Day 1 - 19 June 2019
Justin van der Lande
Principal Analyst, Research
09:40AM - Day 1
Data Analytics for AI and IoT: Chair’s welcome and opening remarks
Edge processing for data analytics and training AI Algorithms
- How the huge influx of data will require fit-for-purpose architecture. What is the distance from the edge to your device and how to consider this during the creation of your IOT / AI architecture?
- Discussing how IoT / AI architectures need to be put in place to ensure increased compatibility across domains.
- Using cloud analytics platforms to derive value from IoT / AI data vs physical gateways -pros and cons.
Keynote: Information is everything
Data is the new oil is a familiar paradigm in 2019, but until we learn to process and derive actionable insights from this data how valuable is it? This talk will cover a real life case study where a business has successfully taken data generated by the IoT and converted into into real business actions. Hear about their journey, and their recommendations for uncovering new economic structures made available by access to intelligent data.
Emerging Use Cases for IoT Data Analytics
Discussing new use cases for the data produced by IoT hardware, from video analytics to customer product usage data that can aid marketing.
Panel: IoT and AI data analytics for intelligent decision making
- Identifying target-rich, high-value data that can be used to generate business intelligence
- Using cloud analytics platforms to derive value from IoT data
- Discussing the barriers to widespread IoT/ AI /Big Data value delivery and how these might be overcome.
- Real time data analytics in practice – examples of how IoT / AI data is creating business efficiency and revolutionising working practices
NoSQL for big data analytics: Best practice and use cases
- NoSQL vs Hadoop vs SQL
- Enterprise implementations and use cases
- Advantages of horizontal vs vertical scalability
- Ensuring greater performance with larger data sets
Afternoon Keynote: AI, Big Data and Autonomous Vehicles
Panel: The (Big) Data challenge
- How to collect data
- Quality versus quantity
- The labelling challenge
- How to handle noisy data – random noise versus systematic noise
What is the state of Hadoop today?
Hadoop has been one of the most important big data tools over several years – but if you look at the most recent report from Wikibon, vendors are not mentioning Hadoop as much as previously.
So why is this? The big cloud vendors have arguably been cannibalising Hadoop with their own storage layers while for many, object storage has become the de facto method of crunching big data. This session will explore the benefits and challenges facing Hadoop implementation, as well as trends in big data platforms, from object storage to stream computing.
Case Study: How to get the most out of Apache Spark
- Moving from testing and proof-of-concept through to production applications
- The industries set to be impacted – financial, manufacturing, pharmaceutical
- Flexibility and adaptability in workloads