Thursday 7th November, 2019
The Seminars will take place from 12:15 – 13:00
Delegates will be able to attend one seminar at the event. No pre selection is required – delegates will be able to select which session they attend onsite.
Auditorium (Main Conference room)
Understanding Your Customers Inside Out: Using Data Assets to Balance Customer Retention and Profitability in a Shrinking Market
Simon Kelly, Director – Business Intelligence Group, CACI
The balance between customer churn and profitability is never an easy one. In a highly competitive market, even the smallest change in rate plans can have a significant impact on a company’s overall revenue.
Learn how one of Britain’s largest telecommunication companies combines big data technology, complex prediction algorithms and visual analytics to understand customer behaviour and its impact on the company’s profits. What was once a guessing game is now a more exact science and a key enabler in strategic pricing decisions.
Seeing is Believing: A Practical Look at the Path to Enterprise AI
Larry Orimoloye, Data Scientist, Dataiku
Enterprise AI is a target state where every business process is AI-augmented and every employee is an AI beneficiary. But is that really attainable? And, if so, how can an organisation practically leverage their data to achieve this?
In this talk, Larry Orimoloye, Solutions Architect at Dataiku and visiting researcher from Cambridge, will share practical examples of how AI has been leveraged in the industry to solve real business problems. He will show how companies of different sizes and across different sectors have begun this journey towards enterprise AI. And while some are farther along than others, by making the right decisions now and avoiding stumbling blocks, you too can supercharge your quest to this AI-fuelled future.
Data Modelling for Big Data & Analytics Projects with Apache Cassandra
Patrick Callaghan, Business & Technology Strategist, DataStax
Big Data and Analytics initiatives depend on data to work. That data has to be modelled and managed well to get value out of it. However, the approach we take around data modelling can be influenced by the ways that we store data over time.
Understanding this in advance can make it easier to query our data, and also avoid problems caused by bad decisions or overlooking data modelling at the start.
This session will help you understand how to build a strategy and implement a data model on Apache Cassandra to support your Big Data projects.
Welcome Aboard! An Exciting Journey of Big Data Analytics in the UK Rail Sector
Dr Apurva Sinha, CTO – Transport and Manufacturing, Hitachi Vantara
The UK railways are one of the busiest rail networks in the world. The demand for the UK rail network has been growing dramatically since the early 90s and expected to double within the next 8 years. Market structure and rising demand rewards innovation in the rail sector unleashing a myriad of opportunities for analytics. Rolling stock predictive maintenance, optimization of supply chain operations, analysis of train delays and knock-on effects, video intelligence on trains station to improve safety – this is just a few examples of big data analytics applications in rail domain.
In this seminar, we discuss
• Types of data sources in rail and how to work with them efficiently
• Bringing value to the rail sector through data analytics and technology
• Facilitating collaboration between data scientists and rail domain experts
Bridging the AI Gap: Uniting Data Scientists and Data Engineers for Accelerated Analytics Insight Using Talend
Ben Saunders, Expert Solutions Engineer, Talend
Today, many Data Science teams are choosing to leverage the power of MLaaS (Machine Learning as a Service) platforms on Azure, AWS and Google Cloud for developing and operationalising their AI and ML pipelines for predictive analytics. There are huge advantages to working this way such as lower TCO, accelerated development and scalable cloud solutions. However, this migration to MLaaS platforms is not without its challenges. Firstly, Data Scientists still spend far too much of their valuable (and costly) time locating, preparing and cleansing data prior to modelling. Secondly, integrating new ML pipelines into existing applications (typically managed by different teams) can be difficult and often slow, meaning many organisations struggle to achieve the desired ROI from AI initiatives. This has led some companies to build entirely new (often segmented) teams to manage the operationalising and deployment of their ML pipelines using a process called MLOps. If only solutions existed to make the hand off between Data Scientists and Data Engineers more seamless leverage existing processes and practices…
In this session we demonstrate how Talend can be used to dramatically enhance the productivity of your Data Science and Data Engineering teams by continually delivering high quality, trusted data to the Cloud whilst also enabling a viable MLOps solution.