FROM PRACTICE / INTO INTERACTION
BA Project Compass.
Make work visible. Move fictional activities through a workflow, connect them to value themes and inspect the evidence behind completion.
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ACTIVITY / OUTPUT / VALUE
Completion needs an explanation.
My Compass work aimed to make analysis activity and its contribution more visible. Pair a completed activity with a reviewed output and the decision it supported. Activity counts describe work recorded; they do not establish productivity, realised savings or a better service.
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 support planning, monitoring and requirements decisions. The board’s stages are an independent teaching workflow, not an IIBA-prescribed lifecycle.
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.
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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.
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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