Postgraduate Diploma in Business Intelligence

Purpose

The purpose of the Postgraduate Diploma in Business Intelligence is to assist executives, managers, and other operational personnel to make better, effective and efficient business decisions. Business intelligence enables companies to cut costs, identify new business opportunities, and identify inefficient business processes. Business intelligence can help companies make better decisions by showing present and historical data within their business context. Analysts can leverage Business Intelligence (BI) to provide performance and competitor benchmarks to make the organisation run smoother and more efficiently. Analysts can also easily spot market trends to increase sales or revenue.

This qualification is designed to equip learners to navigate complex business scenarios exposed to disruption caused by volatile global influences such as health crises, climate change, and fluctuating global markets to mention a few. It has become increasingly important for business managers to drive business decisions based on data generated by advanced internal systems to provide rational and calculated cost-effective solutions to not only remain competitive but also survive and sustain business growth given severe challenges.

Upon completion of this qualification, qualifying learners will be able to

• Understand and apply fundamental business intelligence processes to solutions generation and guide the business in the direction of a consistent approach to data-driven decision-making.
• Manage Information and Knowledge for purposes of presupposing its role in business intelligence.
• Utilize business intelligence in the application of financial management procedures.
• Conduct business research applicable to the context of the digital business environment.

Rationale

The rationale for offering this qualification in South Africa is due to the increased demand for professionals to analyse and interpret data for business. The institution is regarded as one of the fastest growing sectors in industry and involves the collection, integration, analysis and presentation of data to enable organisations to make strategic decisions, (Allemann, 2023). Therefore, industry and Small, Medium and Micro Enterprises (SMEs) require professionals who are equipped with diverse skill sets such as data analytics, data engineering and business intelligence developers. In considering the expansion of academic programmes, the institution finds a compelling rationale for the introduction of this qualification. In South Africa, most new SMEs do not grow. There is a failure rate of 75%, which makes it among the highest in the world (Chimucheka, 2013; Olawale & Garwe, 2010). SMEs are catalysts for the future economy and serve as a means for innovation of new products and socio-economic development (Boonsiritomachai, et al., 2014; Adeniran & Johnston, 2014).

Therefore, there is a great need to accelerate their growth, information flow and competitiveness. Watson & Wixom (2007) regard SMEs as the spine of the world's economy since they constitute more than 95% of all global enterprises. Previously, Business Intelligence (BI) solutions and tools were aimed solely at large organisations, whilst inaccessible and insufficient for SMEs (Grabova, et al., 2010). As such, SMEs possessed fewer alternative BI solutions (Guarda, et al., 2013). However, in today's highly competitive business environment, SMEs now have their own tailor-fitted solutions. Unfortunately, they are still not making use of these tailored solutions to improve their socio-economic performance (Campbell, 2014).

The potential of business intelligence to aid SMEs towards improving and transforming data management whilst also increasing profitability, competitive advantage and creating improved business processes is substantial (Guarda, et al., 2013; Kumari, 2013; Lloyd, 2011). For SMEs to grow, turnover and create an even greater competitive advantage against their challengers; it is essential that they utilise business intelligence tools in their data processing to inform their strategic planning and decision-making processes. (Ponelis & Britz, 2011).

This initiative is grounded in several key factors and industry dynamics that underscore the relevance and significance of such a programme.

The trajectory of careers in business intelligence is experiencing a notable upswing, projections indicate a 15% employment growth for computer and information research scientists from 2019 to 2029, well above the average for other occupations. This signifies a substantial demand for BI expertise, a demand that the Institution can meet with a dedicated qualification. Industry insights from industry leaders, such as Paul Newman, the operational director of Page Group South Africa, emphasise the critical role of technology trends like Cloud Computing, Data Science and Artificial Intelligence in driving the need for BI and analytics professionals, (Allemann, 2023).

By aligning the curriculum with these trends, qualifying learners are well-prepared to meet industry demands. A substantial skills gap, particularly in digital proficiency, exists in emerging economies like Africa. The African Development Bank's research underscores this challenge, revealing a dearth of digital skills among the workforce in the region. The qualification can contribute significantly by bridging this gap through targeted education and training. There is an evident disparity between the skills currently possessed by the workforce and the skills in demand. Science and technology skills are particularly sought after, but there is a shortage of qualified professionals in these fields.

Furthermore, in today's fast-paced business landscape, data-driven decision-making is the norm, and business leaders require dependable analytics to inform their choices. This presents a unique opportunity for BI professionals, and the introduction of a dedicated programme can supply the industry with a pool of skilled professionals capable of meeting this demand. The skills gap in digital proficiency in developing economies is pivotal to realising the full potential of the digital economy, the specialised qualification will equip learners with the necessary skills.

In summary, the introduction of a Postgraduate Diploma in Business Intelligence is not only timely but also strategically aligned with industry demand and the imperatives of bridging the skills gap. This programme will empower graduates with the skills and knowledge to excel in the burgeoning field of business intelligence, meet industry requirements and contribute to the growth and innovation of the digital economy, particularly in emerging economies.

Recognition of Prior Learning (RPL)

The institution has an approved Recognition of Prior Learning (RPL) policy applicable to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications.

RPL for access

• Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.
• To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal and/or informal learning to cope with the qualification expectations.

RPL for exemption of modules

• Learners may apply for RPL to be exempted from modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

RPL for credit

• Learners may also apply for RPL for credit for or towards the qualification, in which they must provide evidence in the form of a portfolio that demonstrates prior learning through formal, non-formal and/or informal learning to obtain credits towards the qualification.
• Credit shall be appropriate to the context in which it is awarded and accepted.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Commerce in Management Information Systems, NQF Level 7.

Or

• Advanced Diploma in Business Analysis, NQF Level 7.

And

• Two years of managerial work experience.

This qualification consists of the following compulsory modules at National Qualifications Framework Level 8 totalling 120 Credits.

Compulsory Modules, NQF Level 8, 120 Credits

• Digital Leadership, 20 Credits.
• 21st Century Risk Management, 20 Credits.
• Information and Knowledge Management, 20 Credits.
• Managing Business Intelligence and Big Data Analytics, 20 Credits.
• Financial Intelligence, 20 Credits,
• Business Research, 20 Credits.

Exit Level Outcomes

1. Demonstrate the ability to use a range of business intelligence skills to identify, analyse and address complex or abstract problems in the digital environment.
2. Construct business intelligence strategies to facilitate leadership in the digital environment.
3. Examine the value of business intelligence, execute business intelligence strategies and engage in solutions to manage big data.
4. Effectively communicate complex data findings through data visualization techniques, ensuring that information is accessible and actionable for various stakeholders.
5. Develop contemporary risk management approaches for the 21st Century.
6. Execute strong problem-solving and critical thinking skills, enabling them to address complex business challenges by leveraging data and BI solutions.
7. Conduct business research applicable to the context of the digital business

environment.

Associated Assessment Criteria for Exit Level Outcome 1

• Expound in detail on the fact that leadership has not evolved, but general understanding and application of leadership, inclusive of situational leadership.
• Provide comprehensive insight into the role of transforming the business culture to a digitized one using effective leadership and adapting the organizational culture to implement this transformation process.
• Explain in full, business intelligence as data and information that will facilitate data-driven decision-making and the role blockchain technology plays.
• Accurately assess and differentiate between the digitization drivers as well as the nature of what is becoming known as "digital leadership.
• Design a logical framework for the transformation of current leadership to digital leadership.

Associated Assessment Criteria for Exit Level Outcome 2

• Identify and differentiate with insight between the following data sources, integration services, data management services, reporting and analytical services, information delivery and consumption services.

Associated Assessment Criteria for Exit Level Outcome 3

• Differentiate between sophisticated databasing and business intelligence together with the required technology to facilitate the production of business intelligence.
• Elaborate on the need for business intelligence in terms of the organisation's capacity to manage big volumes of data.
• Comprehensively discuss the role of balanced score-carding in business intelligence.
• Comment on the following implications of big data management: volume, variety and velocity.
• Propose various techniques in terms of statistical analyses in the framework of big data management.
• Report in detail on how risks can be managed in an organisation, especially during processes of digitisation.
• Provide a comprehensive analysis and assessment of key indicators of risk.
• Propose how risks can be managed in a digitised environment.

Associated Assessment Criteria for Exit Level Outcome 4

• Illustrate a conceptual understanding of strategic risk management and the mitigation of factors causing risk, especially in a digital environment.
• Report in detail on how risks can be managed in an organisation, especially during processes of digitization.
• Provide a comprehensive analysis and assessment of key indicators of risk.
• Propose how risks can be managed in a digitized environment.
• Suggest how to go about developing and applying a risk management framework for the 21st century through, rethinking risk management frameworks, creating a risk management framework that resonates with senior executives, and building and leading effective risk workshops that target senior executives.
• Use data to build an impactful narrative in risk discussion and use risk appetite to drive business value.
• Manage the potential risks caused by digitization.
• Illustrate a conceptual understanding of strategic risk management and the mitigation of factors causing risk, especially in a digital environment.

Associated Assessment Criteria for Exit Level Outcome 5

• Illustrate an applied understanding of the implementation of information systems to achieve business objectives and goals.
• Sufficiently refine and automate business processes using information systems to improve organisational performance.
• Appropriately develop strategies using information systems to harness a competitive edge.
• Design a comprehensive set of principles and guidelines to govern ethical and social issues.
• Effectively evaluate information technology hardware and software trends to meet the demands of an evolving marketplace.
• Sufficiently design and develop a database to improve business performance and decision-making.
• Effectively design telecommunications, the internet, and wireless technologies to support communication and e-business.
• Appropriately develop an organizational framework for security and control of the information system.
• Effectively analyse operational goals and data constraints in establishing an enterprise system.

Associated Assessment Criteria for Exit Level Outcome 6

• Successfully apply e-commerce to improve marketing, revenue models and business transactions.
• Clearly analyse decision-making by processing input from a Knowledge Management System.
• Illustrate comprehension of the role of financial business intelligence in the financial services sector about the following: Increased marketing opportunities, Increased risk management, Boosting the bottom line, Strategy development, Up-to-date data, A more informed staff, and better customer relations.
• Apply business intelligence principles when managing financial risk.
• Propose how business intelligence can increase business performance and efficiency.
• Differentiate between factors that will affect compliance and profitability when modelling BI frameworks.
• Outline how business intelligence can improve financial products and services.
• Describe the role of AI and machine learning in the context of business solutions making.

Associated Assessment Criteria for Exit Level Outcome 7

• Distinguish between the research paradigms, methods and designs in such a way that the qualitative and quantitative differences are evident.
• Establish academically accepted concepts of research methods, as well as understand and apply these to the business environment.
• Recognize, examine and formulate a business research problem that is of sufficient depth to conduct a research study.
• Assess and extract from the body of knowledge theoretical frameworks to support and enlighten one's research.
• Analyse and interpret current literature to compile a comprehensive literature review showing the mastery of the topic in a synthesized manner.
• Exhibit an in-depth understanding of the acceptable data collection and analysis methods in the setting of qualitative and quantitative research, with one's research topic.
• Acquire the knowledge and skills to compile a research report that exhibits the representation and application of theoretical research methodology for the selected research problem.
• Illustrate relevant application of the above criteria in the context of the field of research displaying acknowledgement of the field-specific norms and practices.

INTEGRATED ASSESSMENT

The assessments will centre on

• The extent to which the student has grasped the concepts.
• The application of theory to a practical context.
• The methods of research used.
• A balance between theory and practice and its relevance to the level of the qualification.
• The use of appropriate technology to ensure effective communication of ideas.

Assessment methods measure the extent to which the student has achieved competence in the different areas of study.

Formative Assessment

These assessment methods may include

• Case studies.
• Report writing.
• Interpretative and analytical problem-solving.
• Work-based assignments.

Formative assessment implies assessment supportive of learning, non-judgemental and focused on providing constructive feedback or criticism to the learner. It takes place during the learning process and informs the planning of future learning activities.

Summative Assessment

Summative assessment implies assessment to be mainly concerned with summing up the learning process and therefore usually takes place at the end of the relevant learning process.

Integrated assessment is used extensively across the qualification, particularly in case studies. Self-and formative assessment takes place through case studies, and assignments, including the writing of proposals and financial plans. Summative assessments are integrated into the learning in that they take place at the end of each of the constituent modules of the qualification.