BAN2100 · Module 6 · Introducing Prescriptive Analytics
Status: Retrospective (documented post-build) Program: BBA Degree level: Undergrad Documented by: Claude Code Date: 2026-04-24
Module 6 is the synthesis capstone of the BAN2100 analytics continuum. Having covered descriptive (M3), diagnostic (M4), and predictive (M5) analytics in prior modules, learners now encounter prescriptive analytics and must demonstrate the ability to classify all four analytics types, interpret multi-panel business data, formulate a prescriptive recommendation grounded in evidence, and select appropriate analytics tools — applying the full continuum in an integrated scenario.
| CLO | Text | Pillar mapping |
|---|---|---|
| CLO-1 | Define the main concepts and methodologies used in data analytics | Domain |
| CLO-2 | Differentiate between types of data and identify appropriate analytical techniques for each | Domain |
| CLO-3 | Analyze datasets to uncover patterns and trends that drive business insights | Reasoning |
| CLO-4 | Analyze the impact of data on business decision-making and operations | Domain + Reasoning (CLO-4 formula) |
| CLO-6 | Apply data analytics tools to real-world business scenarios | Contribution |
Note: CLO-5 (data ethics) is absent from the course at build time (GAP-BAN2100-001) and is not assessed in this module.
Status: Retrospective draft — generated by Nexus based on module learning intent and CLO mappings. Requires LXD review and sign-off before these descriptors are wired into the grading prompt (#28).
| Rating | Descriptor |
|---|---|
| Meets Expectations (90–100%) | Correctly classifies all four analytics types (descriptive, diagnostic, predictive, prescriptive) with definitions accurately applied to the Tesco dashboard data; identifies an appropriate prescriptive analytics tool for the scenario with correct justification referencing the specific data type and decision context (CLO-1, CLO-2). |
| Mostly Meets (80–89%) | Classifies three of four analytics types correctly with appropriate definitions; tool selection is appropriate but justification is incomplete or misidentifies one feature of the recommended tool. |
| Somewhat Meets (70–79%) | Classifies two analytics types correctly; tool selection is plausible but unjustified or supported by incorrect feature identification; one analytics type is mislabeled or conflated with another. |
| Does Not Meet (<70%) | Cannot correctly distinguish the four analytics types; tool selection is inappropriate for the scenario or absent; definitions are absent or fundamentally incorrect. |
| Rating | Descriptor |
|---|---|
| Meets Expectations (90–100%) | Advisory brief traces a clear evidence chain from specific Tesco dashboard metrics to the prescriptive recommendation; each analytical claim cites a named data point or output; brief explicitly addresses why the recommended action is preferable to at least one alternative (CLO-3, CLO-4). |
| Mostly Meets (80–89%) | Evidence chain mostly present; most claims are supported by data references; one or two logical gaps where conclusions are asserted without citing specific dashboard data. |
| Somewhat Meets (70–79%) | Recommendation is stated but evidence chain is weak — data referenced generally (e.g., "the sales data shows…") rather than with specific figures; does not address alternatives or competing interpretations. |
| Does Not Meet (<70%) | Recommendation is unsupported; no specific Tesco data cited; reasoning is circular (restates the recommendation as its own justification) or absent. |
| Rating | Descriptor |
|---|---|
| Meets Expectations (90–100%) | Identifies a non-obvious business implication from the data synthesis that goes beyond the scenario prompts — e.g., surfaces a risk, a constraint on tool deployment, or an opportunity visible only by integrating data across analytics types; frames the recommendation in terms of organizational decision impact (CLO-6). |
| Mostly Meets (80–89%) | Adds independent analytical framing beyond restating dashboard outputs; recommendation shows awareness of Tesco's business context and constraints. |
| Somewhat Meets (70–79%) | Advisory brief restates the scenario prompts without independent contribution; reads as a summary of the MCQ steps rather than an original advisory position. |
| Does Not Meet (<70%) | No evidence of analytical contribution; brief paraphrases given content without adding insight or framing. |
These descriptors were drafted retrospectively by Nexus (Claude Code) on 2026-04-27 based on existing CLOs and scenario content. They require LXD review and approval before being wired into the grading prompt (punch list #28).
| Role | Name | Date | Status |
|---|---|---|---|
| LXD (review & approve) | Fadl | Approved | |
| Head of LD (final approval) | Pending |
Once approved, update Status above to "Approved" and add the approver names and dates here. Changes after approval require a new sign-off round and a version bump on the PRD.