Is BADIR an AI model?

No. It organizes analytical work and can be used with many methods.

Does every project need machine learning?

No. The question may be answered through simpler analysis or may require collecting better evidence first.

Can the team revisit earlier stages?

In practice, new evidence may require revising the question or plan; document why the scope changed.

AI Glossary / Data science and analytics / Advanced

BADIR

Definition

BADIR is Aryng's proprietary analytics framework for moving from a business question to a recommendation. The acronym represents Business question, Analysis plan, Data collection, Insights and Recommendations. It organizes an analytical project around a decision and its intended impact. It is a workflow framework, not a statistical algorithm or evidence that a recommendation will necessarily succeed.

What it means

An organization can produce many charts without resolving the question that prompted the work. BADIR provides a sequence for connecting the question, the evidence needed to answer it and a proposed action. Aryng describes the analysis-planning stage as hypothesis-driven, with relevant data gathered after the plan is defined.
The practical distinction is between a request for an output and a question that could change a decision. “Build a dashboard” specifies an output. “Which part of our registration process should we investigate before the next release?” identifies a decision context. The investigation still needs suitable methods and reliable evidence.

How it works

Clarify the business question, then plan the analysis, collect relevant data, derive insights and communicate recommendations. The exact analytical method depends on the question rather than being prescribed by the acronym.
For a practical implementation, record the decision owner, definitions, assumptions and evidence gaps. Treat hypotheses as possibilities to test, not conclusions to defend. When the data contradicts an assumption, revise the plan and explain the change.
Keep findings separate from proposed actions. A finding may describe an observed pattern; a recommendation additionally considers what can be changed, the expected consequences and how the outcome will be checked. Following the stages does not turn an observational association into a causal result.

Three practical examples

A learning website asks which registration step warrants investigation. Its team defines completion consistently, examines relevant events and recommends a focused usability test rather than immediately redesigning every page.
A support manager wants to reduce repeat contacts. Analysts clarify what counts as a repeat, examine a suitable period and distinguish repeated unresolved issues from legitimate new questions before proposing a process change.
A publisher considers adding audio summaries. The team identifies the decision, available evidence and resource constraints, then recommends a limited trial with explicit measures instead of treating general interest in audio as proof of demand.

Think of it this way

BADIR resembles a route from a clearly stated destination to a justified next move. The route organizes work but cannot guarantee arrival. If a critical assumption changes, continuing through the remaining steps without revisiting the question can produce an orderly answer to the wrong problem.

Worked example

Suppose a fictional learning site wants to investigate a registration drop. In one period, 1,000 people begin registration and 700 finish. In a comparable later period, 1,000 begin and 600 finish. Completion falls from 70% to 60%, a decrease of 10 percentage points. Relative to the earlier rate, the decrease is 10 / 70, approximately 14.3%.
Using BADIR as an organizing structure, the business question is which step should be investigated before the next release. The analysis plan defines the two periods and checks tracking consistency. The team collects step-level events and finds that the extra observed abandonment is concentrated around confirmation.
That finding supports investigating confirmation delivery and usability. It does not prove either caused the decline: traffic composition, measurement changes or other factors may matter. The recommendation is a targeted investigation followed by an appropriately designed test. These invented figures establish neither revenue nor causal effects.

Illustrative case study

In a fictional project, Larch Learning receives a request for an AI-powered retention model. Before choosing technology, the team asks what action the model would support. Managers explain that they want to decide whether to improve onboarding or expand reminder messages.
The analysts document competing explanations for early disengagement and identify the evidence available for each. They discover that a change in event tracking makes part of the historical comparison unreliable. The immediate recommendation is to repair measurement and run a small onboarding study, with reminder changes considered after that evidence is reviewed.
Larch uses the five-stage structure to make the reasoning and the next decision visible. The project does not yet demonstrate improved retention, and a predictive model is not required merely because the original request mentioned AI. This fictional case illustrates scoping.

Common misconceptions and mistakes

BADIR is not a guarantee of a particular financial return, speed improvement or analytical accuracy. Claims made in marketing materials should not be treated as universal outcomes of applying the framework.
A hypothesis-driven plan should not become confirmation bias. Teams need evidence that could contradict their preferred explanation. Likewise, collecting only relevant data requires a sound understanding of relevance; an overly narrow plan can omit important alternatives.
Recommendations should not simply repeat findings. “Completion declined” is a finding. A useful recommendation identifies a next action, its rationale and what remains uncertain. Implementation and outcome review still require ownership beyond producing the analysis document.

Business use

Use BADIR to structure conversations between decision-makers and analysts, especially when requests arrive as vague demands for reports, dashboards or models. The framework can provide a shared outline for a project brief and a final recommendation.
Define success in terms of the decision and its consequences, not the number of charts produced. Preserve assumptions, data definitions and unresolved questions so another reviewer can follow the reasoning. For consequential decisions, the required evidence quality comes from the decision itself; a named framework does not replace appropriate experimental design, professional judgment or review.

Check your understanding

What do the five letters represent?

Answer: Business question, Analysis plan, Data collection, Insights and Recommendations.

Why define the decision before selecting a model?

Answer: The intended action determines what evidence and methods are useful.

Does the worked example prove confirmation caused the decline?

Answer: No. It identifies a place to investigate and leaves alternative explanations open.

Related terms

Business analytics
Hypothesis
Data collection
Causal inference
decision science
analysis plan
insight
recommendation
key performance indicator

Learn next

Rewrite a vague dashboard request as a decision-focused question. List competing explanations, the evidence needed to distinguish them and the action that each possible finding would support.

Sources

Aryng, Data Science Process: Why should you use BADIR?: primary explanation of the five stages and hypothesis-driven planning. https://blog.aryng.com/data-science-process-why-should-you-use-badir/ Aryng, BADIR advantage: proprietary framework description consulted through indexed text; direct retrieval was unavailable. https://www.aryng.com/aryng-BADIR-advantage All scenarios and calculations are original illustrations. Provider marketing performance claims are not adopted as established general results.

Previous: Bacterial colony optimization Next: Bag of words