NCFE Level 4 Diploma Unit 1: Legislation and security standards applied to data analytics A/651/0924 Assignment Brief

Unit 1 Legislation And Security Standards Applied To Data Analytics Assignment Brief

Qualification: NCFE Level 4 Diploma: Data Analyst (HTQ)

Unit Code: Legislation and security standards applied to data analytics A/651/0924

Assessment Type: Internally assessed portfolio task

Submission Format: Written report with appendices and references

Word Guide: Approximately 2,500–3,500 words (excluding appendices)

Learning Outcomes

LO1 – Explore relevant legislation and their influence on the safe use of data

LO2 – Investigate the impact of relevant security standards, security frameworks and organisational policies and procedures on data management activities.

LO3 – Examine ethical data principles and the role of artificial intelligence (AI) in the collation and utilisation of data

LO4 – Demonstrate effective use and compliance of data systems when securely collating and utilising data

Submission Guidance

Submit a single PDF or Word file with clear section headings and any supporting appendices. Use the area beneath the marking rubric to start your report.

Ensure academic integrity, reference external sources where used, and comply with data protection policies

Command Word Mini‑Guide (useful for your writing)

• Describe = detailed information presented logically.
• Explain = give reasons and show understanding.
• Define = state precise meaning/scope.
• Outline = key points only, enough to show coverage.
• Explore = consider broadly and draw out relevant points.
• Consider = think carefully and show how views formed.
• Assess = judge impact using evidence/reasoned judgement.
• Critically evaluate = weigh pros/cons and limitations to form a convincing judgement.
• Demonstrate = show practical application with evidence.

Independent Study Assignment: WellCare+ Patient AI

Scenario

Welcome to WellCare+, a fast‑growing private healthcare provider launching a new AI Patient Insights Platform next week.

As part of go‑live readiness, WellCare+ has provided a synthetic pre‑production test extract from the Patient Insights Platform called synthetic_nhs_patient_data.csv.

The extract simulates what the platform could ingest if stakeholders “get their wish list approved”. It contains a mixture of:

• direct identifiers (e.g., NHS Number, Full Name),
• special category / protected characteristic fields (e.g., Ethnicity),
• clinical/service fields (e.g., Appointment Date, Diagnosis Code, Treatment Type),
• and governance flags (Consent Status, Anonymisation Flag, AI Analysis Flag).

Data dictionary (from the provided dataset)

The CSV contains the following fields:

• NHS Number
• Full Name
• Date of Birth
• Gender
• Ethnicity
• GP Practice Code
• Appointment Date
• Diagnosis Code
• Treatment Type
• Consent Status (Yes/No)
• Anonymisation Flag (Yes/No)
• AI Analysis Flag (Yes/No)

Important: The dataset is synthetic, but you must still handle it as if it were real patient data — this is part of the simulation and aligns with the unit’s safe-use emphasis.

Your job as the analyst is to decide:

1. what fields are genuinely needed for the immediate analytics question,
2. what must not be used for routine reporting, and
3. what controls are required before any AI analysis or cross‑department sharing occurs (especially Marketing).

As go‑live approaches, everyone has ideas.

The “Data Capture Creep” Problem

In the final steering meeting, multiple department heads propose “just one more field” to unlock “better insights”:

• Product Lead: “If we capture ethnicity and religion, we can produce demographic insight cards.”
• Operations: “Add postcode and income band — we can map underserved areas.”
• Marketing: “If you share patient contact details and appointment categories, we can send targeted mailshots.”
• Clinical Governance: “We must avoid bias and be able to justify AI outputs.”
• IT Security: “Teams are emailing extracts and storing files in shared folders.”

Two near‑miss incidents land in your inbox:

1. An extract was forwarded to a personal email “to review on a phone.”
2. A shared folder containing patient-related extracts is accessible to “all staff.”

That’s where you come in.

You are a Junior Data Analyst on the Patient Insights project. Your job is to produce a single, readable Go‑Live Readiness Report that helps leadership decide:

• what data we should and should not capture
• what controls must be in place to prevent breaches
• how ethical principles and AI risks will be handled
• proof that compliant data handling is achievable in practice

This isn’t a “business requirements” exercise. It’s a realistic simulation of how a data analyst protects an organisation when insight ambitions collide with legal, security and ethical limits.

Task Instructions (Single Narrative Report)

Output: One continuous report that answers the go‑live questions below.
You will include short embedded exhibits (tables/figures) inside your narrative, because that’s how real readiness reports work.
The exhibits are not separate appendix artefacts — they are part of the narrative.

Task 2: Security & Policy Readiness — “What Controls Stop Breaches?” (LO2)

Scenario Brief: “We’re leaking data through normal work”

The IT Security Manager reports that analysts and managers are:

• saving extracts in loosely controlled folders
• sharing data informally via email/Teams chat
• producing uncontrolled spreadsheets that get reused across teams

Leadership asks you:

“Which security standards/frameworks matter, what policies do we rely on, and where are the gaps?”

Your Task (write in narrative, with two embedded exhibits)

1. Define the relevant security standards/frameworks and relate them to data management activities (governance, integration, analysis).
2. Outline the organisational policies/procedures WellCare+ should use to ensure compliance (minimum: encryption, backup/recovery, plus others).
3. Show how policies operate within a security framework and what impact this has on day‑to‑day data work.
4. Identify security gaps and propose realistic, “go‑live appropriate” solutions.

Embedded Exhibits (include inside Task 2)

Exhibit 2A — Policy–Framework Coverage Matrix (complete inside report)

Exhibit 2B — Security Gap Register (complete inside report)

What to evidence (by grade)

P3 (LO2) – Define relevant security standards and security frameworks related to data management activities.

P4 (LO2) – Outline policies and procedures used by an organisation to ensure compliance with standards and frameworks.
Tip: Anchor policies to real analyst behaviours: “where do extracts live, who has access, how do we prove it?”

★ Merit

M2 (LO2) – Explore the impact of organisational policies and procedures within a security framework that supports compliance.
Tip: Explore how controls affect governance/integration/analysis (not just “security in general”).

✪ Distinction

D2 (LO2) – Assess the impact on data management and propose innovative solutions for any security gaps.
Tip: Prioritise: what must be fixed before go‑live vs what can be scheduled post‑launch.

Task 3: Ethics & AI Readiness — “Are We Building Responsible Insight?” (LO3)

Scenario Brief: Protected characteristics & “better insights”

The Product Lead argues:

“If we capture ethnicity, we can spot unequal experiences and improve services.”

Marketing argues:

“If we know demographics, we can target people more effectively.”

Clinical Governance argues:

“We must prevent bias and avoid harmful outcomes.”

You must set ethical guardrails for a healthcare AI platform and show how those principles translate into practice.

Your Task (write in narrative, with one embedded exhibit)

1. Describe the value of ethical data principles when handling data.

2. Determine how effective applying those principles is in practice, and consequences if they are ignored (use AI reliability/accuracy/bias explicitly).

3. Critically evaluate ethical principles in the context of AI challenges and elaborate how they can be applied (i.e., show governance controls, not just statements).

Embedded Exhibit (include inside Task 3)

Exhibit 3A — Ethics → AI Risk → Mitigation (complete inside report)

What to evidence (by grade)

P5 (LO3) – Describe the value of applying ethical data principles when handling data.

★ Merit

M3 (LO3) – Determine the effectiveness of applying ethical principles and consequences of not applying them.

Tip: Bring it back to AI: “What does bias look like in insight cards? What harm could it cause?”

✪ Distinction

D3 (LO3) – Critically evaluate common ethical principles to address AI challenges and ethical considerations, and elaborate how principles can be applied.

Tip: Write an in‑report “Draft: Acceptable Use of AI” section with controls (roles, approvals, monitoring/audit, prohibited uses).

Marking Rubric

Note: The grade for the unit is based on overall performance across each LO; assessors typically confirm Pass first, then Merit, then Distinction.

LO2 — Investigate The Impact Of Security Standards/Frameworks And Organisational Policies On Data Management

LO3 — Examine Ethical Data Principles And The Role Of AI In Collation And Utilisation