Upstream Analytics: Defensible Business Recommendations for Senior Data ICs

Seth Forbes

Ex-Senior Marketing Analyst @ Quickbase

Covered by the Maven Guarantee

Lead with strategy to drive impact, build influence, & shape business decisions.

AI can draft the dashboard, the summary, even the recommendation.

It can't decide which problem is worth solving, weigh what your stakeholders actually need, or form a point of view when the data is incomplete and nobody agrees.

Upstream Analytics is a live lab for experienced data professionals moving from execution to strategic partner. You'll practice the judgment that doesn't get easier just because the drafting got faster.

WHAT TO EXPECT:

>> 3 Weeks · 9 Live Hours: Interactive 60-minute workshops, followed by 30 minutes for AMA, hands-on work, and further case discussion.

>> Strategy Labs: Bring your own messy workplace challenges into live discussions so we can parse through them together.

>> Practical Frameworks: Use my IMI framework and other tools to frame decisions, evaluate evidence and uncertainty, compare options, and plan feasible next steps.

>> Recommendation Planning Brief: A synthesized breakdown of your reasoning, from problem to evidence to your call, ready to use as strong context for AI tools.

Note: This is not a presentation delivery course. It's about decision framing, critical thinking, reasoning, and the upstream work that is required for projects to move forward.

What you’ll learn

Relevant to the decision. Realistic for the owner. Timely enough to matter. Design robust business recommendations - with tons of examples.

Diagnose business problems beyond the request or output level

Recognize what kind of problem you're dealing with so the next step becomes clear;

classify the opportunity and address meaningful levers.

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.

Align your work to business priorities and terms stakeholders can understand

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

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

Evaluate what could make or break your recommendation if not accounted for

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.

Formulate targeted, decision-ready options backed by explicit criteria

Architect multi-scenario decision frameworks so you can keep recommendations flexible as business conditions shift.

Weigh competing organizational dynamics—such as speed versus confidence—to present executive leadership with defensible strategic tradeoffs.

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

Mobilize key stakeholders and build cross-functional alignment early

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

Finalize a Recommendation Planning Brief you can responsibly move forward with

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.

Learn directly from Seth

Seth Forbes

Organizational Data Strategist | Former Senior Marketing Analyst @ Quickbase

Seth Forbes is an Organizational Data Strategist with 10+ years of experience across NGO, retail, nonprofit, and SaaS environments.

Having worked directly with SVPs, Directors, and Managing Partners while also serving on the ground in high-growth B2B data teams, she has witnessed the same recurring pattern: brilliant insights stalling because they failed to connect with the C-suite.

Seth is the Founder of The Quietly Ambitious Analyst , a media platform where she distills actionable frameworks for scaling analytical impact and stakeholder alignment.

Through her interviews with experts from Stanford, Carhartt, Deloitte, and beyond, she provides the tools data teams need to stop reporting numbers and start driving strategy.