ExpectedOutcomes: 

• Teams acrossEnterpriseKCand the partners they serve across government, enterprise, academia, and economic development gain useful analysis, recommendations, and decision tools that help them solve problems, choose paths forward, and move faster.

• Important questions and opportunities are evaluated through credible evidence rather than activity, assumption, or technology alone.

• DataSpherebecomes more useful, connected, and widely used as EnterpriseKCadds valuable analytical capabilities and builds on what works.

• Assigned work connectsobjectives, leading and lagging indicators, performance information, and outcomes in ways that support continuous improvement.

• Analytical methods, measures, workflows, intelligence capabilities, and learning from one assignment strengthen work acrossEnterpriseKCteams, programs, and strategic industry clusters, helping EnterpriseKCextend proven analytical work without sacrificing quality.

Key Metrics:

• Assigned work begins with a clear business or operating question, analytical question, intended value, and measure of success.

• Quality, credibility, timeliness, and decision value of the resulting analysis, findings, and recommendations.

• Time from an identified needtouseful analysis, a working prototype, or meaningful first use.

• Adoption and repeat use of dashboards, intelligence tools, andDataSpherecapabilities; task completion; and reported usefulness in making a decisionor advancing work.

• DataSphereimprovements, reusable analytical capabilities, and increased use that can be traced to real needs.

• Reviews of analytical performance and decision value that result in documented recommendations, improvements, or reusable learning.

• Work completed on time and accepted within agreed review rounds.

Attributes:

• A hands-on analyst and builder who is energized by turning ambiguous questions into useful intelligence and practical tools.

• Relentlessly focused on creating value rather than producing activity, reporting, or technology for its own sake.

• Curious and disciplined about how organizationsoperate, what the evidence reveals, and what data and emerging technology can make possible.

• Entrepreneurial and resourceful, while able to challenge assumptions, distinguish activity from outcomes, and communicate uncertainty honestly.

• Accountable for the quality of the analysis, the usefulness of what is built, and what happens after delivery.

Skills andAbilities: 

• Translate priority questionsidentifiedby EnterpriseKCteams and programs — including questions arising from their work with partners across government, enterprise, academia, and economic development — into analytical questions, decision requirements, and practical work plans.

• Find, evaluate, organize, analyze, and interpret quantitative and qualitative information; defineobjectives, baselines, leading and lagging indicators, outcome measures, and analytical methods.

• Identifypatterns, gaps, trends, scenarios, implications, risks, and opportunities, then turn them into clear recommendations.

• Define what an intelligence solution should help people do and guide the analytical work from question through testing in use, evaluation, and evidence-based improvement.

• Build usable prototypes and intelligence solutions with artificial intelligence, analytics, visualization, business-intelligence tools, low-code or no-code tools, and reusable workflows.

• Communicate analysis, findings, possibilities, recommendations, risks, and decision value clearly to people withdifferent levelsof data experience.

• Lead analytical projects and cross-functional work, coordinate contributors, and move complex assignments forward.

• Evaluate use, decision value, performance, and outcomes; apply the learning toDataSphere, reusable capabilities, and future work.

• Use artificial intelligence and emerging tools creatively and responsibly; advanced full-stack programming is notrequired, but the willingness and ability to build are essential.