FROM PRACTICE / INTO INTERACTION

Persona & Journey Studio.

Twenty-two fictional role-based personas, including six clinical stakeholder journeys. Explore a full layered journey, edit a stage, compare experiences, and connect people, systems, access, evidence and ownership.

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FROM USER CONTEXT / TO DELIVERY

A persona should change the question.

My playbook work connected user groups to goals, touchpoints, friction, accessibility needs and service outcomes. The purpose was to improve discovery and decisions across projects, with journeys informing requirements, acceptance and adoption.

Read more · Keep the connection testable

Trace a user goal through a journey step, an observed difficulty, a proposed requirement and an acceptance scenario. Review the chain with affected users. A synthetic persona can rehearse that method; it cannot replace research, establish someone’s behaviour or stand in for every member of a group.

Explore Business Analysis & Consultancy Foundations · Try the Evidence Chain Studio

EXTERNAL RESOURCES / GLOBAL BA STANDARDS

Business analysis, grounded in shared practice.

Use these references to strengthen elicitation, stakeholder collaboration and the validation of needs behind a journey map.

IIBA · BABOK® Guide, Version 3 — a globally recognized professional standard for business analysis, covering knowledge areas, tasks, competencies and techniques.

Read more · Official resources and how to use them

IIBA · The Business Analysis Standard offers a concise foundation and a free PDF through IIBA. It is a separate, updated resource—not a new numbered edition of BABOK.

BABOK v3 corrections and errata · IIBA KnowledgeHub

Edition check: 9 October 2026. IIBA’s current resources continue to identify BABOK v3. Access the full guide through IIBA’s membership or purchase options. These links support independent learning; they do not imply IIBA endorsement or certification of this site or its tools.

BUSINESS ANALYSIS & CONSULTANCY FOUNDATIONS

A playbook that stays useful.

Open the full-size fictional playbook example ↗

The original work grew from identity and access questions into a broader view of people’s experiences. I led the BA practice around a collaborative playbook: journeys made service dependencies visible, while ownership, support and refresh arrangements helped it remain useful after launch. This public reconstruction uses newly authored fictional examples; it does not expose institutional access rules or staff records.

Read more · Purpose and principles

A persona represents a researched pattern of needs and circumstances. A journey follows that role through a sequence of actions, touchpoints and handoffs. A service blueprint adds the teams, systems and controls supporting that experience. The examples here are hypotheses for learning, not research findings.

Read across a journey to understand the lifecycle, then down a stage to connect the user’s action to supporting work. Include accessibility, interruptions, multiple roles and departures. A person, identity, account, role and persona are distinct: a persona must never be used as an access grant.

Keep source dates, uncertainty, ownership and review triggers visible. Ask people about their experience rather than assigning emotions from demographic assumptions. A journey map helps identify questions for analytics; it is not an event log, a prediction of behaviour or a ready-made ML dataset.

Read more · Governance, support and adoption

Practice	Fictional accountable role	Useful evidence
Maintain persona and journey	Harbour Playbook Owner	Evidence notes, affected users’ feedback, named maintainer and next review
Review changed access or service steps	Harbour Service Owner	Impact review with system and data owners; approval depends on risk, not percentage of text changed
Support everyday use	Harbour Support Lead	Intake route, content specialist, technical escalation and agreed response expectations
Publish and communicate	Harbour Content Steward	Version, change note, permitted audience and accessible guidance
Review value	Harbour Practice Lead	Useful task completion, documented project reuse and resolved feedback; views alone are insufficient

A suggested campaign moves through four reviewable cycles: establish the hub and ownership; demonstrate use and invite feedback; embed references into discovery and delivery; then review adoption and refresh what has changed. Timeboxes, targets and review cadences should be agreed locally.

Before a workshop, agree the question and invite people affected by the service. During it, walk a normal case and an exception, record disagreement and test assumptions. Afterwards, confirm actions, owners and review dates. Technical faults, incorrect content and urgent safety or privacy concerns need distinct routes.

Read more · What the source pack contributes

The persona workbook and two overview decks supply the layered journey pattern and launch story. The description and use-case documents explain its intended purpose. The stakeholder matrix informs engagement and RACI. The support model supplies routing and maintenance questions. The governance and refresh documents add ownership, review and change controls; the agile framework and awareness workbook add iterative adoption.

The source versions contain different role groupings, persona counts and review intervals. They are historical working artefacts, not a single current inventory or proof that every proposed outcome occurred. This reconstruction removes individual contacts, internal URLs, access entitlements, issue identifiers and draft approvals. The original attachments are not public downloads.

Read the BA capability story → · Connect the journey to a delivery plan → · Explore the six-step improvement framework →

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EXPLAINABLE AI & DATA / READ THE METHOD

Clear logic behind the working draft.

These public workbenches organise inputs through authored rules, templates and calculations. A generated worksheet is a draft to review, not an autonomous consultant.

Follow the reasoning.

FrameState the need and evidence
OrganiseApply the selected rules or template
InspectReview the draft, diagram or comparison
DecideA person owns the next action

Explore the building blocks.

AI Capability5

Algorithm1

Data Science Pipeline2

Evaluation & XAI1

Responsible AI1

Visualisation1

Number of concepts in this guide, not performance or usage measures.

Explore the explanation matrix · 11 concepts

Concept categoryFind a term or example

11 concepts shown

On small screens, scroll the table sideways. “Learning context” explains a related idea; it does not mean that method runs on this page. Deep learning is the standard term for “deep ML”.

Technical term	Technical description / definition	In plain English	How this page uses it
Rule-based logicAlgorithm	Explicit authored conditions map inputs to outputs without fitting parameters from training examples.Primary reference ↗	A transparent set of if-this-then-that instructions.	Running here Browser controls select authored methods and structure the visitor’s inputs; they do not train a model.
Data science pipelineData Science Pipeline	An ordered, reproducible sequence for acquiring, validating, preparing, analysing and evaluating data.Primary reference ↗	A chain from a question and source to an answer someone can check.	Method guide The workflow connects evidence, analysis, a proposed action and an accountable reviewer.
Visual encodingVisualisation	Mapping values or categories to marks, position, length, colour and labels.Primary reference ↗	Make the comparison visible, with a scale and a clear label.	Running here Diagrams and summaries represent entered information or labelled illustrative examples.
Data quality checksData Science Pipeline	Validation of completeness, allowed values, uniqueness, types and consistency with expected constraints.Primary reference ↗	Check missing, repeated and impossible entries before trusting a chart.	Method guide Completeness and review prompts help expose gaps; they cannot certify the underlying evidence.
Explanation versus causal proofEvaluation & XAI	A model explanation describes selected behaviour or contributions; it does not establish causal effects or full reliability.Primary reference ↗	A reason for this output is useful, but it is not proof that the model is right.	Responsible use A structured output shows its method, but the visitor must check the claims and context.
Human oversight and consentResponsible AI	People retain appropriate review authority, permission controls and correction routes for data use and outputs.Primary reference ↗	Keep someone able to question, correct or stop the result.	Responsible use People review and adapt drafts before any operational use.
Large language model (LLM)AI Capability	A language model trained on substantial data to estimate and generate token sequences from context.Primary reference ↗	A fluent drafting partner whose statements still need checking.	Learning context An LLM can assist drafting in an approved environment; these public rule-based workbenches do not silently call one.
Machine learning (ML)AI Capability	Methods that estimate patterns or parameters from examples to support inference on other inputs.Primary reference ↗	Learn a pattern from examples, then check whether it helps with new ones.	Learning context ML learns from examples. Read the page-specific methods below to see whether any model runs here.
Deep learningAI Capability	Machine learning using neural networks with multiple representation-learning layers.Primary reference ↗	A layered model learns useful representations; more layers do not guarantee a better answer.	Learning context Deep learning is an AI capability, not a label for every chart. No deep model is implied by this explanation panel.
Natural language processing (NLP)AI Capability	Computational methods for representing, analysing or generating human language.Primary reference ↗	Help a computer work with words, while remembering that context matters.	Learning context Text search, translation, thematic classification and generation are different language tasks; their implementation is identified separately.
Sentiment analysisAI Capability	A language-analysis task that estimates an expressed attitude, often as polarity or sentiment categories.Primary reference ↗	Estimate the tone of words—not the private feelings or personality of their author.	Learning context Sentiment estimates expressed tone. It must not be confused with topic classification or a diagnosis of a person.