Data quality and information governance - Ethics privacy and...

Learning Outcomes

After reading this article, you should be able to explain the importance of data quality and information governance for performance management. You will understand key ethical principles around data collection, privacy risks, and how bias can affect performance measurement and decision-making. You will also learn to identify and apply exam-relevant controls for protecting data and ensuring reliable, fair performance metrics.

ACCA Advanced Performance Management (APM) Syllabus

For ACCA Advanced Performance Management (APM), you are required to understand the implications of data quality and information governance on management information systems and performance evaluation. Revision should focus on:

• The characteristics of quality data for management decision-making
• The principles of information governance and data protection
• The ethical, legal, and social issues in data collection, processing, and reporting
• The risks of bias and privacy breaches in measurement, including mitigating actions

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 a principle of high-quality management information?
   1. Only historical financial data is included
   2. The data is timely, accurate, and complete
   3. Data is gathered solely from external sources
   4. Data focuses only on quantitative measures
2. A manager uses customer demographic data in performance reports without customer consent. Which information governance principle is at risk?
   1. Data minimisation
   2. Transparency
   3. Data protection and privacy
   4. Completeness

3. True or false? Algorithmic bias in performance measurement can lead to unfair outcomes even if data entry is error-free.

4. List two controls management can implement to reduce the risk of bias in performance data.

5. What is the main ethical risk when using large personal data sets in management reports?

Introduction

Information quality is central to management accounting and performance measurement. Without reliable and well-governed data, management decisions can be misleading or unethical. Increasing use of digital information and advanced analytics makes data quality, privacy, and bias key risks for the ACCA APM exam.

This article examines what makes data reliable, why information governance is essential, and the ethical challenges of using data for measurement. We assess privacy requirements, identify sources of bias, and explain controls to protect data integrity so that performance reports are both trustworthy and fair.

Key Term: data quality
The degree to which data is accurate, complete, timely, relevant, and fit for its intended purpose in management decision-making.

Data Quality in Performance Management

Good management decisions depend on the quality of fundamental data. Poor data quality can mislead, undermine controls, or cause reputational damage.

Characteristics of Good Data

High-quality data used for performance measurement should be:

• Accurate: Free from material errors or omissions
• Complete: All required data is present
• Timely: Up-to-date for current decisions
• Relevant: Matches the informational needs of management
• Consistent: Comparable over time and between sources
• Secure: Protected from unauthorised access or changes

Key Term: information governance
The framework of policies, processes, and controls that ensure data is managed legally, ethically, securely, and efficiently throughout its lifecycle.

Consequences of Poor Data Quality

Inaccurate or out-of-date data can lead to incorrect targets, flawed variance analysis, and inappropriate rewards. It can also result in non-compliance with regulations or loss of trust among stakeholders.

Worked Example 1.1

A business sets a sales bonus for managers based on customer data entries. However, delays in data upload and unchecked manual input errors cause under-reporting. Some managers are unfairly denied rewards, and management decisions are based on incomplete figures.

Answer:
Data quality failures (timeliness and accuracy) resulted in unreliable performance measurement, dissatisfied staff, and poor decision-making. Stronger data validation and regular audits are needed to prevent such issues.

Information Governance and Legal Compliance

Information governance is essential for protecting sensitive information, complying with regulations, and supporting effective management.

Core Information Governance Principles

• Lawfulness and transparency: Collect, process, and report data fairly and as permitted by law.
• Data protection and privacy: Secure personal and confidential data against misuse or unauthorised access.
• Accountability: Assign responsibility for data management to specific roles or teams.

Key Term: data protection
Legal and organisational measures to safeguard personal or sensitive information from unauthorised access, disclosure, or loss.

Key Term: privacy
The right of individuals to control how their personal data is collected, used, and shared by organisations.

Regulatory Requirements

Many jurisdictions impose strict rules, such as the General Data Protection Regulation (GDPR), requiring organisations to:

• Obtain valid consent for data collection
• Tell data subjects how their data will be used
• Permit data access, correction, or deletion on request
• Report data breaches promptly

Organisations that breach data protection laws may face fines and legal action, as well as reputational loss.

Worked Example 1.2

A retailer collects customer purchase data, including contact details, for performance reports but does not disclose how the data will be used. When a data leak occurs, regulators investigate both the security failure and the lack of customer transparency.

Answer:
The company breached information governance principles (transparency and data protection) and regulatory obligations. Penalties are likely, and customers' trust in the firm will decrease.

Exam Warning

Always link exam answers to the core principles of information governance and applicable legal duties (e.g., privacy laws) when analysing performance measurement systems.

Ethics in Information Collection and Reporting

Performance management relies not only on technical accuracy but also on ethical behaviour in the handling and use of data.

• Collect only data that is required (“data minimisation”)
• Use data for legitimate purposes agreed with data subjects
• Disclose performance results honestly and avoid selective reporting
• Respect individuals’ privacy rights in all performance data

Ethical lapses, such as manipulating metrics or omitting negative results, undermine the credibility of performance systems and can damage organisational culture.

Worked Example 1.3

A manager excludes data on delivery delays from a performance dashboard because these data sets are incomplete. As a result, customers and stakeholders receive a distorted view of operational performance.

Answer:
The manager fails the ethical requirement for transparency and honest disclosure. Accurate, fair reporting is an ethical imperative as well as a managerial one.

Bias in Measurement and Data Analytics

Bias—systematic error causing unfair or misleading outcomes—can affect the whole measurement process, from data collection to analysis and reporting.

Key Term: bias (in measurement)
Any process that systematically distorts data or results, leading to inaccurate or unfair performance measurement.

Sources of bias include:

• Flawed sampling (data not representative)
• Manual or recurring errors in data entry
• Use of algorithms that perpetuate existing prejudices
• Selective reporting or presentation (focusing only on successes)
• Ignoring relevant qualitative data, leading to misleading trends

Machine learning and "black box" analytics can perpetuate or even increase bias if historical data reflects discrimination or errors.

Controls to Reduce Bias

• Use clear, objective measurement criteria
• Regularly audit data sources and performance metrics
• Validate algorithms for fairness and accuracy
• Encourage reporting of anomalies or unintended outcomes
• Triangulate findings with multiple data types (quantitative and qualitative)

Worked Example 1.4

An employee reward system uses algorithmic scores from customer feedback to judge staff performance. However, analysis reveals the algorithm penalises employees from certain regions due to inherent data bias.

Answer:
The measurement system contains bias, producing unfair results and potentially illegal discrimination. Mitigating actions must include reviewing the data selection and algorithm logic for impartiality.

Privacy Risks and Large-Scale Data Collection

Storing and processing large amounts of personal data present risks:

• Accidental disclosure (data breach)
• Malicious access (hacking or fraud)
• Unauthorised secondary use of data

Key Term: data breach
An incident where personal or confidential data is lost, stolen, or disclosed to unauthorised parties.

Controls include:

• Encrypting sensitive records
• Limiting access to data only to those who need it
• Regularly updating training and security measures
• Conducting privacy impact assessments when launching new data-driven systems or analytics

Revision Tip

In the exam, always discuss both the technical and ethical risks of using large personal data sets in management information. Link your discussion to legal compliance, privacy, and fairness.

Summary

Data quality, robust information governance, and ethical handling of data are essential for valid, fair performance measurement. Management accountants must ensure information is complete, accurate, used legally and ethically, and free from bias. Privacy protections and controls are not just legal requirements—they protect trust and the value of management information.

Key Point Checklist

This article has covered the following key knowledge points:

• Explain the characteristics of high-quality performance data
• Describe the principles and importance of information governance
• Identify legal and ethical issues in data collection and reporting
• Explain the risks and main types of bias in measurement and data analytics
• Suggest controls to reduce privacy risks and bias in management information
• Apply these principles when evaluating performance systems in scenario-based questions

Key Terms and Concepts

• data quality
• information governance
• data protection
• privacy
• bias (in measurement)
• data breach