data-team-positioning

Three Stances

Data teams operate in one of three positions. Most are stuck at position 1.

1. Request Taker

"Tell us what to build." The backlog is a queue of stakeholder requests. Prioritization is based on who asked loudest or most recently. The team is measured on throughput: stories completed, dashboards shipped, tickets closed.

Why it fails: The team builds what was asked for, not what's needed. No discovery means no understanding of the actual problem. Stakeholders lose trust when delivered products don't solve their real need.

2. Request Shaper

"You asked for X, here's why Y is better." The team pushes back on requests, translating vague asks into buildable specs. Uses stakeholder-alignment patterns to reframe requests around decisions and outcomes.

Why it's not enough: Still reactive. The team only works on problems that stakeholders bring to them. Important problems that stakeholders don't know to ask about go unsolved.

3. Demand Shaper

"We interviewed 15 stakeholders, here are the top 3 problems." The team runs its own discovery. Consumer evidence drives prioritization, not stakeholder politics. The team with the most evidence has the most influence.

This is the target. Discovery work (data-consumer-discovery) IS the positioning mechanism. You don't ask for a seat at the table. You bring the data that makes the table's decisions better.

Evidence as Currency

In organizations, influence follows evidence. The team that can say "we talked to 15 consumers and the #1 pain point is X, costing $200K/year in manual workarounds" outranks the team that says "we think we should build Y."

Build evidence systematically:

• Run discovery cycles quarterly (see data-consumer-discovery)
• Synthesize findings into problem briefs (see research-synthesis-data)
• Score opportunities against the validation scorecard (see data-product-validation)
• Present ranked opportunities at the betting table with evidence attached

The discovery practice is not overhead. It is the single highest-leverage activity for team positioning.

Betting Table Pitch Structure

When presenting data product opportunities to leadership:

Problem (2-3 sentences): State the problem with consumer quotes and workaround evidence. "Regional analytics leads spend 15+ hours/week manually reconciling reports across 3 systems. Quote from Sarah, Analytics Lead: 'I don't trust the automated numbers, so I rebuild everything in Excel.'"

Appetite (1 sentence): Time budget, not estimate. "We'd bet 4 weeks of one squad on this." Appetite caps investment. If it can't be solved within the appetite, reshape the scope.

Tradeoff (1 sentence): What you won't build to fund this. "This means we defer the executive dashboard refresh to next cycle." Every yes is a visible no to something else.

Never pitch without evidence. A pitch backed by "we think" loses to a pitch backed by "we observed." Discovery is the investment that makes every other investment smarter.

Anti-Patterns

Recognize these signs that a team is stuck at position 1:

• No discovery before sprint planning. The team plans what to build without talking to consumers first.
• Backlog populated entirely by requests. Zero items originated from the data team's own research.
• Measured on throughput, not outcomes. Success = stories completed, not decisions enabled or time saved.
• "Urgent" bypasses prioritization. Any stakeholder can jump the queue by saying it's urgent. No evidence required.
• Team can't articulate top 3 consumer problems. If you ask the data team "what are your consumers' biggest pain points?" and they answer with their backlog instead of with evidence, they're order-takers.

Each anti-pattern has a specific remedy:

• No discovery → Schedule 3 consumer interviews this week
• Request-only backlog → Reserve 20% of capacity for team-originated work
• Throughput metrics → Add one outcome metric to the team scorecard
• Urgency bypass → Require evidence (workaround, usage data, or 3 consumer quotes) for priority overrides
• Can't name problems → Run data-consumer-discovery on your most-used data product