before committing resources. Validation scorecard, experiment design, and go/kill decisions. Use when evaluating feasibility, making go/no-go decisions, validating demand, sizing bets, or when someone asks "is this worth building?" or "should we invest in this?"
data-product-thinking
by hollandkevint
First-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for data offerings, or when someone asks "should we build this data product?"
stakeholder-alignment
by hollandkevint
Translates between technical data teams and business stakeholders. Use when preparing stakeholder updates, translating technical data work for executives, shaping vague business requests into buildable specs, navigating competing priorities across data consumers, or when someone asks "how do I explain this to my VP?" or "the business team wants X but that's not how data works."
ethical-risk-assessment
by hollandkevint
Ethical data risk evaluation, bias testing protocols, and governance practices for data products. Use when evaluating ML/AI features for fairness, designing bias testing protocols, planning phased rollouts for high-risk changes, reviewing data governance practices, or when someone asks "could this model be biased?" or "how do we ship AI features responsibly?" For HIPAA-specific guidance, see healthcare-data-domain.