Speculative case study

Head of Product, Datascape

How I would lead this role from Day 1

Datacom's Datascape is the fastest-growing platform in the local government sector. This is not work I have done — it is a genuine projection of how I would approach this role, grounded in 20 years of product and design leadership across government, fintech, and enterprise.

Role: Head of Product · Organisation: Datascape / Datacom · Status: Speculative — April 2026

Starting point

What follows is closest to an ideal state if we were shaping direction largely from scratch — but there are multiple realities for where the team and product actually are on Day 1.

I might instead step in mid-flight: momentum already in motion, delivery advanced, and fractures showing on several fronts at once. In that case I would adapt — rather than assessing entirely ground-up, I would validate what has already been researched and blueprinted, tighten signal from existing work, and move fast to the problems that matter across the fronts where the fires are burning hottest.

THE PLATFORM

Local Government SaaS

Serving councils across Australia & New Zealand

THE MISSION

Communities First

Day-to-day council operations and citizen engagement at scale

THE CHALLENGE

Vision + AI + Team

Own the product roadmap, build the PM team, embed AI responsibly

THE APPROACH

Not a maintenance role. A leadership and growth role.

Datascape is at an inflection point. It has product-market fit, a growing customer base, and a genuine mission. What it needs now is a Head of Product who can set clear direction, build a high-performing PM team, translate customer insight into roadmap, and make AI an embedded, responsible capability — not a feature bolted on for optics.

That is the challenge I am built for. Below is exactly how I would approach it.

PHASE 1

Days 1–90

Listen, Map, Diagnose

PHASE 2

Months 3–6

Operating Model + Roadmap Reset

PHASE 3

Months 6–12

AI Strategy + PM Team Build

PHASE 1 — DAYS 1–90

Listen, Map, Diagnose

The instinct most people get wrong in a new Head of Product role is to move too fast.

Before I opened a single Jira board or sat in a roadmap review, I would get into councils — calls and site visits with 10 customers across different sizes and geographies. Not to demo the product. To watch them use it.

Where do they hesitate? Where do they work around it? What do they complain about to each other that never makes it into a support ticket?

My experience with Transport NSW, the NSW Department of Education, and the Justice of the Peace Department taught me consistently: the most important insights are never in the data. They are in the moment a user stops talking and just clicks something that doesn't make sense — because they stopped expecting it to.

What I would map in the first 30 days

What exists

Full product audit — what is live, what is in progress, what has been promised

Where the gaps are

The delta between what engineering is building, what sales has promised, and what customers actually need

PM team health

Not a formal review — real time in their actual work, discovery sessions, and PRDs

North Star clarity

By Day 90: a two-day offsite to answer the three questions that reset everything

THE NORTH STAR RESET

Three questions. Everything else is sequencing.

What problem does Datascape uniquely solve for councils that no other platform solves?

In 3 years, what does a council that uses Datascape look like — differently from one that doesn't?

What is the one thing that, if we got right in the next 12 months, would make councils renew without hesitation?

PHASE 2 — MONTHS 3–6

The Operating Model

Discovery Cadence

A permanent, lightweight heartbeat — not a big quarterly research sprint:

· Fortnightly customer calls — rotating PMs, shared notes bank
· Monthly synthesis session — 90 minutes, cross-functional, turning signals into insight themes
· Quarterly deep dives — 3–5 days, one major problem area, with council participation

No roadmap decision made in a vacuum.

Outcome Roadmap

Move Datascape from a feature roadmap to an outcome roadmap. Every item structured around the measurable change in council behaviour we are trying to drive — not the feature we are planning to ship.

Each outcome has: a customer problem statement, a hypothesis, a success metric, and a named owner. This makes prioritisation conversations honest. When you argue about features, everyone has an opinion. When you argue about outcomes, you argue about evidence.

Three-Tier Investment Model

· Committed — next 2 quarters. High-confidence, deeply scoped, engineering-ready.
· Directional — 2–4 quarters out. Customer-validated problem areas. Not yet solution-defined.
· Exploratory — 4+ quarters. Emerging signals and AI bets. Deliberately held loosely.

Gives the executive and sales teams what they need for honest customer conversations — without over-committing engineering.

PHASE 3 — AI STRATEGY

Practical, not performative.

The most dangerous thing a product team can do with AI right now is build it because it is expected — not because there is a clear customer problem it solves better than the alternative.

Define the problem, not the solution

Map every Datascape workflow: where is a council staff member making a decision that takes disproportionate time relative to the value it produces? That is the AI opportunity space.

Responsible AI by design

Human-in-the-loop for any AI output that influences a citizen-affecting decision. Clear explainability standards. Rollback plans. This is what makes AI trustworthy in the public sector.

Start narrow, prove value, expand

First AI capability: clearest problem, cleanest dataset, most measurable improvement. Prove the pattern. Build organisational confidence. Then expand.

I don't hire PMs to manage backlogs. I hire PMs to own problems.

Individual development plans

Specific skill gaps tied to each PM's actual problem space — not generic career goals
Weekly PM craft session

45 minutes, rotating facilitator, one real decision worked together. No hierarchy.
Senior IC pathway

Clear mandate, explicit decision rights, feedback structure — not just being left alone
Cross-functional embeds

Engineering, customer success, and sales cycle participation every half

HOW I WOULD MEASURE SUCCESS

At 6 Months

Customer NPS trending up — or a clear diagnosis of why not
Discovery cadence running without me facilitating every session
Roadmap restructured around outcomes, not features
Each PM has a development plan and knows what they own
AI opportunity map complete; first responsible experiment in progress

At 12 Months

At least one AI-enabled capability live, with before/after council workflow data
PM team operating with genuine autonomy — I am coaching, not directing
Roadmap on regular C-level communication cycle
At least one major council segment's renewal friction measurably reduced
Sales team using roadmap as a genuine commercial tool

Why this role. Why now.

I have spent 20 years in complex, regulated, mission-critical environments — banking at CBA, global energy at BP, financial services SaaS at Lumiant, NSW Government across Education, Transport, and Justice. The common thread is not the sector. It is the type of problem: organisations that serve people who depend on them, where the product has to work reliably, and where trust is the foundation of everything.

Datascape serves councils. Councils serve communities. That is a mission I understand from the inside — and it is the kind of work I want to lead.

This is a speculative case study — a genuine projection of how I would approach this role, based on real experience and working methods. It is not hypothetical. It is what I would actually do.

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