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All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source. |
| SOUTH AFRICAN QUALIFICATIONS AUTHORITY |
| REGISTERED QUALIFICATION: |
| Advanced Diploma in Big Data and AI Management |
| SAQA QUAL ID | QUALIFICATION TITLE | |||
| 125658 | Advanced Diploma in Big Data and AI Management | |||
| ORIGINATOR | ||||
| Boston City Campus (Pty) Ltd formerly Boston City Campus and Business College (Pty) Ltd | ||||
| PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY | NQF SUB-FRAMEWORK | |||
| CHE - Council on Higher Education | HEQSF - Higher Education Qualifications Sub-framework | |||
| QUALIFICATION TYPE | FIELD | SUBFIELD | ||
| Advanced Diploma | Field 03 - Business, Commerce and Management Studies | Generic Management | ||
| ABET BAND | MINIMUM CREDITS | PRE-2009 NQF LEVEL | NQF LEVEL | QUAL CLASS |
| Undefined | 120 | Not Applicable | NQF Level 07 | Regular-Provider-ELOAC |
| REGISTRATION STATUS | SAQA DECISION NUMBER | REGISTRATION START DATE | REGISTRATION END DATE | |
| Registered | EXCO 0639/26 | 2026-04-16 | 2029-04-16 | |
| LAST DATE FOR ENROLMENT | LAST DATE FOR ACHIEVEMENT | |||
| 2030-04-16 | 2033-04-16 | |||
| In all of the tables in this document, both the pre-2009 NQF Level and the NQF Level is shown. In the text (purpose statements, qualification rules, etc), any references to NQF Levels are to the pre-2009 levels unless specifically stated otherwise. |
This qualification does not replace any other qualification and is not replaced by any other qualification. |
| PURPOSE AND RATIONALE OF THE QUALIFICATION |
| Purpose:
The Advanced Diploma in Big Data and AI Management is designed to address the evolving needs of the global and national workforce by providing specialised, career-focused education that bridges the gap between understanding Big Data and AI technologies and their application in strategic management. The qualification aims to equip learners with the knowledge and practical skills to leverage Big Data and AI for strategic decision-making and management. The qualification responds to the growing demand for professionals who can effectively integrate Big Data and AI into management practices, thereby enhancing the competitiveness and innovation of South African industries. It supports the national goal of socio-economic development by contributing to a highly skilled workforce capable of driving data-driven strategies across diverse sectors. Career possibilities for learners include managerial roles in sectors such as finance, healthcare, retail, logistics, and government agencies, where data management and AI have become increasingly significant. The qualification offers learners the opportunity to continue their professional development through the inculcation of a deep and systematic understanding of current thinking, practice, theory, and methodology in an area of specialisation. Upon completion of the qualification, qualifying learners will be able to: The qualification will further equip learners with the skills and knowledge necessary to become work-ready professionals adept at navigating the complexities of Big Data and AI within the context of managerial decision-making. The qualification purpose addresses both national and industry-specific needs, preparing learners to thrive in South Africa's evolving socio-economic landscape. Additionally, the development of critical thinking and effective communication skills supports learners in engaging thoughtfully with societal challenges and contributing actively to civil society. Rationale: The global business landscape is increasingly driven by data and artificial intelligence (AI) (Deloitte, 2019). Organisations across sectors are leveraging Big Data analytics and AI technologies to enhance decision-making, optimise operations, and gain competitive advantages (Gartner, 2021). This shift has created a niche demand for professionals who understand how to apply these tools strategically within a management context. The integration of Big Data and AI into business processes has transformed industries such as finance, healthcare, retail, and manufacturing. According to industry reports (Deloitte, 2019; Jansen, 2022), the global Big Data and AI market is projected to grow exponentially in the coming years. This growth is driven by the need for businesses to harness data strategically to make informed decisions, enhance customer experiences, and innovate products and services. However, there is a significant skills gap in the workforce, particularly in understanding how to apply Big Data and AI within a managerial and strategic framework. The qualification aims to address this need by preparing learners who can bridge the gap between understanding these technologies and their application in strategic management. Traditional management education often lacks a focus on the application of Big Data and AI in business contexts, while technical the qualifications may not sufficiently address their strategic and managerial uses. This qualification provides a balanced curriculum that integrates insights into Big Data and AI with core management principles. Learners will learn how to lead data-informed projects, interpret data-driven insights, and make strategic decisions that are enhanced by an understanding of AI's potential. The interdisciplinary nature of the qualification ensures that learners are well-equipped to meet the strategic needs of modern organisations. The qualification is designed for working professionals who seek to enhance their careers by acquiring a deeper understanding of Big Data and AI within a management context. The qualification is also suitable for recent learners with a background in business, management, information technology, or related fields who aspire to roles that require strategic insight rather than technical expertise. By targeting a diverse audience, the qualification aims to contribute to the development of a workforce that can effectively lead and strategise in a rapidly evolving technological landscape. In addition to its direct benefits to individual learners, the qualification contributes to the broader economy and society by fostering a workforce that can drive innovation and efficiency across various sectors. Learners will be prepared to lead initiatives that enhance business performance, thereby contributing to economic growth. Furthermore, their ability to apply data-driven strategies in private and public sectors will support societal progress. Therefore, the qualification bridges the current skills gap, ensuring that organisations and society can fully leverage the transformative potential of Big Data and AI. Career possibilities for qualifying learners include managerial roles in sectors such as finance, healthcare, retail, logistics, and government agencies, where data management and AI have become increasingly significant. As the qualification will be offered in the open distance e-learning mode of provision, the institution enhances accessibility and flexibility, thereby providing opportunities for working professionals and those in non-metropolitan areas to gain access to the qualification. The qualification aligns with the institution's mission to provide quality, relevant, and innovative education that meets the needs of the workforce. This qualification provides a career pathway, and learners will be able to articulate vertically into a Postgraduate Diploma or a Bachelor's Honours Degree, specialising further in management, business administration, business intelligence, or information management. |
| LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING |
| Recognition of Prior Learning (RPL):
RPL policy and practices will be applied in relation to the qualification. The principles of recognising learning that has already taken place will be applied at a modular level. The institution follows the CHE's Policies on the Recognition of Prior Learning, Credit Accumulation and Transfer, and Assessment (CHE, 2016), and in accordance with the policy, learners applying for admission to the qualification via RPL can only be exempted from a maximum of 50 % of the qualification modules. RPL for access: Learners need to be able to demonstrate their prior learning either through evaluation/verification and/or assessment. Each application will be considered on a case-by-case basis, and learners will enter into discussion with the RPL advisor to determine the requirements for the RPL assessment/portfolio of evidence. RPL for exemption: Although learners are rigorously assessed, as per the principles of RPL, there is no guarantee that a learner will gain admission into a qualification or receive exemptions from modules. While the institution is making provision for RPL, the institution adheres to the guidelines set by the CHE (CHE, 2016), and only 10% of a cohort will be admitted to the qualification on an RPL basis. All RPL applications will be adjudicated by the Academic Committee and Registrar: Administration. Entry Requirements: Or |
| RECOGNISE PREVIOUS LEARNING? |
| Y |
| QUALIFICATION RULES |
| This qualification consists of the following compulsory modules at National Qualifications Framework, Level 7, totalling 120 Credits.
Compulsory Modules, NQF Level 7, 120 Credits. |
| EXIT LEVEL OUTCOMES |
| 1. Identify, evaluate, and apply Big Data and AI technologies to enhance business strategies and operations.
2. Employ strategic leadership and effective change management in the integration of Big Data and AI into organisational processes. 3. Demonstrate an understanding of the ethical, legal, and social implications associated with the use of Big Data and AI in business contexts. 4. Employ effective project management skills specific to Big Data and AI initiatives, including planning, execution, and evaluation. 5. Demonstrate an understanding of data-driven decision-making by utilising analytics and visualisation tools to inform and guide organisational strategies. |
| ASSOCIATED ASSESSMENT CRITERIA |
| Associated Assessment Criteria for Exit Level Outcome 1:
Associated Assessment Criteria for Exit Level Outcome 2: Associated Assessment Criteria for Exit Level Outcome 3: Associated Assessment Criteria for Exit Level Outcome 4: Associated Assessment Criteria for Exit Level Outcome 5: INTEGRATED ASSESSMENT The institution applies an integrated assessment approach, as outlined in our Higher Education Assessment and Moderation Management Policy (BCCPP030). Assessment is designed to support learner learning and ensure the achievement of learning outcomes through a combination of formative and summative methods. These methods are appropriate to the qualification NQF levels and the open distance e-learning mode of provision. Formative Assessment: Formative assessments are used throughout the learning process to provide feedback and guide learner development. Integrated assessment ensures that theoretical knowledge and practical competencies are assessed in a coherent and meaningful way, often through contextualised tasks such as case studies, projects, and simulations. Summative assessment: Summative assessments evaluate the achievement of exit-level outcomes. One of the assessment modalities used to assess learners summatively is an invigilated examination. Invigilated examination can take place either non-venue-based or venue-based, as described below. The summative assessment, weighted at 50%, will assess the full range of knowledge and skills learners have developed throughout the course. Assessment practices are moderated internally and externally to ensure fairness, reliability, and academic integrity. The assessment strategy is structured to reflect the complexity and progression of the qualification, supporting the development of applied competence and preparing learners for the world of work. |
| INTERNATIONAL COMPARABILITY |
| This qualification was compared to the following international qualifications:
Country: Australia Institution: Deakin University (DU) Qualification title: Graduate Certificate in Artificial Intelligence for Business Duration: One year full-time. Entry requirements at least two years' relevant work experience (or part-time equivalent). Or If the learner has completed previous studies that the learner believes may reduce the number of units the learner has to complete at Deakin, indicate in the appropriate section of the learner's application that the learner wishes to be considered for Recognition of Prior Learning. The learner will need to provide a certified copy of the learner's previous course details so the learner's credit can be determined. Purpose/rationale: The Graduate Certificate in Artificial Intelligence for Business is designed to equip business professionals with applied knowledge and skills to identify suitable opportunities for artificial intelligence (AI), evaluate and use existing AI systems, and apply machine learning techniques to solve business problems. The programme emphasises responsible and ethical use of AI and develops learners' ability to communicate AI-related considerations to non-specialist stakeholders and recommend AI-enabled solutions aligned to organisational and societal needs. Qualification structure: Qualification outcome: This course provides learners with the skills and knowledge to use and evaluate AI systems to complement learners' existing business careers in job markets, including: Similarities: Differences: Country: Singapore Institution: Singapore University of Social Sciences (SUSS) Qualification title: Graduate Certificate in Artificial Intelligence for Business Duration: 12 months to complete. Entry requirements: Purpose/rationale: The Graduate Certificate in Artificial Intelligence for Business takes the X + AI approach. It equips business professionals from different industry sectors and non-IT functions with relevant AI knowledge and skills to apply and use AI in their respective job roles. The curriculum was designed around real business opportunities and challenges that can be solved through AI and Machine Learning (ML) technologies. The qualification consists of the following four (4) modules: Assessment: The qualification consists of a mixture of pre-class quizzes, participation, group-based assignments, and end-of-qualification assessments. Similarities: Differences: |
| ARTICULATION OPTIONS |
| The qualification articulates vertically with qualifications such as:
Horizontal Articulation: Vertical Articulation: Diagonal Articulation: |
| MODERATION OPTIONS |
| N/A. |
| CRITERIA FOR THE REGISTRATION OF ASSESSORS |
| N/A. |
| NOTES |
| N/A. |
| LEARNING PROGRAMMES RECORDED AGAINST THIS QUALIFICATION: |
| NONE |
| PROVIDERS CURRENTLY ACCREDITED TO OFFER THIS QUALIFICATION: |
| This information shows the current accreditations (i.e. those not past their accreditation end dates), and is the most complete record available to SAQA as of today. Some Primary or Delegated Quality Assurance Functionaries have a lag in their recording systems for provider accreditation, in turn leading to a lag in notifying SAQA of all the providers that they have accredited to offer qualifications and unit standards, as well as any extensions to accreditation end dates. The relevant Primary or Delegated Quality Assurance Functionary should be notified if a record appears to be missing from here. |
| 1. | Boston City Campus (Pty) Ltd formerly Boston City Campus and Business College (Pty) Ltd |
| All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source. |