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
• Explainailabilty and fairness – Explaining a model does not guarantee fairness- (“Fairwhashing”)
• 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

• Details to follow…

On-Demand Presentation

• Examining the opportunities of digital and third-party data – Determining what’s relevant, for how long, and its practical applications for your business. 
• Strategies for determining relevancy and actionability – Hearing through the ‘data noise’ – For how long does digital data remain relevant, and for what duration should it be stored? 
• Is dark data a greater opportunity then third-party data? 

On-Demand Presentation

• According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
• Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
• Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
• Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
• Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
• A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals

On-Demand Presentation

• How can business value be shown at board level; both in quantitatively and qualitatively? 
• How do you quantify efficiency savings vs. cost savings and revenue? 
• Overcoming the hurdles to monetising your data and lessons learned on the journey. 
• How do you attribute business success to actual data?  
• Selecting the most important business challenges to solve using data science techniques to engage stakeholders and secure future investment. 

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

• 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