Learning Outcomes
After reading this article, you will understand the main attributes that define high-quality information for performance management: accuracy, completeness, timeliness, and relevance. You will be able to explain why these qualities are essential in supporting effective decision-making. You will also gain the ability to assess and recommend controls that support robust data governance and ensure information systems provide dependable outputs.
ACCA Advanced Performance Management (APM) Syllabus
For ACCA Advanced Performance Management (APM), you are required to understand how data quality and information governance support management decision-making and performance measurement. You must be able to:
- Define and explain the attributes of good information, especially accuracy, completeness, timeliness, and relevance
- Assess information systems and recommend improvements to data quality controls
- Evaluate the impact of poor data quality on performance reporting and decision-making
- Explain the need for good information governance and its role in supporting strategic and operational objectives
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.
-
Which of the following best describes "completeness" in a performance management information system?
- Having no numerical errors in reported values
- Including all relevant data necessary for the intended analysis
- Ensuring information is delivered before the reporting deadline
- Using only external information sources
-
True or false? Data that is highly accurate but not relevant may still support effective managerial decision-making.
-
A performance report is delivered one week after manager decisions are required. Which key attribute of data quality is most likely deficient?
-
Briefly explain how lack of timeliness can affect performance measurement.
Introduction
Information drives every aspect of performance management. Accurate, complete, timely, and relevant information allows managers to plan, control, and evaluate organisational performance. Poor data can undermine even the best systems and lead to costly mistakes. Understanding the core data quality attributes and information governance practices ensures reliable decision-support information for any organisation.
Key Term: data quality
The extent to which information meets requirements for accuracy, completeness, timeliness, and relevance, allowing it to be fit for purpose in decision-making.
Data Quality Attributes
To support effective planning and control, information must meet strict criteria. The most important are accuracy, completeness, timeliness, and relevance.
Accuracy
Information must be correct and free from errors. Decisions made on inaccurate data can lead to poor outcomes and financial losses. Accuracy depends on careful data entry, validation checks, and robust processing routines.
Key Term: accuracy
The degree to which data correctly describes the real-world facts or events it is intended to represent.
Completeness
Completeness refers to the inclusion of all required data for the intended use. Partial data may give a misleading picture and cause managers to overlook important factors.
Key Term: completeness
The extent to which all necessary data or information is present for a given purpose.
Timeliness
Information must be available when needed. Delayed reporting can lead to missed opportunities and ineffective control.
Key Term: timeliness
The degree to which information is available to decision-makers in time to influence action.
Relevance
Only data that is suitable for the user’s needs and purpose should be reported. Irrelevant information causes confusion and may create information overload.
Key Term: relevance
The extent to which information applies to the specific needs or objectives of its users.
Information Governance
Reliable information requires strong governance—systems, policies, and controls that protect data quality through its collection, processing, storage, and reporting. Good governance includes clear roles and responsibilities, documented procedures, access controls, and periodic data quality reviews.
Key Term: information governance
The framework of policies, standards, and controls that ensures information is managed to meet organisational quality and compliance requirements.
Risks of Poor Data Quality
Failures in any of the above attributes can result in significant performance management problems:
- Inaccurate data leads to faulty forecasts, budgets, or KPIs.
- Incomplete information may hide fundamental issues or risks.
- Delayed data makes it impossible to take timely corrective action.
- Irrelevant information distracts attention from key issues.
Poor data quality damages trust in reports, increases operational risk, and can result in regulatory breaches or failed strategies.
Supporting Controls in Information Systems
Effective controls are needed throughout the information lifecycle:
- Data entry checks (validation rules, double-entry, use of drop-down lists)
- Automated reconciliations
- Access controls to prevent unauthorised changes
- Periodic review and cleansing of outdated or irrelevant data
- User training on data handling and reporting standards
- Audit trails to ensure changes can be traced and reviewed
Worked Example 1.1
A division’s monthly production report overstates finished goods by 2,000 units due to a spreadsheet error, leading management to delay new orders. Describe the data quality problem and suggest controls to prevent recurrence.
Answer:
The issue is an error of accuracy—reported figures do not reflect actual output. Management decisions based on this incorrect figure result in underproduction and lost sales. To prevent this, the business should implement automated formula checks, peer review of critical spreadsheets, and reconciliation of reported outputs to inventory records before publishing reports.
Worked Example 1.2
A regional manager receives performance figures that omit one entire branch’s sales. What data quality attribute is missing? What is the likely impact?
Answer:
The missing attribute is completeness. Without all branches’ data, management may misjudge the region's actual performance and potentially allocate resources incorrectly. The solution is to implement completeness checks before report finalisation, such as automated alerts for missing branch submissions.
Worked Example 1.3
A board report contains data from two months ago because of a backlog in data processing. What attribute is lacking, and what are the risks?
Answer:
The deficiency is timeliness. Outdated figures may not reflect current trends or issues, so corrective actions could be delayed or misguided, reducing the effectiveness of performance management.
Exam Warning
In ACCA APM, you may be asked to evaluate a set of management information and recommend improvements. Clearly identify which attribute(s) of data quality are deficient and show how the issues could impact performance decisions.
Summary
Organisations rely on high-quality information for planning, control, and performance measurement. Accuracy, completeness, timeliness, and relevance are essential attributes that support these activities. Good information governance—clear policies, controls, and periodic review—helps ensure dependable data for managers and stakeholders.
Key Point Checklist
This article has covered the following key knowledge points:
- Define the key attributes of data quality: accuracy, completeness, timeliness, and relevance
- Recognise the effect of poor data quality on performance measurement and decision-making
- Identify basic information governance controls to support data quality
- Apply data quality concepts to typical ACCA APM scenarios and evaluate information systems
- Suggest practical steps to address deficiencies in information systems
Key Terms and Concepts
- data quality
- accuracy
- completeness
- timeliness
- relevance
- information governance