Learning Outcomes
After reading this article, you will distinguish between data, information, and knowledge, and identify their roles in business. You will be able to describe the qualities of good information, explain why data quality matters, and outline key data controls such as validation, authorisation, and security measures. You will also understand common risks to data and implement appropriate controls relevant to the ACCA Business and Technology (BT) exam.
ACCA Business and Technology (BT) Syllabus
For ACCA Business and Technology (BT), you are required to understand the nature of data, how it is processed into information, and how controls ensure data quality and security. Focus your revision here on:
- The definitions of data, information, and knowledge, and their differences
- Features of good quality information (the ACCURATE checklist)
- Common sources of data error and risk in organisations
- The purpose and types of data controls (preventive, detective, corrective)
- Methods for protecting data integrity, including validation, authorisation, and access controls
- Roles and responsibilities for ensuring data quality and security in business operations
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.
- What is the main difference between data, information, and knowledge?
- List four key features of good quality information.
- Give two examples of preventive controls that improve data quality in an organisation.
- A payroll clerk enters employee hours incorrectly due to missing validation checks. What type of control could help prevent this error?
Introduction
Organisations depend on high-quality data to operate effectively. Data is collected from various sources, processed into information for decision-making, and sometimes analysed further to produce knowledge. Inaccurate or poorly controlled data can lead to mistakes, faulty analysis, and even regulatory breaches. Understanding the distinction between data, information, and knowledge, as well as the controls required to maintain data quality, is essential for efficient business operations and compliance.
Key Term: Data
Raw facts, figures, or symbols that have not yet been processed to provide meaning.Key Term: Information
Data that has been processed, organised, or structured so that it is meaningful and useful for decision-making.Key Term: Knowledge
Perceptions or understanding gained by interpreting and analysing information, often through experience or specialist knowledge.
Data Quality: Why It Matters
High-quality data supports reliable information and improves business decision-making. Errors in data entry or processing can result in financial loss, legal issues, or bad decisions.
To ensure that data is fit for purpose, businesses use a set of criteria for good information. The ACCURATE acronym summarises these features.
Key Term: ACCURATE
A checklist describing the attributes of good information: Accurate, Complete, Cost-effective, Understandable, Relevant, Adaptable, Timely, Easy to use.
Features of Good Information (ACCURATE)
- Accurate: Correct and error-free for its purpose.
- Complete: Contains all essential details, with nothing important omitted.
- Cost-effective: The effort and cost to obtain the information do not exceed its value to the user.
- Understandable: Presented clearly, so recipients can comprehend and use it easily.
- Relevant: Matches the needs of the decision-maker; removes extraneous details.
- Adaptable: Can be modified or presented to suit different needs.
- Timely: Available when required for decision-making.
- Easy to use: Clearly structured and delivered through suitable channels.
Key Term: Data Quality
The degree to which data is accurate, complete, reliable, timely, and suitable for its intended use.
Worked Example 1.1
A sales manager receives last month’s sales report, but half the new customers’ addresses are misspelled, and several sales entries are missing. Is this report good information?
Answer:
No, the report fails the ‘Accurate’ and ‘Complete’ criteria of the ACCURATE framework, meaning it is unreliable for business decisions.
Types of Data and Common Problems
Data used in business can be classified as:
- Quantitative: Numbers that can be measured (e.g., sales amounts)
- Qualitative: Descriptions or categories that cannot be measured in precise numbers (e.g., customer feedback)
Data may also be structured (in defined fields) or unstructured (free text, images, etc.). Errors can arise from manual entry, system issues, or poor data design.
Key Term: Data Validation
The process of checking data for correctness and reasonableness before it is processed or stored.Key Term: Data Verification
The process of confirming that data has been accurately input or transferred, often by comparing with the original source.
Data Controls in Business
Organisations need controls to ensure data is valid, accurately recorded, and protected from loss or misuse. Controls can be grouped as follows:
- Preventive Controls: Help stop errors or fraud before they enter the system (e.g., validation checks, passwords).
- Detective Controls: Identify issues after they occur (e.g., reconciliation reports, exception reports).
- Corrective Controls: Handle errors or restore data after a problem (e.g., backup and recovery procedures).
Key Data Controls
- Validation checks: Ensure data input follows required formats (e.g., date fields in DD/MM/YYYY).
- Authorisation: Only authorised personnel can change or input certain data, reducing risk of error or fraud.
- Access controls: Limit who can view or modify data, using user IDs and passwords.
- Physical security: Protect hardware and storage devices from theft or damage.
- Encryption: Converts sensitive information into a secure format during storage or transmission.
Worked Example 1.2
A company uses an online form to collect customer orders. The form rejects incomplete addresses or invalid phone numbers. What kind of controls are applied?
Answer:
Data validation checks are in place to prevent inaccurate or incomplete data from entering the system.
Exam Warning
Errors can slip into systems if controls such as validation or authorisation are weak. Always consider how controls reduce both errors and the opportunity for fraud.
Data Security and Responsibilities
The responsibility for data quality and protection lies with all staff, but especially with managers who design systems and with those who input and process data.
Key roles include:
- Data owner: Responsible for data integrity and quality within their area.
- IT or system administrator: Manages access rights and technical controls.
- All staff: Expected to follow procedures and report issues or errors.
Risks to data include:
- Human error (e.g., incorrect input)
- Fraudulent entry or alteration of data
- Data loss due to system failure or cyber attack
- Unauthorised access or disclosure
To mitigate these risks, controls such as regular backups, password policies, and routine monitoring are put in place.
Worked Example 1.3
A finance department discovers that a supplier’s bank details were changed in the system without authorisation, resulting in a fraudulent payment. What control should have prevented this?
Answer:
Changes to critical data should require authorisation by a manager. Mandatory approval protects against unauthorised changes.
Monitoring and Continuous Improvement
Continuous monitoring of data and systems (through reporting and review processes) helps detect issues early. Training, clear procedures, and regular audits also improve data quality and reduce the risk of data-related incidents.
Revision Tip
Review the ACCURATE framework regularly and practice identifying which features are missing in poor-quality information examples.
Summary
Reliable business decisions rely on good quality data and information. Poor data quality can be costly and damaging. Controls such as validation, authorisation, and access security help maintain data quality and protect business assets. It is essential for all staff to use and maintain controls to achieve high-quality data for information production.
Key Point Checklist
This article has covered the following key knowledge points:
- Define and distinguish between data, information, and knowledge
- Explain the ACCURATE criteria for high-quality information
- Identify key risks to data quality in organisations
- Describe common preventive, detective, and corrective data controls
- Give examples of validation and authorisation procedures
- Recognise roles and responsibilities for data quality
- Understand the importance of continuous monitoring for data controls
Key Terms and Concepts
- Data
- Information
- Knowledge
- ACCURATE
- Data Quality
- Data Validation
- Data Verification