Applied Data & Analytics

Chairpersons welcome and opening remarks. 

• Maximising harvests and minimising waste – how data and analytics have proven to be the ultimate game changer for the agriculture sector through real-time crop insights.  
• How dealing with smaller data pools can help reduce risks. 
• A collaborative effort – how combining capabilities of ML, IoT and drones is continuing to help the sector flourish and meet growing demands in a sustainable way.

This session will cover the holistic approach of using a data fabric including:  

• Frictionless access for teams. 
• Data sharing capabilities. 
• AI driven automation. 

• Identifying and breaking down silos across departments and business data stacks.  
• Aligning the data with specific business goals – making use of insights for targeted business e.g. seasonal buying cycles and customer demand.  
• From big to small – are small data sets going to be the norm for reduced risks moving forward?

• Understanding the real value of data before trying to add a price tag to deliverables.  
• How to measure data through KPI’s – creating a holistic business strategy where data is part of the chain.  
• Discoverability and data quality – the key pillars for being able to action and monetize capabilities.   
• Being adaptable and open to innovation.

• Digital identities – looking at the role of public sectors in relation to data for digital identities in countries and cities.  
• The need for consumers to have control over their own data to help build trust. 
• Looking at public private data sharing models to monetize data. 
• Data sovereignty – who owns it? Why regulations and frameworks are desperately needed.

• How best to understand and prioritise business processes in order to contextualise data modelling decisions? 
• Understanding the tools and techniques involved; what recent innovations have increased efficiency in data modelling?  
• The trade-off between complex data models and future efficiency – where is the line? 

• Identifying target-rich, high-value data that can be used to generate intelligence. 
• Transitioning from big data to ‘right’ data analytics – how to dissect the data you need. 
• Collection, storage and exchangeability – how to ensure optimised processing of data for optimum response and actions. 
• Data as the core of digital – aligning strategies to maximise data capabilities. 

This session will look closer at hybrid data storing, including why it is important, why enterprises needed different storage capabilities e.g., cloud Vs onsite, and the opportunities for innovation here.

• Centralised Vs de-centralised models – allowing for self-service analytics and non-technical users to access insights and make strategic decisions quickly.  
• Allowing for job growth and diversity as those without technical backgrounds can utilise data-led decision making.  
• Where data models need to alter to fit this de-centralised shift – is data mesh architecture the answer for irregular, scattered data?