Big Data and Business Analytics Innovation Summit

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.

Focus on frameworks that ensure data governance, ethical model development, explainability, auditability, and compliance — building trust in data-driven decisions across the organization.

Explore techniques such as federated learning, differential privacy, and encryption to analyze sensitive data securely — enabling cross-organizational collaboration without compromising privacy.

Dive into how maintaining high-quality, consistent, and traceable data (through lineage and MDM) underpins reliable analytics, compliance, and accurate reporting across business units.

Compare modern approaches to data architecture — data lakehouse, data fabric, and data vault — to support efficient ingestion, management, storage, and governance at scale.

Discuss the design and implementation of streaming pipelines (e.g., Kafka, Flink), event-driven systems that process and analyze live data for dynamic decision-making and rapid business response.

Examine how organisations can leverage big data and advanced analytics to detect fraud, monitor risk, and secure operations — turning security data into actionable insights.

Explore the use of machine learning and artificial intelligence to build forecasting models, optimize supply chains, pricing, and resource allocation — enabling smart, data-driven operational decisions.