Programme @


Big Data Analytics

16 June 2022

Millennium Gloucester Conference Centre, London

Programme @ BDA

Morning Session

moving forward in a data-driven way with your users, customers and business working together

  • Data in motion: moving your data at the speed of business
  • From the edge to the cloud and everywhere in between
  • What’s out there? Marrying your disparate data sources
  • From vendor to in-house – How to reimagine your analytics landscape
  • Graph data science: what can it do for you?
  • Getting to know your customers

09:00 (BST)

Conference Chair's Opening Address

Dr Gilad Rosner
Founder, IoT Privacy Forum; Expert in Public Policy of IoT & Identity Management; Privacy and Technology Policy Researcher
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Dr Gilad Rosner, Founder, IoT Privacy Forum; Expert in Public Policy of IoT & Identity Management; Privacy and Technology Policy Researcher

09:10 (BST)

How to create a real competitive advantage with technology?

Elvys Nunes
Global Head of Category Insights and Analytics, Reckitt
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Elvys Nunes, Global Head of Category Insights and Analytics, Reckitt

Insights, intelligence, and analytics teams face more challenges than ever before: tight budgets, more demand and lack of resources.

You need to find the right tech partner to solve a business need or bring a competitive advantage to your business.

• What are the challenges of choosing the right technology for your industry?
• How to master insights processes with partnerships?

09:25 (BST)

From the edge to the cloud and everywhere in between

Organisations are forever searching for new and innovative ways to share and collaborate on data in order to maximise the value it has to the business.

Such a preoccupation has only served to become an obsession thanks to the enforced workforce distribution of the pandemic.

One such transformative way in which to do so is with a cutting-edge data cloud centre.

We address:

  • Why data cloud over other platforms?
  • Its architecture, structure and performance value
  • How it can match workload size and scalability challenges

09:40 (BST)

How are making complex financial decisions easy through using data (Simples!).

Maaike Van Den Branden
Associate Director - Data Insights & Analytics,
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Maaike Van Den Branden, Associate Director – Data Insights & Analytics,

Offering price comparisons for over 20 insurance, financial and digital products, has collected a lot of customer data from a large part of the UK adult population over the years.

Sorting out financial household management is perceived as a hassle by many of us, so is using that data to make customers’ lives easier and do some of the heavy liftings for them.

In this presentation we would like to share some of the journeys CtM has been on:

• How we realised that customers weren’t interested in more tools to help them, they needed some of the work taken away from them

• How we decided that our technical landscape had to change to accomplish this

• How a change in the company-wide operating model supported a test and learn approach to achieve this ambition

• How machine learning techniques allowed us to become more relevant to our customers

• How bringing people together changed the dynamics and speed of delivery of our company

• How we formed our dream team to enable this

09:55 (BST)

Working at Government Scale

Andrew Morgan, 6point6

Join this session to find out how you can succeed with data assurance at scale. We address the challenge with data profiling tools, and explain how we’ve been working to deliver data quality at scale with the UK Government.

10:10 (BST)

Sky Finance Analytics Case Study

Rajiv Chandra
Head of Finance Analytics, Sky
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Yixiang Dong
Head of Finance in cost Analytics, Sky
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Rajiv Chandra, Head of Finance Analytics, Sky

Yixiang Dong, Head of Finance in cost Analytics, Sky

10:25 (BST)

case study: From vendor to in-house - How to reimagine your analytics landscape

Transitioning from a vendor-based data warehouse to a homegrown open-source-based platform has become a noticeable trend amongst end-user organisations.

No small undertaking, its scale and complexity are one which many fail to navigate successfully.

However, with a well thought out strategy, breakthrough technological innovations and a well-drilled team, it can be achieved.

We highlight one such organisation which took the path of establishing its own, bespoke data analytics platform, and the key lesson learned along the way.

10:40 (BST)

Questions to the Panel of Speakers

10:55 (BST)

Refreshment Break Served in the Exhibition Area

11:25 (BST)

Welcome to Session Two

11:30 (BST)

Delivering a holistic picture of the business

Christina Finlay
Director, Data & Analytics, Nest
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Christina Finlay, Director, Data & Analytics, Nest

We address, using data to break down organisational silos, how to make data a team sport, and drive simplicity in reporting.

11:45 (BST)

How LexisNexis® ThreatMetrix® is using near real-time analytics to reduce transaction fraud for global brands by verifying customer identity

Matthias Baumhof
CTO, ThreatMetrix Business Services LexisNexis Risk Solutions
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Matthias Baumhof, CTO, ThreatMetrix LexisNexis Risk Solutions

ThreatMetrix® uses analytics to verify identities and reduce fraud as a transaction occurs, supporting thousands of global brands across tens of thousands of web properties.

– Learn how ThreatMetrix overcame performance and reliability challenges by switching to Yellowbrick for their real-time analytics needs, transforming their product offering.
– Understand how ThreatMetrix operates at high performance, low latency and at massive scale.

12:00 (BST)

AI: Buy vs Build

Joe Murfin
Lead Data Scientist, Saint Gobain
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Joe Murfin, Lead Data Scientist, Saint Gobain

In 2022 there is a wide variety of data science and AI solutions on the market. Many solutions already exist to cover most common needs so when should you purchases and off the shelf data science and AI solutions and when should you consider building in house?

• The landscape of data and AI solutions
• Buy – purchasing an AI solution (pros and cons)
• Build – making an AI solution (pros and cons)
• What is most appropriate in which scenario?

12:15 (BST)

Questions to the Panel of Speakers & Delegates move to the Seminar Rooms

12:30 (BST)

Seminar Sessions

13:15 (BST)

Networking Lunch Served in the Exhibition Area

Afternoon Session

Solving your biggest issues, identifying best practice and realising your greatest ambitions

  • Why the democratisation of data accessibility matters?
  • Modernise your IA
  • Building assurance into your ML production pipeline
  • Predict and optimise your business outcomes
  • Fighting misinformation with big data and AI
  • Data governance as a business enabler
  • Mitigate risk and accelerate insights

14:00 (BST)

Conference Chair’s Afternoon Address

14:05 (BST)

Why the democratisation of data accessibility matters?

Many organisations are still yet to expand the accessibility of ML, and by extension AI, beyond their data science teams.

This is due in large part to most enterprises have not put in place the right machine learning platform for their business, which in turn prevents this truly transformational tool from being democratised and adopted across the business.

We address, why it is never too late to bake into your business the competencies required to maximise the value of ML and AI, why ML is the primary driver of enterprise innovation, and the strategy you need to secure your ML breakthroughs.

14:20 (BST)

Measuring and evidencing the value of diversity and social mobility in data and analytics recruitment

Jeremiah Gogo
Head of Data & A&R Analytics, Atlantic Records
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Jeremiah Gogo, Head of Data & A&R Analytics, Atlantic Records

14:35 (BST)

case study: Modernise your IA

Chester Enslin
Head of IT Enterprise Integration and Software Quality Assurance, JSE
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Kevin McKerr
Sales Leader, BITanium Consulting
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All successful AI stories begin with the effective sourcing, storing, interrogation and utilisation of data.

Quite simply, AI cannot exist without a reliable, robust, and well-structured information architecture.

Still, being able to gain business value and insights from data is not always easy. From legacy infrastructure to data silos, failure to secure a holistic view of all your sources severely limits the value of AI.

We address:

• What makes the best data foundation for AI?
• How to integrate and infuse across the business
• Ensure such a foundation is open and flexible
• Establish streamlined access to data no matter the type or where it resides

14:50 (BST)

Questions to the Panel of Speakers

15:05 (BST)

Afternoon Networking and Refreshments served in the Exhibition Area

15:35 (BST)

Welcome back

15:40 (BST)

Predict and optimise your business outcomes

Moiz Husain
Head of Enterprise Data, BT
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Moiz Husain, Head of Enterprise Data, BT

Data is essential, but true optimisation comes from improving performance and competitive advantage from the production of analytics models which allow for democratic access to the insights gained.

Many believe that the best approach to producing an effective model starts with the identification of a business opportunity that allows you to determine how the model can improve performance, rather than starting with the data.

We address how such an approach to modelling can generate faster outcomes and embed models in practical data relationships that are more accessible and how this approach avoids the potential of designing overly complex models which exhaust organisational capabilities.

15:55 (BST)

Data governance as a business enabler

Amadeus Stevenson
Data Lead, NHS AI Lab
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Amadeus Stevenson, Data Lead, NHS AI Lab

Data governance is not some monolith to circumvent for fear of it acting as an innovation disabler but should instead be seen as the engine to scale the use and distribution of trusted data pipelines throughout your company.

Just as in society, so too in business, good governance leads to the proper functioning of the people, processes and technologies designed to ensure the delivery of desired outcomes.

We address, how to develop and deliver trusted data, direct it to the right users in the right format and right time to harness business value, and confidently ensure data privacy, proactive compliance with regulations, and easy collaboration with data professionals in every function.

16:10 (BST)

Fighting misinformation with big data and AI

Stefan Marwah
Principal Data Engineer, Schroders
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Stefan Marwah, Principal Data Engineer, Schroders

In the age of information and data overload, misinformation has become an increasing tendency, almost the “new normal”, with far-reaching consequences including impacting the results of the 2016 United States election and, more recently, the spreading of false information around the pandemic.

The consequences of inaction are serious. From the fragmentation of society into competing, and often violent factions, to the irretrievable decline in societal trust in democratic systems of governance.

Join us as we discuss the role that big data analytics and AI can play in the fight back against this assault on truth and trust.

16:25 (BST)

Questions to the Panel of Speakers

16:40 (BST)

Conference Chair Closing Remarks

16:45 (BST)

Conference Closes

Please note:
Whitehall Media reserve the right to change the programme without prior notice.

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