Upstream Analytics: Defensible Business Recommendations for Data Professionals

Recognize what problems are worth solving with a three-question screening test; classify the opportunity and determine the next move.

Locate the mechanisms that drive the underlying problem so that time isn't wasted: the situation, trigger, stakes and unknowns.

Determine the Definition of Useful that your recommendation must speak to so you have a clear standard to guide your work.

Translate your work into language that helps affected stakeholders understand its relevance—not just its accuracy or interesting POV

Decode the business layer. Use performance structures and functional frameworks to connect your recommendation to the company’s strategy.

Evaluate scenarios instead of fixed answers so you can keep recommendations flexible as business conditions shift.

Weigh competing dynamics like speed vs. confidence so you can build defensible cases leadership respects.

Filter choices by scope, timing, and risk so you can present clear, actionable options ready for executive decision-making.

Distinguish between Controllable (what your team can directly fix) and Adaptable (what you must design around) so your plan is realistic.

Use the IMI approach to identify the organizational, operational, and technical conditions that could derail a promising idea.

Create a visual decision-making map (recommend, decide, agree, input, perform) so you bring in the right people at the right time.

Plan nudges to prime key decision makers like a pro: draft preliminary options, cost-of-doing-nothing vs. change in status quo and more

Run a recommendation blind-spot review using what you've built from the course to surface realistic concerns and adjust your final plan

Do not build a material recommendation on one unchallenged signal; seek converging evidence or narrow the next step so your plan has legs

Submit your final plan, stakeholder panel results, and decision design choices with direct feedback.

Leave with mental models that can support your AI work to fill in gaps, support stronger critical thinking and generate more robust outputs.