Questions and Analytical Thinking
How effectively you frame business questions, select analytical approaches and connect analysis with the decision that must be made.
INDENTRA Business Analytics Academy
Business Analytics Skills Assessment
Assess your business analytics capability across seven connected competency domains, identify strengths and development priorities, and translate the results into a focused learning pathway.
A structured capability diagnostic
Understand how your business analytics capability is distributed across analytical thinking, data, modelling, communication, prediction, decision support and responsible AI.
The assessment uses 35 applied self-rating statements across the Business Analytics Academy capability system—from business questions, statistics and data quality to SQL, visualisation, forecasting, financial models, optimisation, analytics strategy and responsible AI.
- 35 statements grouped into seven practical business analytics competency domains.
- Five-point behaviour-based self-rating scale focused on applied capability.
- Overall profile, category scores and a visual radar chart.
- Text interpretation identifying strengths, priority gaps and relevant Business Analytics Academy pathways.
Seven competency domains
Assess the connected capabilities required to turn data into trustworthy insight, defensible decisions and measurable value.
The profile follows the current Business Analytics Academy capability system, combining analytical thinking, statistics, data quality, SQL, visualisation, business intelligence, forecasting, financial modelling, optimisation, analytics strategy and responsible AI.
Data Literacy and Statistics
Your ability to interpret data, variation, statistical evidence and uncertainty without overstating what the analysis demonstrates.
Data Quality and Governance
How effectively you prepare and govern data so analytical outputs are traceable, trustworthy and fit for the intended decision.
Structured Query Language and Data Modelling
Your capability to query, combine and structure data into reliable analytical datasets and reusable models.
Visualisation and Performance
How effectively you turn analytical evidence into decision-focused dashboards, performance measures and clear narratives.
Prediction and Decision Analytics
Your ability to forecast outcomes, build decision models and evaluate alternatives under assumptions, uncertainty and risk.
Optimisation, Strategy and Artificial Intelligence
How effectively you use optimisation, analytics operating models and AI-enabled methods to scale analytical value with appropriate governance and accountability.
Your assessment
Business Questions, Analytical Thinking and Decision Context
Rate what you can currently do in practice—not what you know in theory or expect to learn.
Competency 1 of 7
Business Questions, Analytical Thinking and Decision Context
How effectively you frame business questions, select analytical approaches and connect analysis with the decision that must be made.
1. I can translate a business problem, opportunity or performance issue into a clear analytical question and define what decision the analysis must support.
2. I can distinguish descriptive, diagnostic, predictive and prescriptive analytical needs and select an approach that fits the question rather than the available tool.
3. I can define relevant measures, success criteria, assumptions and decision thresholds before beginning detailed analysis.
4. I can identify where analytical evidence is insufficient, biased or too uncertain to support a confident recommendation.
5. I can explain the difference between an analytical finding, an interpretation and a business recommendation and keep those distinctions visible in decision discussions.
Please answer all five statements in this competency before continuing.
Competency 2 of 7
Data Literacy, Statistics and Analytical Reasoning
Your ability to interpret data, variation, statistical evidence and uncertainty without overstating what the analysis demonstrates.
6. I can distinguish data types, measures, distributions and summary statistics and explain when common measures such as averages may be misleading.
7. I can interpret variation, outliers, sampling and uncertainty and recognise when apparent patterns may be unstable or unrepresentative.
8. I can distinguish association or correlation from causation and avoid making causal claims that the evidence does not support.
9. I can challenge analytical claims by examining definitions, denominators, samples, assumptions, missing context and alternative explanations.
10. I can communicate statistical or analytical uncertainty in language that decision-makers can understand without either hiding uncertainty or making the analysis sound unusable.
Please answer all five statements in this competency before continuing.
Competency 3 of 7
Data Preparation, Quality, Lineage and Governance
How effectively you prepare and govern data so analytical outputs are traceable, trustworthy and fit for the intended decision.
11. I can assess data fitness for use by considering completeness, validity, consistency, uniqueness, timeliness and relevance to the analytical question.
12. I can profile data before cleaning or transforming it and use evidence to decide how missing values, duplicates, outliers or inconsistent coding should be treated.
13. I can document data transformations, business rules and assumptions well enough for another analyst to understand how an analytical dataset was produced.
14. I can trace important analytical data from source through transformation to output and identify ownership or stewardship for significant quality issues.
15. I can incorporate privacy, access, retention, security and responsible-use considerations into data preparation rather than treating governance as a separate final check.
Please answer all five statements in this competency before continuing.
Competency 4 of 7
Structured Query Language, Data Structures and Analytical Data Modelling
Your capability to query, combine and structure data into reliable analytical datasets and reusable models.
16. I can interpret relational data structures, keys and relationships well enough to understand how business records should be combined for analysis.
17. I can write or review SQL queries that filter, join, aggregate and derive data while checking that the result still answers the intended business question.
18. I can recognise common query problems such as duplicate amplification, incorrect joins, missing records or inappropriate aggregation.
19. I can structure analytical datasets or data models so definitions, dimensions, measures and relationships support consistent reporting and analysis.
20. I can validate query or modelling outputs against source totals, business rules or independent checks before relying on them in decision support.
Please answer all five statements in this competency before continuing.
Competency 5 of 7
Data Visualisation, Business Intelligence and Performance Analytics
How effectively you turn analytical evidence into decision-focused dashboards, performance measures and clear narratives.
21. I can select charts and visual forms according to the analytical question, comparison and audience rather than personal preference or decoration.
22. I can design dashboards that provide context, trends, targets, exceptions and relationships rather than displaying disconnected measures.
23. I can define KPIs and performance measures that connect operational evidence with objectives, accountability and decision action.
24. I can identify misleading scales, poor denominators, visual clutter or inappropriate comparisons that could distort interpretation.
25. I can communicate analytical findings through a concise narrative that distinguishes evidence, implication, uncertainty and recommended action.
Please answer all five statements in this competency before continuing.
Competency 6 of 7
Forecasting, Financial Modelling and Decision Analytics
Your ability to forecast outcomes, build decision models and evaluate alternatives under assumptions, uncertainty and risk.
26. I can select forecasting or predictive methods according to the business question, available data, horizon, assumptions and required decision accuracy.
27. I can evaluate predictive models using appropriate performance measures and distinguish in-sample fit from useful out-of-sample prediction.
28. I can build or review financial models that make cash flows, value drivers, scenarios, assumptions and sensitivities transparent.
29. I can use scenario, sensitivity and risk analysis to show how decisions change when key assumptions or uncertain inputs change.
30. I can compare alternatives using analytical evidence while recognising model limitations, decision criteria and non-quantifiable factors that remain material.
Please answer all five statements in this competency before continuing.
Competency 7 of 7
Prescriptive Analytics, Analytics Strategy and Responsible Artificial Intelligence
How effectively you use optimisation, analytics operating models and AI-enabled methods to scale analytical value with appropriate governance and accountability.
31. I can formulate prescriptive or optimisation problems using objectives, decision variables, constraints and trade-offs that reflect the real business decision.
32. I can use simulation or optimisation results to support action while testing sensitivity, feasibility and the consequences of model assumptions.
33. I can define analytics priorities, roles, governance, operating models and value measures so analytical capability scales beyond isolated reports or individual experts.
34. I can evaluate AI-enabled analytical outputs for validity, bias, privacy, explainability, security and fitness for the decision being supported.
35. I can define where human judgement, validation, override rights and accountability remain necessary in AI-enabled analytical decisions.
Please answer all five statements in this competency before continuing.
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Overall self-assessed profile
Your business analytics profile
Assessment result
Capability profile
See how your capability is distributed across the seven domains.
The radar chart makes balance visible. A strong overall result with one or two lower domains may indicate a focused development need rather than a general business analytics capability gap.
Development priorities