Programme @

BDA uk


16 June 2021

Brought to you by Whitehall Media

Programme @ BDA uk

Session one

realising your ambitions with the right strategy and governance

  • Digital transformation in practice: from moment to action
  • Health population management: AI-led intervention
  • Self-service analytics: achieving freedom with order
  • AutoML for all: a holistic approach to ML production
  • Data-driven good governance: deriving maximum value
  • AutoML for all: a holistic approach to ML production
  • Protecting digital assets: insider versus outsider threats
  • NLP as a part of your AI arsenal: adding visibility and reducing complexity


Conference Chair's Opening Address

David Terrar, Director and Chair, Cloud Industry Forum


Measuring the impact of technologies like IA, AI, Big Data on the future of our society

Rui Pedro Silva
Director of Technology Strategy, A.P. Moller – Maersk
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Pedro Silva, Director of Technology Strategy, A.P. Moller – Maersk

Over the past 10 years, the world has changed drastically, and the behaviours of people have changed with it.

The biggest question is not to what extent corporations are ready to embrace the era of AI automation, but whether society is ready to acknowledge it and prepared to handle it in an efficient way.

Aspects such as education, political environment and primary forms of socialization will become even more relevant for the generations to come.

Are we ready for that?


Understanding the importance of data in a creative field

Christine Osazuwa
Global Marketing Director of Data & Insights, Warner Music Group
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Christine Osazuwa, Global Marketing Director of Data & Insights, Warner Music Group

Discussion of working in data in a creative industry (music) & the successes and pitfalls that come with creating/forging your own path within an industry that is used to using its “gut” to make decisions.

  • Opening yourself to the possibility of tech roles outside of tech companies
  • Understanding the unique challenges of working in a creative industry
  • Skills you need in these roles (tech & non-tech)



Striking the right balance: self-service analytics

Deepak Jose
Head of Business Strategy and Analytics, Mars
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Deepak Jose, Head of Business Strategy and Analytics, Mars

Your IT team is forever engaged in a struggle to balance freedom with order. The benefit being that the business is then able to use its time more efficiently, which in turn creates the space for greater revenue generation and personalised customer care.

Self-service analytics with data governance baked in empowers your workforce to query and resolve their own questions with data, enables them to share insights with ease across the business, and ensures that the decisions made are reliable, trusted, and secure.

We address:

  • Balancing the needs of business and IT
  • Ensuring good governance and business agility
  • Redistribute content workload to where it is most needed
  • Giving decision making power back to where it is needed


Big Tech and the Law

Frank Walter
Business Director, Zenith Media
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Frank Walter, Business Director, Zenith

Big tech is now deeply intertwined with our economy, our lives, and our democracy.

As the tech industry has grown in importance, the problems associated with it have grown. All around the world, academics, politicians, and regulators are dreaming up ways to reduce the power of big tech, protect citizens and boost competition.

Today’s presentation is a look at what those rules and regulations might look like in the UK.

In this session, we will cover:

• The challenges posed by big tech to privacy, content moderation and competition
• Who is going to implement the regulation?
• A deep dive on expected new UK antitrust (pro-competition) reforms for social, search and display


Auto ML and augmented analytics: an integral part of ML

A holistic approach to ML production consists of analysis, engineering, training, and fine tuning.

An important function that can be added to this process, which many have not yet utilised, is to have these tasks performed by an ML platform that is able to produce reliable model candidates for review and deployment.

We address, AutoML and the role it plays in allowing you to automate your learning models without hindering visibility, access, and control.


Questions to the Panel of Speakers


Networking Break


NHS Digital Case Study

Titilayo Are, BI & Analytics Project Manager, NHS Digital


Gousto Case Study: combining business scalability with customer personalisation

Irene Iriarte Carretero, Senior Data Scientist, Gousto


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


Seminar Sessions


Networking Lunch Served in the Exhibition Area

Session two

deploying the right technology to where it is needed most

  • IoT analytics: making your production work
  • Data-led personalisation: algorithmic marketing
  • Mapping productivity: using data to optimise processes
  • Transforming your data and reporting: making your data flow
  • Responsible AI: protecting your reputation
  • Integrating your data sources: pooling your resources


Conference Chair’s Afternoon Address


Marketing and Advertising Case Study:

Frank Walter
Business Director, Zenith Media
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Frank Walter, Performance Business Director, Zenith

How digital will become a regulated industry and what this means for us in media?

• The challenges posed by big tech to privacy, content moderation and competition
• A deep dive on expected new UK antitrust reforms for social, search and display


Data-led personalisation: algorithmic marketing

The economics of marketing can be summarised as counting the cost of the initial impression and the effectiveness of the message, which is measured in the response you receive. knowing what message is most effective requires knowing your audience on a granular level and accounting for any contextual concerns.

With algorithmic marketing you can take multiple creatives with different messaging and turn them into a comprehensive all-in-one algorithmic content creative. In this creative, you provide the logic to optimise it towards a given goal over a given dimension.

We address:

  • Platform agnostic algorithmic content
  • Segmentation at scale
  • Media optimisation


What are the right tools and systems to support rapid innovation in a Data Science team?

Dr David Hoyle
Research Data Scientist Specialist, Dunnhumby
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Dr David Hoyle, Research Data Scientist Specialist, dunnhumby

A healthy pipeline of new products is vital to many companies and so the product innovation and research process need to be efficient as possible.

Contributing to the innovation pipeline is a key task for many Data Science teams, but often they are held back by the constraints of having to build upon legacy products and use inappropriate systems.

In this talk, I’ll highlight the technical barriers to innovation faced by Data Scientists, and also some of the tools, such as probabilistic programming languages, that we have used at dunnhumby to support rapid experimentation with Data Science models.

• Effective innovation requires the right systems and tools to support rapid research and experimentation
• The right tools for rapid experimentation directly reflect how a Data Scientist thinks about a problem
• Probabilistic Programming Languages (PPLs) are one such tool.


Questions to the Panel of Speakers


Afternoon Networking and Refreshments served in the Exhibition Area


Transforming your data and reporting: making your data flow

So much investment in data reporting and analytics capabilities gets lost in the enterprise ether. Much of the blame lies with the fact that investment is directed towards a wide range of business functions with a host of vendors making up a crowded and poorly aligned platform.

The result of this inefficiency is a lack of data flow, which in turn impacts your ability to collect, analyse, evaluate, and make use of vital information.

We address:

  • Simplifying your data logistics and architecture
  • Controlling data usage
  • Managing interfaces and API management
  • Agile delivery and results operationalisation
  • Establishing a solid data foundation


Responsible AI: protecting your reputation

As significant a role as AI now plays in enterprise activity, it is decisions made by humans which ultimately lead to biased inputs and false positives.

Responsible AI provides model transparency, which in turn acts as a layer of protection against poor decision making. This helps to strengthen the employee-technology dynamic and creates the possibility for expanding adoption and business alignment throughout the organisation.

We address, how an ethical approach to AI operationalisation can protect your brand, improve transparency and accountability.


Integrating your data sources: pooling your resources

The complexity of today’s data landscape means that organisations are having to account for a variety of data sources across multiple enterprise platforms, from data centres, cloud storage, and data stores.

To prevent data remaining in the dark, it is important to address unsustainable data spread by deploying a data lake platform which can connect to and discover data and enable your data ops to deliver continuous, component based development of data analytics.

We address:

  • Produce trusted re-usable data assets
  • Govern and integrate data distribution in multi cloud
  • Defining your data
  • DataOps approach to data assets reliability
  • Securing end-to-end DataOps
  • Operationalisation from data lake to marketplace


Questions to the Panel of Speakers


Closing Remarks from the Conference Chair


Conference Closes

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

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