Management Information: 2025 Syllabus Updates

Chapter 6: Budgeting

The Workbook provides revised text on big data, data analytics, and data mining in Chapter 6. Section 3.10 defines data as distinct pieces of information that can exist in various forms, such as numbers, text, electronic memory, or facts stored in a person's mind.

Big Data

Section 3.10.1 explores the concept of big data, revising the definitions of two key characteristics:

1. Velocity: Big data is generated rapidly and flows continuously, often requiring quick processing in real-time due to its time-sensitive nature.
2. Veracity: The accuracy, reliability, and quality of big data can be compromised by various factors, including varying quality of data sources, challenges in verifying informal or unstructured data, the need to filter out noise and identify bias and a lack of time for checks when processing data in real-time

Section 3.10.2 outlines the numerous business applications of big data, including customer insights, predictive analytics, risk management, operational efficiency, fraud detection, product development, and social media analysis.

Artificial Intelligence and Machine Learning

Section 3.11 introduces further content on artificial intelligence (AI) and machine learning (ML). AI is defined as the field of study and application involving the creation and utilisation of advanced computer systems to perform tasks that traditionally require human intelligence. These tasks include learning from data, reasoning, problem-solving, sensory understanding, language processing, and creative work.

Machine learning is described as the ability of a computational device to learn from large volumes of training data and improve performance of a given task without being explicitly programmed.

The Workbook outlines various subsets of AI, including:

• Computer vision
• Generative AI
• Natural language processing
• Machine learning (including deep learning)

AI systems are categorised into deterministic and probabilistic systems:

• Deterministic AI operates under pre-defined rules and produces consistent outputs given the same inputs.
• Probabilistic AI incorporates uncertainty in its models.

The syllabus also covers the application of AI in budgeting and forecasting, focusing on its ability to assist Management Information (MI). Examples of forecasting tools using machine learning include cash flow forecasts and expense tracking coupled with analytics.

The Workbook discusses potential problems with AI, such as:

• Over-trusting outputs
• Data protection concerns
• Ethical considerations
• Copyright issues
• Quality of data
• Data bias

It emphasises the need for accountants to develop skills in professional scepticism and critical thinking to ensure the responsible use of AI in their work.

Chapter 8: Performance Management

Cloud Accounting

Section 2.9 of Chapter 8 adds further syllabus content on cloud accounting. Cloud computing is defined as on-demand access via the internet to computing resources, including applications, servers, data storage, development tools, and networking capabilities, hosted at a remote data centre managed by a cloud services provider (CSP).

Cloud accounting involves conducting basic accounting tasks, such as managing and balancing the books, using software that resides in the cloud and is typically delivered in an as-a-service model. The Workbook outlines three main models for cloud accounting:

1. Software-as-a-Service (SaaS)
2. Platform-as-a-Service (PaaS)
3. Infrastructure-as-a-Service (IaaS)

Section 2.9.3 discusses real-time monitoring in cloud accounting systems. This feature allows for tracking activities as they occur, updating sales, cost, inventory, and profit data almost instantly. The benefits of real-time monitoring include:

• Fast reaction to market fluctuations
• Improved cash flow management
• Higher accuracy
• Better adherence to regulatory compliance

Data Bias

The syllabus now includes further text on data bias (Section 5.1). Data bias occurs when data is not representative of the population being analysed. It can be inherent in the collected data or introduced by those analysing it. The Workbook explains that data bias can arise when datasets fail to accurately model the intended reality, often due to skewed, incomplete, or prejudiced information.

Data bias can significantly impact machine learning models and AI algorithms, leading to inaccurate or unfair results. The syllabus highlights the importance of understanding and addressing data bias, particularly in AI applications such as recruitment, insurance, criminal justice, and healthcare.

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