FROM PRACTICE / INTO INTERACTION

The BA Service Studio.

Explore forty-eight public teaching activities, assemble a service pack and connect techniques to outputs and acceptance criteria.

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CONSULTANCY / SELECTIVE METHODS

Choose the service the problem needs.

The service-catalogue idea grew from a varied portfolio: discovery, process review, procurement, data requirements, assurance and adoption called for different combinations of analysis. Select activities for the decision and context, then agree their outputs and review points. More artefacts do not automatically mean more value.

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 selecting analysis activities and techniques for the context. This site’s catalogue is an independent adaptation, not a reproduction of BABOK tasks.

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.

THE FULL PRACTICE / SOURCE EDITIONS & AGENT DELIVERY

The complete BA activity catalogue.

Explore all 100 v2 activities, the 56 consolidated v3 services and 101 historical rows. Compare editions, inspect every populated service field and assemble a downloadable pack. These extend the original 48 teaching activities above.

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IIBA BABOK reference ↗ · Persona & Journey Studio · Project Compass · Meeting-agent case study

For BAs, practice leads and stakeholders: use the catalogue to agree services and outputs, then connect discovery, participation, requirements, testing and adoption to a governed agent build. Descriptions and effort values are source-author estimates, not validated delivery promises. No operational agents run here.

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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.