## **Learning Outcomes**

After reading this article, you will be able to explain the importance of data ethics in performance management, identify confidentiality risks and requirements for handling sensitive information, and recognise how data bias can affect decision-making. You will also understand typical exam requirements on ensuring ethical and fair data practices within organisations.

## **ACCA Performance Management (PM) Syllabus**

For ACCA Performance Management (PM), you are required to understand ethical considerations and behavioural impacts in the use of data and management information. The following syllabus points are relevant for this topic:

- Define and discuss ethical behaviour concerning the use, storage, and analysis of data
- Recognise the importance of data confidentiality and users' legal obligations
- Identify typical risks and controls over handling sensitive or personal information
- Explain how data bias may affect reporting and decision-making
- Understand guidelines for fair, objective, and ethical use of information in performance management

## **Test Your Knowledge**

_Attempt these questions before reading this article. If you find some difficult or cannot remember the answers, remember to look more closely at that area during your revision._

1. Which of the following is an example of failing to maintain data confidentiality?
   a) Encrypting customer data before sending it
   b) Discussing client account details in a public space
   c) Using anonymised performance reports for analysis
   d) Restricting employee access to sensitive payroll records

2. True or false? Data bias occurs only when an employee deliberately manipulates data to mislead users.

3. Why is it important for management accountants to consider data ethics in performance reporting?

4. Give two common controls used by organisations to protect the confidentiality of data.

5. What principle should guide managers when deciding whether to use personal data for a new business analysis?

## **Introduction**

Reliable information is the basis for sound performance management. However, ethical behaviour is essential in ensuring management data is trustworthy, secure, and used fairly. As data-driven decisions become more common, managers must protect sensitive data, comply with regulations, and avoid bias that may distort results.

Ethical data management is about respecting laws, internal policies, and the rights of individuals. Failure in any of these areas can damage an organisation’s reputation, result in regulatory penalties, and lead to incorrect decisions.

> **Key Term: Data ethics**
> Principles and standards governing the responsible use, management, and sharing of data to respect individuals' rights and maintain integrity.
>
> **Key Term: Confidentiality**
> The obligation to protect sensitive or personal information from unauthorised access or disclosure.
>
> **Key Term: Data bias**
> Systematic distortion in analysis or reporting due to prejudicial assumptions or flawed data, leading to unfair or misleading outcomes.

## **DATA ETHICS IN PERFORMANCE MANAGEMENT**

Ethical use of data means that information is collected, stored, and processed only with clear purpose and legal authority. It also ensures transparency about what data is collected and how it will be used.

Managers need to consider:

- Whether they have consent to process and use personal or sensitive data
- If the data they obtain is relevant and accurate for its intended purpose
- How their actions affect individuals’ rights and organisational integrity
- That any use of data complies with legal and professional codes

Data ethics is especially important when using advanced analytics, such as big data or predictive models, which may process large amounts of personal or commercially sensitive information.

> **Exam Warning**
> Using data for reasons not clearly stated when it was collected — or passing on personal data without consent — can breach privacy laws and lead to disciplinary action.

## **CONFIDENTIALITY: RISKS AND CONTROLS**

Organisational data often includes personal, financial, or commercially sensitive details. The risk of unauthorised access or accidental disclosure is high. Maintaining confidentiality is a core ethical and legal responsibility.

Key controls for protecting confidentiality include:

- Logical access controls: Passwords, user permissions, and multi-factor authentication restrict access to sensitive data.
- Data encryption: Scrambling data in storage and during transmission prevents unauthorised parties from reading it.
- Physical security: Locked offices, secure servers, and monitored access to areas where data is held.
- Staff training: Clear policies, awareness training, and procedures for reporting breaches.
- Audit trails: Monitoring who accessed or changed data and when.
- Data minimisation: Only collecting and retaining the amount of data necessary for the stated purpose.

### **Worked Example 1.1**

**Scenario:** An HR assistant is given access to employee medical records needed for compiling a monthly absence report. The assistant copies some records to their personal email to work from home and later sends a spreadsheet with names visible to an external vendor for analysis.

> **Answer:**
>
> Both actions breach confidentiality controls. Sensitive personal information must not be transferred unprotected outside secure business systems, nor shared with third parties unless explicitly authorised and anonymised. The organisation could face regulatory fines and reputational harm.

## **BIAS IN DATA AND REPORTING**

Bias in management data can arise unintentionally or deliberately. It often affects decision quality and fairness.

Common types of bias include:

- **Selection bias:** Data analysed does not represent the full population (e.g., surveying only satisfied customers)
- **Confirmation bias:** Interpreting results to fit pre-existing beliefs
- **Measurement bias:** Using inconsistent or inaccurate data sources

Consequences of data bias include:

- Misleading performance evaluation
- Unjustified appraisal of staff or departments
- Poor resource allocation
- Reduced trust in reporting among stakeholders

> **Revision Tip**
> Always assess the data source and method before drawing conclusions or making recommendations. Consider sampling methods and whether any hidden assumptions may skew results.

### **Worked Example 1.2**

**Scenario:** A performance manager uses sales data from the last month to set targets for the next quarter. The last month included a one-off promotional campaign.

> **Answer:**
>
> This approach introduces bias, as the performance is not representative of normal conditions. Future targets should be set using trend data and by removing anomalies.

## **IMPLICATIONS OF UNETHICAL OR BIASED DATA PRACTICES**

Unethical behaviour, data breaches, or unrecognised bias can lead to:

- Legal action and regulatory penalties
- Loss of public and client trust
- Sanitised or manipulated reporting for personal gain
- Inaccurate business decision-making

Management accountants in ACCA roles are expected to champion ethical data use, report risks, and act as role models in upholding professional standards.

## **Summary**

Ethically managing data in performance management protects individuals’ privacy, preserves organisational integrity, and ensures accurate decisions. Confidentiality depends on robust controls and responsible behaviour at all levels. Recognising and correcting bias is essential for fair, effective reporting and performance assessment.

## **Key Point Checklist**

_This article has covered the following key knowledge points:_

- Define data ethics and explain why ethical data management is essential in performance management
- Identify the legal and organisational risks relating to confidentiality breaches
- List and explain controls for maintaining confidentiality of sensitive information
- Recognise and describe forms of data bias that can affect management reporting
- Explain the potential impact of unethical, biased, or careless data handling on decisions and stakeholder trust

## **Key Terms and Concepts**

- Data ethics
- Confidentiality
- Data bias


Ethical and behavioural considerations - Data ethics, confidentiality, and bias