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Think Tank

Optimisation of Data in a Complex Programme Space

Major programmes are increasingly crucial to get right first time, given the importance of using scare resources, supplementing conventional components and methods with new and sustainable ones, taking a responsible approach to funding and financing capital, managing the complex mesh of data, systems and partners, evidencing social and environmental value?and of course delivering a major asset that actually achieves its intended, long term outcomes. It's clear that an integrated, consistent and real-time view of data flows across this complex landscape is now a pre-requisite for success. Interface and integration management has to be designed in (technically and contractually), digitally powered and underpinned by behaviours that promote a shared goal. We shall explore how we can best architect, set-up, manage, optimise and of course secure data across complex programmes. 

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No Data-Pain, No Data-Gain: Fast-Tracking Value With Data At An Acceptable Cost

• Articulating the typical obstacles to deliver value with data
• Exploring the right position of data in the business value chains
• Describing an approach to determine what ?good enough? data means

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Using Data to improve Customer Experience through Data driven Personalisation'

Today's customers expect amazing experiences from all businesses. Experiences that are personalized across all touch points during customer lifecycle in a privacy safe environment.   The role of AI continues to evolve in the customer experience landscape and is now applied across more industries.   Businesses are building personalization strategies using cloud machine learning & analytics to attract new customers and gain a competitive edge.  
Data Leaders have an important role to play, and go beyond laying strong data foundations.  There is need unlock potential with focus on customer back by solid data governance practices Join me, Sid Shah in this session to discuss strategies to effectively harness customer data and build robust capabilities that help create personalization at scale.  Leverage data models and ML techniques to create new insights and best in class experience experiences.  The role of AI in improving customer experience and how can we ensure right practices and data ethics in place

Should be a fascinating discussion, See you there

Presented by:

Sid Shah, Global Head of Analytics & Insight, Conde Nast

 

 

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Making Data Driven a Reality - How we Plan to Embed a Data Culture in our Organisation)

On the face of it, being data driven is something you'd be mad not to aspire to.  Particularly when we hear about the amazing results data-driven organisations like Amazon and Google deliver.  So how do we translate that in a professional services firm? 

Our paper will explore what Data Driven means to an organisation like KPMG and how crucial it is to tie this to concrete outcomes. We will explain some of the challenges we have faced on our journey so far, the progress we have made, and what we are going to focus on in the next 2-3 years.

Presented by:

Lynne Bailey, CDO, KPMG UK

 

 

Sofia AlcalaPaterna, CDO, KPMG

 

 

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The Analytics "last mile" - where insight becomes value

Data and insight only create value if they result in better decisions. In this think tank, we'll discuss this ?last mile' of analytics - the point at which value is created - focussing on scenarios with a human in the loop. Human decision-makers will weigh up multiple factors when choosing a course of action, including input from data and analysis alongside other considerations - and will ultimately make a judgement call. 
This can create something of an attribution problem for analytics professionals. Decisions occur through an opaque, subjective interpretation step in a human's brain (very much a black box!), which combines data and analysis with many other factors. It can be tricky to disentangle the impact that data points or analyses have on decision-making processes.
This 'last mile' is often a weak link in the analytics value chain, however. During this session, we'll discuss three challenge areas which can contribute to this:

• Feedback Loop: Analytics teams do not always have clear line of sight on what actions are actually taken (eg which recommendations are acted upon)

• Cognitive biases: Human factors, such as confirmation bias, can derail the value of data and analysis - often in subtle ways which stakeholders may not be aware of

• Cultural incentives: The broader cultural environment can implicitly discourage stakeholders from seeking - or believing - data and analysis that might change (rather than support) their ?default' decision

Presented by:

Lucy Butler, Chief Analytics Officer, Financial Times