Case StudyMarketing Analytics & MarTech Modernization

Running a marketing-analytics modernization as one program, charter to benefits.

I led the end-to-end modernization of a fragmented marketing-analytics and MarTech estate — standing up the program charter, governance, and agile delivery, then steering it through architecture, UAT, cutover, and benefits realization. This is the case study where the program-management operating system is the story.

Representative composite. Company name (“Nexora”) is fictional and all figures are illustrative — this page shows how I run a program of this shape, not confidential client data.

Case Study Snapshotproblem → operating model · at a glance
The problem

Marketing ran on a patchwork of disconnected tools and hand-built spreadsheets. Campaign, web, and CRM data never reconciled, reporting was late and contested, and no one owned a single source of truth. The gap wasn't a missing dashboard — it was the absence of a program to fix it properly.

How I ran it

I framed it as a governed program: a charter and steering model, a phased roadmap delivered in agile sprints on Jira, a modernized data-and-MarTech architecture, RAID-driven risk control, staged UAT, a controlled cutover with hypercare, and a benefits-realization plan that outlived go-live.

Program CharterSteering & RAIDAgile · JiraCloud Data Warehousedbt / SQLGA4 & campaign sourcesCDP / MarTechTableau · Power BI

Program at a glance

One program, thirteen workstreams, run as a single governed delivery.

Artifact · Program metadatastructure, not vanity metrics
My roleTechnical Program Manager — end-to-end owner, charter to benefits
Program shape13 lifecycle workstreams: initiation, governance, requirements, planning, architecture, agile delivery, data engineering, MarTech, BI, testing/UAT, risk & change, cutover, closure & benefits
Delivery modelAgile / Scrum, sprint cadence tracked in Jira, stage-gated at each phase
DomainMarketing analytics + MarTech + BI modernization
GovernanceExecutive steering committee → program lead → workstream leads, weekly RAID
Outcome frameTrusted single source of truth for marketing performance, adopted and owned by the business

I've deliberately left hard KPIs off this table. On a modernization like this, the credible number is the one the business can defend six months later — so those get filled in from measured benefits, not launch-day estimates. What follows is the operating system I used to get there.

Governance & stakeholders

The operating model, before a single sprint

Modernizations don't fail on technology — they fail on unclear ownership and decisions that never get made. So the first artifact wasn't an architecture diagram; it was the governance model: who decides, who's consulted, and at what cadence.

Why this first

Without a named decision forum, a program defaults to whoever escalates loudest. A steering committee with a standing agenda is what let me protect executive time, keep workstreams unblocked, and make trade-off calls in days instead of weeks.

Artifact · Governance tiersdecision rights & cadence
TierWhoOwnsCadence
Steering committeeExec sponsor + function headsScope, budget, go/no-go, cross-function trade-offsBi-weekly, escalation on demand
Program lead (me)TPMRoadmap, dependencies, RAID, delivery cadence, reportingDaily standups + weekly RAID
Workstream leadsData, MarTech, BI, QA, business readinessSprint delivery inside their workstreamSprint (2-week) + joint planning
Business ownersMarketing ops & regional leadsRequirements sign-off, UAT, adoptionSprint reviews + mandatory UAT gates

The pattern: match the forum to the decision. Reversible calls stayed with workstream leads; anything touching scope, budget, or sequence went to steering with a one-page recommendation, not an open question.

Roadmap & agile delivery

A phased roadmap, delivered in sprints

The roadmap gave stakeholders the shape of the journey; the sprint plan made it real. Phases were stage-gated — nothing moved forward until the previous gate's exit criteria were signed off.

01DiscoveryCurrent-state audit, stakeholder mapping, requirements
02FoundationTarget architecture, environments, delivery setup
03BuildData pipelines, models, MarTech & BI in sprints
04ValidateUAT, reconciliation, business sign-off
05CutoverControlled go-live + hypercare
06BenefitsAdoption, benefits tracking, handover
How I ran the sprints

Every workstream fed one Jira backlog under program epics mapped to the roadmap phases. Two-week sprints, a shared definition of done that included documentation and test evidence, and a dependency board I reviewed daily so a blocker in data engineering never silently stalled BI.

Architecture

From patchwork to a governed data-to-activation flow

The target state wasn't just "a warehouse." It was a clean line from raw marketing sources, through a modeled single source of truth, out to both BI reporting and MarTech activation — so the same trusted numbers drove the dashboard and the campaign.

Current state
Disconnected toolsManual extractsSpreadsheetsContested reports
Target state
Campaign · web · CRM sources Cloud ingestion Modeled warehouse (dbt / SQL) Semantic layer
BI & reporting (Tableau / Power BI) MarTech activation (CDP / campaign)

My job here was less about writing the transforms and more about the decisions around them: what became the source of truth, how conformed dimensions were governed, and where the boundary sat between the engineering team's build and the business's definitions. I owned the sequencing and the sign-offs; the engineering workstream owned the pipelines and models.

My role — program
  • Roadmap, sequencing & dependencies
  • Governance, RAID, steering reporting
  • Requirements & UAT sign-off gates
  • Cutover controls & go/no-go
Team — engineering
  • Pipelines, data models, dbt/SQL
  • MarTech & CDP configuration
  • BI build in Tableau / Power BI
  • Automated tests & data quality checks

Risk, change & quality

How I kept it from drifting

Two disciplines ran the whole way through: a live RAID log scored by probability × impact and reviewed weekly, and a staged UAT that treated business sign-off as a gate, not a formality.

Artifact · RAID scoringprobability × impact
SeverityTriggerMy response
HighThreatens scope, date, or data trustNamed owner, weekly steering visibility, mitigation tracked to close
MediumRecoverable if caught earlyReviewed weekly in RAID, contingency identified
LowContained; monitoring sufficientLogged, bi-weekly spot check

Change control

Scope changes went through a lightweight change log with an impact assessment — so "just one more field" was a visible trade-off against the date, not a silent scope creep.

Staged UAT

Business owners tested against real scenarios and signed off per workstream. Reconciliation against the legacy numbers had to pass before anything was called done.

Incident readiness

A defined severity scale and response path meant that when something broke in build or hypercare, the question was "who and by when," not "what do we do now."

Cutover & benefits

Landing it — and making it stick

Go-live is the risky moment and the anticlimactic one: if the program was run well, cutover is boring. Then the real test begins — does the business actually adopt and trust the new source of truth?

Artifact · Cutover approachcontrolled, reversible
  • Go/no-goExplicit exit criteria and a sign-off forum — not a date we backed into.
  • Controlled switchLegacy kept available as fallback until the new reporting was validated in production.
  • HypercareA defined support window with fast triage while adoption ramped.
  • HandoverDocumentation, runbooks, and ownership transferred to the business and BAU teams.
Benefits realization

The program didn't close at go-live. I set up benefits tracking against the original charter goals — adoption of the new reporting, retirement of the shadow spreadsheets, and cycle-time on marketing reporting — so value was measured, not assumed. These are the figures that belong on the scoreboard, once they're real.

The throughline: this was a program-management case, not a tooling one. The tools mattered, but what made it work was the operating system around them — governance, cadence, risk discipline, and a benefits mindset that treated go-live as the middle, not the end.

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