Optimized 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.  

• Where to start in 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. 
• Fostering a data driven culture to create a holistic data eco-system for optimised results.  

• Uncovering business opportunities through competition monitoring facilitated by public web data intelligence.
• An effective top-down strategy for data operations that maximizes value and yields insights into the competitive landscape.
• How machine learning is changing core data collection processes and solves the most pressing data collection issues.
• An in-depth look at the pros and cons of using in-house versus outsourced data collection tools and the rationale behind each approach.

To follow soon …

• 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.

• 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.