SELECTED WORK / 03 · DECISION INTELLIGENCE
Better signals, better decisions
The value of an analytical signal depends on a clear decision, credible evidence and a person who can act on it.
Process before prediction
In a requisition workflow, I mapped hand-offs, approvals and the information people needed to track a request. That process work informed a proposed future state with clearer controls and measures. A December 2025 benefits case set indicative targets: requisition processing from 20 to 10 days and approval time from 96 to 48 hours, a 50% reduction in each measure. Baselines and benefits needed validation during delivery; these figures are targets, not observed results.
The proposed improvement, shown as a target.
Requisition processing
Approval time
What moved the decision forward.
Choose opportunities with a path to action
I prepared a prioritised view of predictive analytics opportunities in student support, assessment, service demand and asset operations. The screen was practical: is there relevant data, an owner, a timely intervention and a proportionate governance route? This was a decision framework for where to investigate next, not a claim that those models were deployed.
Use each data source for what it can support
Related analyses used restricted real feedback and software-usage exports to ask bounded service questions. A separate procurement notebook used curated synthetic records to rehearse delay, approval, anomaly and other modelling questions. Its scores describe constructed examples, not workplace prediction accuracy. The portfolio labels these evidence bases on each case.
Responsible use
I look for a simple baseline, interpretable outputs, privacy controls, validation on appropriately authorised real data and a review owner before any operational decision. Sensitive people or student signals need particular care and must not become automated judgments about individuals. Internal datasets, identities and commercially sensitive measures are not reproduced here.
AI enablement begins with the work
My adoption approach connects people and practical tasks with data, ownership, appropriate controls and measurable value. A good opportunity may call for training, clearer processes or deterministic automation as well as AI.
Read more · Literacy, use cases and responsible adoption
Role-based learning should use safe examples from the work people actually do, including plausible errors and the point at which human review is needed. Use-case assessment considers user pain, readiness, value, data availability, effort and failure consequences. Champions, clinics and maintained guidance help colleagues apply learning. Resistance can reveal workload, trust or accountability problems; attendance alone does not establish capability or benefit.
FORWARD DIRECTION / AI & DATA
From a signal to a supported intervention.
I developed complementary proposals around decision intelligence, predictive analytics and responsible AI adoption. Together they asked how demand becomes a well-framed question, how evidence informs priorities, and how people gain the confidence and support to act.
Read more · Three connected design questions
- Decision intelligence: connect demand, evidence, delivery choices and a value-review owner.
- Predictive analytics: establish whether an earlier signal could support a timely, proportionate intervention.
- Responsible adoption: join role-based learning, practical clinics, inclusion, oversight and feedback.
These were authored strategic proposals informed by delivery experience. They are not evidence that every proposed role, forecasting service or adoption programme was established.
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 connect strategic needs, options and solution evaluation with evidence-based decisions.
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.
Try the Smart Procurement Lab.
Four explainable prototypes: PO delay · Approval bottlenecks · Anomaly review · Carbon impact. Change fictional inputs, compare scenarios and inspect measured synthetic-test results.
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. |
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