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AI providers to South African financial institutions
18 named organisations that answered a questionnaire for AI technology providers to South African financial institutions describe how they contract for, build, test and govern AI. “Provider” here refers to the questionnaire they answered, not their main business: most are insurers and reinsurers as well as some software companies. The questions cover liability and contract types, how accuracy and reliability are assured, training methods and training data, use cases, audit frequency, governance frameworks, ethics, consumer protection, data privacy, how financial soundness is assessed, investment, customer support and complaints, emerging technologies, regulatory compliance and its challenges, and how impact is measured. The data suits descriptive and multiple-response analysis of AI practices, and comparison with what financial institutions report about their own AI use. Each respondent is named, so answers can be read company by company
Data SetQualitative18 rowsPrimary dataQuantitativeView the data →AI adoption in South African financial institutions
220 South African financial institutions that use AI, including investment managers, insurers, banks, payment providers, fintechs, lenders and pension funds, answered 156 questions on how they use and govern AI and generative AI. The questions cover the expected AI market and their own AI investment for 2024, how material AI is across 17 business areas, ratings of 20 business areas and 35 AI risks (data, model, governance, consumer and operational), machine learning and generative AI use cases, regulatory and other constraints, governance frameworks, explainability methods, accountability and ethics, plus six open questions on customer disclosures, model validation, safe adoption, regulation and examples of AI use. The data suits comparison across sectors, ranking of risks and constraints, cross-tabulation, reliability and factor analysis of the rating blocks, and thematic analysis of the open answers.
Data SetMixed methods220 rowsPrimary dataQuantitativeView the data →How adults use the internet when seeking health care
213 adults, from 18 to over 65, rate how often they use their phones and the internet for everyday tasks and for health: researching symptoms, calling doctors, following up on visits, watching medical videos and joining online health forums. The survey covers smartphone and daily internet use, 11 everyday online activities, where people go for care (hospital, over-the-counter medicine, traditional healers) and how they use digital channels to get a diagnosis or follow-up. Every respondent answered every question, so the file suits group comparisons by age and gender, scale building and ranking.
Data SetQuantitative213 rowsPrimary dataQuantitativeView the data →Mini Research on Information overload, screen time and wellbeing among employed adults
20 employed adults answered an online survey on their daily screen time, physical activity, physical and mental complaints, and their experience of information overload, including about COVID-19. The questions cover time on devices, eye strain, back, neck and limb pain, headaches, internet addiction, stress and sleep, how often and from which sources and social media they felt overloaded, and agreement ratings for 16 sources and 18 kinds of COVID-19 information. The data suits descriptive analysis and simple tests of association between screen time, overload and wellbeing.
Data SetMixed methods20 rowsPrimary dataQuantitativeView the data →Fleet monitoring systems in mining operations
12 mine overseers, engineers, managers, a mines inspector and fleet service providers in South African mining wrote answers to 28 questions on fleet monitoring systems. The questions cover their role and experience, the systems they use, how fleet monitoring affects productivity, safety, maintenance and decision-making, how its impact is measured, and how leadership, organisational culture, communication, resistance to change and training shape adoption. The data suits thematic analysis and studies of technology adoption using models such as TAM or diffusion of innovations.
Data SetQualitative12 rowsPrimary dataQualitativeView the data →ICT project misalignment in the public sector
50 staff in the ICT unit of a South African national government department rated 26 statements on why ICT projects fall out of line with organisational change initiatives and what it costs. The statements cover the effects of misalignment (cost, delays, quality, stakeholder discontent, value of IT investment), its causes (resistance to change, resources, leadership, unclear objectives, stakeholder involvement, communication, technology, vendors), the challenges experienced and gaps in dynamic capabilities, so the data suits descriptive ranking of causes and effects, reliability analysis and correlation between sections.
Data SetQuantitative50 rowsPrimary dataQuantitativeView the data →Police discipline and misconduct handling in North West province
198 members of the SAPS, community policing forums, IPID and the Civilian Secretariat for Police in North West rated 34 statements on police discipline on a five-point agreement scale. The statements cover the level of discipline and public trust, how misconduct cases are investigated and finalised, training and supervision, zero tolerance for misconduct, and the discipline strategy, leadership and organisational culture, so the data suits descriptive comparison of agreement, reliability and factor analysis, and correlation or regression between these areas.
Data SetQuantitative198 rowsPrimary dataQuantitativeView the data →Body composition, fitness and nutrition knowledge of primary school children
155 children aged in Potchefstroom had their height, weight and body fat measured, completed two fitness tests and answered a 10-item nutrition knowledge quiz. The data covers BMI and BMI z-score, body fat and fat-free mass from bioelectrical impedance, standing broad jump, 20 m shuttle run and predicted VO₂max, nutrition knowledge scores, sex and household socio-economic quantile. It suits group comparisons (boys vs girls, weight categories), correlation and regression between body fat, fitness and nutrition knowledge.
Data SetQuantitative155 rowsPrimary dataQuantitativeView the data →Mobile reporting of community health events in Lesotho
336 village health workers and health staff in Lesotho rated 19 statements on delayed reporting of community health events and a mobile reporting tool (BKM), and described current reporting challenges in their own words. The statements cover the importance of timely reporting, ICT skills, ease of use, usefulness, attitude towards technology and intention to use the tool, so the data suits technology-acceptance analysis (reliability, factor analysis, regression or SEM) alongside a short thematic analysis of reporting barriers.
Data SetQuantitative336 rowsPrimary dataQuantitativeView the data →How informal manufacturers cost and price their work
Twelve owners of small informal manufacturing businesses explain how they work out what a job costs them and what to charge for it, and how they learned to do this. The businesses are in welding, carpentry, textiles, aluminium and upholstery. The interviews cover each owner’s background, how they cost a job, how they set prices, mistakes that cost them money, competition and whether the business supports their household. Every answer is in the owner’s own words, often with real Rand examples. The data suits thematic analysis across participants as well as comparing one business with another.
Data SetQualitative12 rowsPrimary dataQualitativeView the data →Social media and gender-based violence: views of university students
University students rate how far social media facilitates gender-based violence (GBV), which features of social media make it easier, and how social media could help curb it, then describe other factors and the solutions they would propose. Respondents come from three faculties (Engineering and Technology, Education, and Management Sciences) and all four years of study. The questionnaire combines 11 agree–disagree statements, one 1–5 rating, an overall judgement of social media’s influence on GBV, and two open questions.
Data SetMixed methods276 rowsPrimary dataMixed methodsView the data →Repo rate, CPI, M3, USD/ZAR, Brent crude, manufacturing production, SAVI and JSE sector returns: South Africa, monthly, 2015–2024
Monthly figures for South Africa from January 2015 to December 2024 (120 months) on ten variables: Repo Rate, CPI, M3 Growth, USD/ZAR, Brent Crude Oil Price, Manufacturing Production Index, SAVI, and the JSE’s Financial Return, Industrial Return and Resource Return.
StatisticGDP at current pricesReal GDP at constant 2015 pricesReal GDP growthView the data →Unemployment and jobs in South Africa’s nine provinces
Unemployment and jobs in South Africa’s nine provinces, for everyone, women and men, every quarter from Q1 2016 to Q2 2026. It covers the official unemployment rate and the number of people employed, unemployed and in the labour force (ages 15 to 64, to the nearest thousand). Women and men by province are Intelligent Data estimates from Stats SA’s Quarterly Labour Force Survey microdata; every quarter’s totals match Stats SA’s published figures.
StatisticUnemployment rateEmployedUnemployedView the data →How Mahikeng residents rate municipal procurement, by age, 2024
This is a free extract from a larger dataset: a community survey of 314 adult residents (August 2024) on how municipal procurement affects jobs, local business and service delivery. The full dataset includes every response, residents’ own words, coded themes and ready-made tables by sex, age, employment and project benefit. What you get with the full dataset Mahikeng service delivery survey 2024: full dataset (Excel) The free chart shows one view: agreement with ten statements by age group. The full dataset has everything behind it. Every response. One row for each of the 314 adult residents, anonymised and cleaned. Duplicate submissions and under-18s have been removed. All ten ratings on the 1–5 scale, with age group, sex, employment status, and whether the respondent or their family benefited from a local economic development project.
Statistic18-2425-3435-44View the data →