1. Deleted syllabus content
(1)Assess the use of strategic management accounting in the context of multinational companies. (former chapter 13 – 2.3)
(2)Evaluate the impact to an organisation of a move beyond budgeting (former chapter 2 – 3)
(3)Discuss the concept of business integration and the linkage between people, operations, strategy and technology. (former chapter 3 – 3.4)
(4)Identify and discuss the required changes in management accounting systems as a consequence of empowering staff to manage sectors of a business. (former chapter 5 – 4.1)
(5)Discuss how IT systems provide the opportunity for instant access to management accounting data throughout the organisation and their potential impact on business performance. (former chapter 3 – 6.5)
(6)Assess how IT systems facilitate the remote input of management accounting data in an acceptable format by non-finance specialists. (former chapter 3 – 6.5)
(7)Assess the continuing effectiveness of traditional management accounting techniques within a rapidly changing business environment. (former chapter 4 – 1)
(8)Assess the impact of governmental regulations and policies on performance measurement techniques used and the performance levels achieved (for example, in the case of utility services and former state monopolies). (former chapter 4 – 4)
(9)Assess the impact of responsibility accounting on information requirements. (former chapter 5 – 9)
(10)Discuss the ways in which high-level corporate performance objectives are developed. (former chapter 5 – 10.1 and 10.2) 尽管大纲已去掉，这个部分仍需要理解。
(11)Identify strategic objectives and discuss how they may be incorporated into the business plan. (忽略这个变化即可)
(12)Discuss social and ethical obligations that should be considered in the pursuit of corporate performance objectives. (former chapter 3 – 9)
(13)Explain the performance ‘planning gap’ and evaluate alternative strategies to fill that gap. (former chapter 9 – 2)
(14)Justify the crucial objectives of survival and business growth. (former chapter 7 – 2)
(15)Explore the traditional relationship between profits and share value with the long-term profit expectations of the stock market and recent financial performance of new technology companies. (former chapter 7 – 7)
(16)Discuss the implications of the growing emphasis on non-financial performance indicators. (忽略这个变化即可)
(17)Discuss the significance of non-financial performance indicators in relation to employees. (忽略这个变化即可)
(18)Discriminate between quality, quality assurance, quality control and quality management. (former chapter 10 - 2.1)
(19)Advise on the structure and benefits of quality management systems and quality certification. (former chapter 10 - 2.3)
(20)Evaluate the ways in which performance measurements systems may send the wrong signals and result in undesirable business consequences. (忽略这个变化即可)
(21)Discuss and apply the Performance Prism. (former chapter 11 – 4)
(22)Discuss the ways through which management accounting practitioners are made aware of new techniques and how they evaluate them. (忽略这个变化即可)
(23)Discuss, evaluate and apply environmental management accounting using for example input/output analysis. (former chapter 15 – 1.4 (1))
(24)Discuss the issues surrounding the use of targets in public sector organisations (忽略这个变化即可)
(25)Discuss contemporary issues in performance management. (忽略这个变化即可)2. New added syllabus content
(1)Discuss the development of Big Data and its impact on performance measurement and management, including the risks and challenges it presents.
(2)Advise on common mistakes and misconceptions in the use of numerical data used for performance measurement. (上一期徐开金老师讲义已经包括此内容)
(3)Assess the statement; ‘What gets measured, gets done.’ (上一期徐开金老师讲义已经包括此内容) 1. Big Data and performance management
The growth of the internet, multimedia, wireless networks, smartphones, social media, sensors and other digital technologies are all helping to fuel a data revolution.
In the so-called 'Internet of Things', sensors embedded in physical objects, such as mobile phones, motor vehicles, smart energy meters, RFID tags, tracking devices and traffic flow monitors, all create and communicate data which is shared across wired and wireless networks that function in a similar way to the internet.
The timing and location of cash withdrawals from ATM machines could also be a potential source of data.
Internet of things: A situation in which everyday objects have network connectivity, allowing them to send and receive data over the internet.
Consequently, whereas intuition has historically played a large part in business decisions (in the absence of reliable and timely data) organisations now expect business decisions to be based on robust data analytics, supported by intuition and experience. 1.1 What is Big Data?
(1)'Big data: The next frontier for innovation, competition and productivity', McKinsey Global Institute defined Big Data as 'datasets whose size is beyond the ability of typical database software to capture, store, manage and analyse'.
(2)Big Data is 'high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processes for enhanced insight and decision making.' (Gartner) 1.2 Characteristics of Big Data(1)Volume
Perhaps the main benefit of Big Data analytics comes from the ability to process very large amounts of information.
The bigger the data, the more potential insights it can give in terms of identifying trends and patterns, and in terms of getting a deeper understanding of customer requirements.
However, the 'volume' aspect of Big Data also presents the most obvious challenges to conventional IT structures, due to volume of storage space required for the data.
In this respect, the use of external capacity (i.e. cloud computing) could be a useful way for organisations to increase the amount of data they can store. (2)Velocity
Refers to the increasing speed with which data flows into an organisation, and with which it is processed within the organisation.
It is important to recognise that the competitive advantage an organisation can gain from 'velocity' relates to the speed with which data is processed and the velocity of a system's outputs, as well as the speed with which data initially flows into it. (3)Variety (or variability)
A common theme in relation to Big Data is the diversity of source data, with a lot of the data being unstructured (i.e. not in a database). For example, keywords from conversations people have on Facebook or Twitter, and content they share through media files (tagged photographs, or online video postings), could be sources of unstructured data.
However, this variety presents a challenge to organisations, as they need to find ways of capturing, storing and processing the data. If data is too big, moves too fast, or doesn't fit with the structures of an organisation's existing information systems, then in order to gain value from it an organisation needs to find an alternative way to process that data.
In this respect, 'Big Data analytics' is likely to be crucial to making use of the potential value of Big Data. (4)Big Data analytics
It refers to the process of collecting, organizing and analysing large sets of data ('Big Data') to discover patterns and other useful information which an organisation can use in its future business decisions.
Being able to extract insights from the data available is crucial for organisations to benefit from the availability of Big Data – for example, to help them understand the complexity of the environment in which they are operating, and to respond swiftly to the opportunities and threats presented by it; or to develop new insights and understanding into what customers need or want. (5)Veracity (truthfulness)
For that data to be beneficial for decision making, it needs to be reliable and truthful. If the data is not truthful (for example, due to bias or inconsistencies within it) this could reduce the value of any decisions which are informed by it. 1.3 Making use of Big Data
It is becoming possible for all organisations to access and process the volumes of Big Data potentially available to them, due to cost-effective approaches such as cloud-based architectures and open source software.
Making effective use of Big Data could confer competitive advantage for an organisation. Alternatively, in time, competitors who fail to develop their capabilities to use Big Data and information as strategic resources could be left behind by those who do.
While these might initially seem to be quite bold claims, Big Data can certainly create value for organisations through its ability to drive innovation and by helping organisations gain greater and faster insights into their customers.
Analysing data from as many sources as possible can also increase the amount of useful information available to managers when they are making decisions.
However, the distinction between simply having 'data' and having 'useful information' is important here. 1.4 The value of Big Data(1)Creating transparency
Making data more easily accessible to relevant stakeholders, in a timely manner, can create value in its own right. This transparency could relate to data within an organisation as well as external data.
For example, within a manufacturing company, integrating data from research and development, engineering and manufacturing units to enable concurrent engineering could significantly reduce time to market as well as improving quality.
Another important context in which transparency can be valuable for an organisation is in relation to fraud. For example, having real-time information available from a variety of sources could help an organisation expose fraud and irregular business practices. (2)Performance improvement
The increasing amount of transactional data in digital form provides organisations with an increasing amount of accurate and detailed performance data in real or almost real time.
By analysing variability in performance and the causes of that variability, organisations then manage performance to higher levels. (3)Market segmentation and customization
The volume and variety within Big Data enables organisations to create highly specific segments within its markets and to tailor its products and services precisely to meet those needs.
The ability to perform precise customer segmentation and targeting could be used to help organisations improve customer loyalty and retention, as well as in attracting new customers. (4)Decision making
The sophisticated analytics tools which are used to uncover previously hidden patterns and trends in data could also be used to improve decision-making. For example, trends identified by a retailer in in-store and online sales in real time could be used to manage inventories and pricing. (5)New products and services
Entities can use data about social trends and consumer behaviors to create new products and services to meet customers' needs, or to enhance existing products and services so that they meet customers' needs more exactly.
For example, the emergence of real-time location data, from traffic light sensors and satellite navigation systems, could enable insurance companies to refine the pricing of their insurance policies according to where, and how, people drive their cars.
More generally, Big Data could also provide new business opportunities in their own right. For example, Facebook's advertising business incorporates analysis of a user's actions as well as their friends' actions.
Equally, Amazon could be seen as an example of a company which has built its business and serves its customers by using data and analytics; for example, through the way it makes recommendations for customers linked to the purchases made by other customers with similar interests. 1.5 Potential risks and challenges of Big Data
The critics argue that very few instances exist where analysing vast amounts of data has resulted in significant new discoveries of performance improvements for an organisation.
Correlation not causation: It can often be easier to identify correlations between different variables than to determine what is causing that correlation. Correlation does not necessarily imply causality.
Similarly, if an organisation does not understand the factors which give rise to a correlation, it will equally not know what factors may cause the correlation to break down. 1.6 Ethics and governance(1)Potential ethical issues
Although Big Data can help entities gather more information about their customers and understand customer behaviour more precisely, gathering this data could also raise significant ethical and privacy issues.
Critics argue that in order for Big Data to work in ethical terms, the individuals whose data is being collected need to have a transparent view of how their data is being used or sold.
On the one hand, if organisations have access to personal data – such as health records and financial records – this could help them to pinpoint the best medical treatment for a patient or the most appropriate financial products for a customer. On the other hand, however, these categories of personal data are those which consumers regard as being the most sensitive. In this respect, Big Data raises questions around how organisations and individuals will manage the trade-offs between privacy and utility of data.
If companies are using Big Data properly, it will be vital for them to consider data protection and privacy issues. The issue of data security is also closely linked to issues of privacy.
The significance of data security could also be increased if organisations want to use third-party cloud service providers to store their data. (2)Potential governance issues
The use of Big Data also requires organisations to maintain strong governance on data quality. For example, the validity of any analysis of that data is likely to be compromised unless there are effective cleansing procedures to remove incomplete, obsolete or duplicated data records.
Similarly, it is very important for organisations to ensure that the overall data quality from different data sources is high because the volume, variety and velocity characteristics of Big Data all combine to make it difficult to implement efficient procedures for validating data or adjusting data errors. 2. Advise on common mistakes and misconceptions in the use of numerical data used for performance measurement.文章来源：东方立品徐开金老师原创作品，转载请注明出处。联系方式
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