SELECTED WORK / 02 · CAPABILITY LEADERSHIP
Business Analysis Capability as an Internal Consultancy
I led a team of 10 analysts as an internal consultancy: helping colleagues define the real problem, navigate change and make sound decisions across services and projects.
Internal consultants, close to the work
The team was a partner to project teams, service owners, architects and operational colleagues. We clarified outcomes, challenged assumptions, mapped processes and data, and translated complex needs into choices that could be tested. I developed the service model and coached analysts to use a common quality bar without turning every project into the same template.
Scale and value
During my 2022–2026 tenure at Queen Mary University of London (QMUL), I supported a digital transformation portfolio of approximately £16m a year, across projects valued from £50k to £2.5m. These are the values of work supported; budget ownership and delivery sat with the relevant sponsors and teams.
The in-house Business Analysis Capability offered continuity, capacity and a common quality standard. My former line manager estimated that the service delivered comparable capability, capacity and quality at roughly half the cost of day-rate contractor cover. This is a comparative estimate, not an audited cash saving.
One in-house service, reusable across a substantial portfolio.
A service model people could use
How the BA Capability held together.
The 2022 vision and team boards connected people, a common method and evidence of value. The diagram preserves the operating logic without displaying internal boards or individual workloads.
What I put in place
By October 2025, I had documented a catalogue of more than 100 analysis activities across eight delivery gates, with entry cues, techniques, outputs, effort and complexity prompts. An accompanying import guide made the catalogue ready for workflow configuration.
I set a practical modelling standard from value-chain views to procedures, with ownership, versioning, peer review and links to requirements, tests and benefits. The team approach favoured the lightest useful model for each decision.
I shaped governance for a shared persona playbook: BA ownership, review triggers, change approval and a phased awareness plan. The framework was designed to keep journeys and system touchpoints current as services changed.
What the internal consultancy connected
From an “as is” process to an owned change.
My process re-engineering diagram set out a practical sequence. Teams still had to adapt its depth to the decision, risk and service context.
- 01DefineMap the current work, roles, rules and technology.
- 02AnalyseFind root causes and strategic gaps.
- 03IdentifyEvaluate improvement opportunities.
- 04DesignChoose a future process and its controls.
- 05DevelopSpecify, communicate and test changes.
- 06ImplementSequence adoption and monitor performance.
How data became part of the BA Capability
My QMUL-funded MSc at Northeastern University London brought a personal learning journey into this work. In a staff-apprenticeship film with colleagues, I shared my experience to encourage others to study. The accompanying reflection traces my growing confidence with mathematics and the move from course concepts to team use cases.
In August 2024, I drafted a data-requirements method that put ownership, flows, quality, migration, security and validation into discovery and traceability. A July 2025 strategy then adapted those questions to four types of work—workplace and space operations, learner support, physical assets and service management. Analysts could ask what data a change needed, how it would be governed and which evidence would show that the intended service worked.
The resource planner, activity tracker and service catalogue were separate operational and knowledge assets. I outlined requirements for a portfolio tool to bring requests, allocation, stage activity and feedback into a filtered view. That was a target integration; it does not mean a unified platform or predictive staffing model went live. Explore the eight underlying asset categories ↗
The December 2025 AI and Data Academy pathway extended the practice into learning: a planned progression from common data language to AI concepts, analytical thinking and Python exercises. It complemented coaching and peer review, without treating course attendance as proof of operational AI capability.
See the operating model and milestones in view ↗
Milestones in the capability
Data requirements in discovery. I drafted a method for identifying sources, owners, quality, migration, privacy and test evidence early. In discussion with data architecture colleagues, the guidance was refined around project-specific requirements rather than a universal migration template.
Activity and value visibility. I developed a Jira-oriented Compass board prototype to record activities by stage, technique and value theme, with a rollout plan and dashboard measures. The documentation establishes the prototype and proposed rollout; it does not verify that every planned dashboard or teamwide workflow went live.
Read less
Make recognition part of everyday practice.
Alongside technical learning, I co-led planning for a staff-survey action on feedback and recognition. The aim was a lighter, more visible rhythm of non-monetary recognition, with participation, clarity and consistency as measures to review. It treated capability as a social practice: people need opportunities to learn, contribute and have their work acknowledged.
Read more: keep the human judgment
The action plan proposed regular recognition moments and a visible record of contributions. Those were planned practices, not evidence of a measured improvement in morale. The separate feedback analytics prototype explored a selective log; it was not an automatic appraisal or reward system.
My learning catalogue and team pathway serve a related purpose: make analytical methods understandable, provide room to practise, and judge usefulness through application rather than course attendance alone.
The lasting value
A BA Capability can act as a trusted internal consultancy when its methods are visible, its analysts are supported and its advice is tied to decisions. The examples here describe my leadership and authored frameworks at a suitable public level. Personal records, private correspondence and colleagues’ contact details remain private. Supporting Python code is privately stored and maintained by me; I’m happy to answer questions about the methods and their limits.
From shared language to practical confidence
- FOUNDATIONSBuild shared understanding of data, AI and its limits.
- ANALYTICAL THINKINGExplore the data-science lifecycle and reviewed use of generative tools.
- PRACTICEApply Python and analysis methods to bounded learning exercises.
Source basis: The private vision and leadership framework, V2MOM and team-hub exports, resourcing and portfolio-tool requirements, data guidance, academy materials, and process-management documents inform this account. The six-stage process illustration is supported by both the supplied Visio diagram and the re-engineering framework. Original workplace files and access links are not published.
LEADERSHIP / CLOSE TO DELIVERY
The operating model behind the artefacts.
I used demand and resource records to understand what a project needed: its problem, timing, required analysis, complexity and available capability. Allocation considered both delivery needs and supported development opportunities. Coaching, peer review and communities of practice then fed lessons back into the service offer.
Read more · Leadership and hands-on contribution
My involvement varied by assignment. On asset management, workplace systems and identity-and-access work, I provided Lead BA resourcing, oversight and coaching to assigned analysts. This included challenging definitions, ownership, user journeys, exceptions and acceptance criteria. Those contributions are distinct from being the direct project analyst or implementing the underlying platform.
The wider operating model linked demand, prioritisation, allocation, capability, delivery, review and learning. Its individual records and working methods should not be confused with a fully integrated or predictive staffing platform.
Explore Business Analysis & Consultancy Foundations · Try the Evidence Chain Studio
EXTERNAL RESOURCES / GLOBAL BA STANDARDS
Business analysis, grounded in shared practice.
Use these references when developing a BA practice, choosing techniques and reviewing the value delivered to stakeholders.
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.
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.
Number of concepts in this guide, not performance or usage measures.
Explore the explanation matrix · 11 concepts
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. |
No matching concepts. Choose another category or clear the search.
