Decision Signals

Decision Signals.

Patterns I’ve learned to recognize before the visible problem is correctly understood.

Across platforms, growth journeys and zero-to-one products, the same warning signs appear in different forms. These are some of the signals that change how I frame the problem, what evidence I seek next, and where I choose to intervene.

Stage 01

Understand the System

Clarity · Insight · Framing · Systems

Read past symptoms to find the real problem.

When the visible problem is only the surface

What I notice: The brief asks for a redesign, feature, campaign or platform change, but the same friction appears across workflows, ownership, technology or measurement.
What it may actually mean: The visible request may be a symptom of a wider system problem.
What I look for next: Where the problem repeats, what happens before and after the visible interaction, who owns each handoff, and which constraints would remain even if the requested change shipped.
What changes because of it: I expand the frame before committing the solution—so the team solves the system behind the problem rather than improving its appearance.

Related professional proof

Jack.org

When the data shows friction but not its meaning

What I notice: A funnel, completion rate or behavioural signal tells us where people stop, but not why they stop.
What it may actually mean: The next useful evidence may not be another dashboard. The problem could be comprehension, confidence, choice overload, technical failure, or a mismatch between the customer's goal and the journey we designed.
What I look for next: Segment behaviour, the decision being asked of the user, contextual research, task-level friction, and what happens immediately before and after the drop.
What changes because of it: Measurement becomes an input to diagnosis rather than a substitute for it.

Related professional proof

Google Tangerine

Stage 02

Choose the Path

Judgment · Strategy · Trade-offs

Choose the intervention that creates the right leverage.

When growth is really a resilience problem

What I notice: Demand, adoption or interest is increasing, but reliability, infrastructure or operating capacity is becoming more fragile at the same time.
What it may actually mean: The constraint may no longer be customer demand. It may be whether the product can sustain the value it has already created.
What I look for next: Failure patterns, dependency risk, learner or customer continuity, support workarounds, platform constraints, and whether additional growth would amplify the underlying weakness.
What changes because of it: I treat resilience as a growth decision—not merely technical maintenance.

Related professional proof

Be There Certificate

When the solution is becoming clearer faster than the problem

What I notice: Teams are converging on architecture, features or delivery plans while the user need, information model, requirements or operating context is still moving.
What it may actually mean: The organization may be gaining confidence in a solution before it has earned enough confidence in the problem.
What I look for next: What research could invalidate the current direction, which assumptions are being treated as requirements, what must be true for the proposed architecture to work, and what can remain reversible.
What changes because of it: I use evidence to challenge the early solution and keep the product architecture adaptable until the important uncertainties have been resolved.

Related professional proof

edHUB

Stage 03

Execute for Outcomes

Execution · Growth · Transformation

Make delivery reinforce the outcome, not just the release.

When the easiest metric is not the outcome that creates value

What I notice: A convenient success measure—launches, deposits, account creation, clicks or completion—can improve without proving that the customer has reached meaningful product value.
What it may actually mean: The team may be optimizing the event that is easiest to count rather than the behaviour that matters.
What I look for next: The downstream state that represents durable customer value: meaningful activation, funded use, recurring behaviour, reliable measurement or sustained adoption.
What changes because of it: I redefine success around the behaviour we actually want to create, even when doing so makes the product problem harder.

Related professional proof

Google Tangerine

When launch readiness is really operating readiness

What I notice: The product can technically ship, but ownership, governance, QA, measurement, content operations or post-launch learning are still unresolved.
What it may actually mean: The team may be ready to release an experience that the organization is not yet ready to sustain.
What I look for next: Who owns what after launch, how quality is maintained, how performance is measured, how teams operate the product, and how learning gets back into prioritization.
What changes because of it: Launch becomes a transition into an operating system—not the end of the project.

Related professional proof

Jack.org edHUB Be There Certificate

From signal to decision

Recognizing the signal only matters if it changes the decision.

See Proof in Practice