Day 2 - 20 June 2019

Big Data for Industry

Over the next few years, data and analytics will become even more crucial across all industries. Day 2 will look at case study examples of how big data is being applied in practice, the rewards and challenges of implementing these strategies.


Big Data for Industry: Chairman’s Welcome

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Keynote: AI and Machine Learning Landscape

  • A look at the lasted trends and buzzwords
  • How to cut through the hype
  • Technology breakthroughs
  • How to get started
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Panel: Predictive analytics for customer recommendations

  • How to deliver truly personal recommendations
  • Enhancing suggestions using self-learning algorithms
  • Quantifying and unlocking the potential unstructured data
  • Getting a 360 degree view of customers
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Networking Break


Big data, machine learning and healthcare

  • How machine learning is claimed to be creating algorithms which can outperform humans
  • Exploring the application of machine learning to big healthcare data
  • The differences between application of machine learning and deep learning in healthcare
  • Ethical concerns and roadblocks – MDDUS warns of ‘inherent risks in remote consulting’
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Panel: The big data crunch: Transforming healthcare

  • How much data does the healthcare industry have? What isn’t being utilised?
  • Assessing the sheer amount of data the healthcare industry has and why it will take a long time to be digitised
  • The rise of the Internet of Things – apps, wearable devices, sensors – in creating new data streams
  • How other data – food shopping purchases, social media – can be utilised
  • How this can all fit together to create a more streamlined, holistic view of the patient
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Networking Lunch


Keynote: Big Data and Manufacturing

  • Using Big data to solve design, manufacturing and after-sales support problems
  • Challenges faced when transitioning to become a data –enabled company
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Panel: How data science is making waves in finance and fintech

  • Deep learning predicting financial markets
  • Use cases of deep learning/ML crunching big data – automated risk management, fraud detection, algorithmic trading
  • Ethical concerns – is this playing the game or gaming the system?
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Case study: Big Data and Financial Services

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Big data in media and Entertainment

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Session Close