Big Data Oct 2026 Day 1

Explore how a data mesh approach distributes ownership to business domains, empowering teams to treat data as a product and scale analytics with agility, collaboration, and domain-specific governance.

Discuss processing data at the edge (IoT devices, sensors) to reduce latency, enhance responsiveness, and enable real-time insights for industries such as manufacturing, retail, and logistics.

Examine strategies for combining on-premises, cloud, and multi-cloud data infrastructures to balance cost, scalability, resilience, and performance across enterprise analytics environments.

Investigate AI-powered self-service tools that democratize data, enabling business users (non-technical) to generate insights, build models, and drive decisions without relying entirely on data science teams.

Highlight how businesses can use predictive models to forecast trends and prescriptive analytics to recommend actions — helping to optimize operations, reduce risk, and proactively seize market opportunities.

Unpack ways organisations can turn data into revenue streams: creating data products, partnering for data marketplaces, and leveraging analytics to generate customer value and business profitability.

Show how big data and analytics enable businesses to measure and optimize environmental, social, and governance (ESG) performance — aligning data strategy with sustainability goals.