Big Data & Analytics

• Making data the igniter of decision making: End to end processes with data (Product, Marketing, Sales, etc.).
• Generating actionable insights from data to improve performance across business.
• Installing a data-driven culture throughout the organisation.

On-Demand Presentation

• Classical fallacy: just because my model does not use gender (or any other protected attribute) it is not enough to guarantee fairness on those attributes
• Ways to introduce unfairness in an unintended way – the machines do exactly what you instruct them, no more, no less
• Explainalabilty and fairness – Explaining a model does not guarantee fairness- (“Fairwashing”)
• Metrics and Trade-off’s in designing robust, explainable and faired algorithms- Pareto curves and the cost of fairness
• The future of fairness

On-Demand Presentation

• Discussing the ecosystem of data warehouses/lakes/the cloud, what is needed to build an effective analytics environment. Examining the benefits and pitfalls of each approach. 
• Exploring what is needed in this new world of analytics, with ever growing data sets, fringe data, upgraded tools and processes and security threats, how do you build the right architecture to handle this whilst remaining agile and adaptive?  
• Do you have access to the data you need, and if not how do you go about gaining access and organising that data?  

Live webinar panel

• With 15+ years of experience in business-critical projects and almost a decade in large-scale data-driven systems based on Neo4j, other NoSQL technologies and streaming process tools, beginning this year LARUS has started a new challenge heading to graph explainable artificial intelligence (XAI).
• When we use AI to predict the risk of an investment, take lending decisions, provide explanations for high-scoring cases of suspected fraudulent activities or recommend a medical treatment, black-boxed AI is not suited: human experts must be able to validate the truth of AI produced results and gain new insights and graph technologies add the required context for this level of explainability.
  At AI & Big Data Expo Larus will talk about “Galileo XAI”, the brand new LARUS graph-based platform for explainable AI powered by Fujitsu Deep Tensor®.

On-Demand Presentation

Operational analytics can provide valuable real-time insights for Data Science but only if they can avoid the time consuming process of Extracting, Transforming and Loading data.

In this session you will hear how organisations in multiple industries, such as Dominos, Credit Agricole and Tikeasy are delivering real time customer segmentation and behaviour or performing complex price computations and data segmentation for Financial Risk Management. You’ll learn how Couchbase, a distributed, NoSQL document-oriented database with a dedicated Analytics service is helping these companies and others gather in Real time operational insights for dashboard and reporting using analytical queries without impacting the operational data nodes.

On-Demand Presentation

• Data monetisation – Turning your data into a true area of value generation. 
• At what point does your data become a capital asset and how do you value it? 
• Strategies for monetising customer interaction data – The importance of lifetime customer value and not just short term gains. 

On-Demand Presentation

• Building a cohesive business strategy for digital transformation – Who are the key stake holders involved and do you need dedicated C-Level representation and a dedicated department? 
• Defining strategies for working with multiple stakeholders – how to combat internal resistance and how does this differ depending upon business size, industry and culture. 
• How can value and ROI be quantified? And what specific KPIs can you put in place when working across many lines of business teams and projects? 
• Decision for the purchase and consumer journey – understanding the customer journey to help inform investment and service development decisions 
• Analytics and Digital Transformation – What metrics can you use for measuring that return, and what should be outputs/outcomes be? 

On-Demand Presentation