Optimised Data & Analytics

Chairpersons welcome and opening remarks. 

• 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? 

This session will delve into the current Data as a Service (DaaS) models, and how enterprises can capitalise on these services within markets.  

• Identifying and breaking down silos across departments and business data stacks.  
• How to maximise data sets and data pipelines.    
• Aligning data with specific business goals – making use of insights for targeted business e.g. seasonal buying cycles and customer demand. 
• Collection, storage, exchangeability and security – how to ensure optimised processing of data for optimum response and actions. 

This presentation, while providing some historical insights into Natural Language Processing (NLP) advancements, highlights the risks of hastily jumping onto the AI bandwagon without a well-defined strategy. We explore the possible challenges in neglecting key factors such as data quality, monitoring and domain expertise while building data products. By understanding the past, attendees will gain a sharper perspective on building reliable and impactful AI data products. Let’s walk confidently before sprinting into the AI revolution, ensuring a solid foundation for success.

• The advantages of decentralizing data  
• Utilising the enterprises strategic advantage – how to unlock data’s value.  
• Studying current processes for capturing data and looking at next steps.  

• Leveraging data and analytics to inform and shape strategic priorities 
• Harnessing data to identify solutions and create a plan 
• Using analytics and visualisation to operationalise and track execution 

• How to get the most out of real time intelligence – what process need to be in place to aid this?  
• Tackling the issues around legacy systems and the lack of available data for intelligent decision making – what are the current market solutions that can help?  
• How being adaptable can lead to optimised services, reduced downtime and in some industries, help save lives.  
• Taking the next steps to semi and fully autonomous systems enabling advanced analytics. 

• Understanding regulatory and data privacy obligations – how to maximise trust and compliance across assets. 
• How to ensure your data environment is giving you the best opportunity to govern access and storage.  
• Best practices in DataOps for governance and compliance.  
• A future with synthetic data generation.  

• What traditional pricing strategy looks like and its challenges
• A more scientific approach to choosing the optimal price point
• AI model to predict sales as a basis for the price elasticity model
• Results validation and deployment