Emerging insights from the Disruptive Trends Report suggest that driving efficiencies and product creation are likely to be top priorities for leaders in 2023.

Now that businesses are data rich and digital first, data analytics is make or break for achieving these mission critical goals. So how can data analytics be transformed to drive more efficiencies and better business outcomes?

As purse strings tighten and new revenue streams become essential, now’s the time to double down on extracting more value from your data. Join our virtual round-table to dive into the latest trends in data analytics and hear about the real-world business innovations emerging as a result.

AGENDA

08:45 Welcome and introductions

08:50 Member’s Case Study: Transforming Data Analytics to Build Better Products and Services

Pedro Rente Lourenco, Global Head of Data Science, Laing O'Rourke 

09:10 Fireside chat: Making Data Analytics More Efficient

Ben Cooper, Sales Director, Unravel Data

09:20 Roundtable discussion

• What new business models, products or services have you recently developed from data analytics?
• How do you control costs and demonstrate the ROI from data analytics?
• How efficient is your current approach to data-ops?
• How can your teams more effectively obtain actionable insights from data?
• What problems emerge when trying to scale data analytics across your business?

10:00 Close

Hear from...

Pedro Rente Lourenco, Global Head of Data Science, Laing O’Rourke

Throughout his career, Pedro has been building applications of AI to different industries, including healthcare, retail commerce, e-commerce, telecommunications and more recently construction and manufacturing. He is inspired by the value to be unlocked from large and small datasets, with a strong business focus. Previously, he led data teams at Vodafone Group where he launched several initiatives of data for social good, including a global COVID-19 analytics platform to help governments and NGOs respond to the pandemic. His main focus areas are business-driven data science, data and AI ethics, fairness and sustainability, as well as applied R&D and technology transfer.