Big Data 2025 Day 1

Faith Sithole

By Faith Sithole: Lead - Data Science and Analytics at Sasfin | Multi-award Winning Author | Woman in Data| Corporate Key Note Speaker

Tamu Dube PhD,MBA

As data becomes a critical asset, monetizing it responsibly is key. This session explores strategies for turning data into revenue streams through insights, partnerships, and marketplaces while ensuring compliance with evolving regulations and maintaining trust with customers.

Ivan Sabljak

Quantum computing is reshaping big data by processing complex datasets exponentially faster than classical systems. Learn how businesses are leveraging quantum algorithms for optimization problems, predictive modeling, and fraud detection, pushing the boundaries of analytics and delivering transformative results in finance, logistics, and advanced AI applications.

Gary Allemann

Analytics platforms expose sensitive data to new risks. This session details common vulnerabilities and shows how to implement a dynamicsecurity layer, enforcing data masking, preventing unauthorized access, monitoring user activity, and blocking threats before analytics tools even receive the data, ensuring data integrity and privacy

Bryan McLachlan

In an era where use of big data, analytics and AI is expanding dramatically, AI/ML models in large organisations are often built and implemented using disparate data, across multiple environments, using multiple platforms and technologies. At the same time various regulators and business risk functions are asking questions about their reliability, ethical integrity and compliance with regulations and business rules and policies. How can data and analytics professionals be confident that their AI/ML models are performing optimally and at the same time not introducing risks to the organisation, or its customers and stakeholders. Content to be covered includes: • Ensuring models are developed ethically and in alignment with governance standards. • Tracing every feature used back to source data including every step of data transformation and engineering. • The importance of clear documentation for risk and compliance officers, auditors, and regulators? • Explainability and interpretability of models and decisions resulting from the models. • How to include business policies, rules and “human in the loop”. • Tracking model performance in real time. • Mitigating the risks effectively when models do fail.

Kutlwano Ngwarati

Many businesses want to be “data-driven,” but the reality is that most end up with overworked teams, disconnected dashboards, and very little impact. This keynote gives leaders a practical roadmap to build a lean, human-centered data strategy — one that prioritises the right metrics, automates the grunt work, and frees people to focus on high-value decisions. Attendees will walk away knowing how to scale data maturity without scaling burnout.

Mohamed Khan

Edge analytics processes data at the source rather than sending it to centralized systems. This topic explores its applications in IoT, retail, and smart manufacturing, emphasizing how it reduces latency, enhances security, and provides real-time insights critical for dynamic, data-driven environments.

Norbit Williams

With AI's growing influence, addressing biases in big data algorithms is crucial. This session will explore frameworks and strategies to build ethical AI systems, ensuring fair outcomes in hiring, credit scoring, healthcare, and other sectors while promoting transparency and accountability.