Session ONE – ANALYTICS, MACHINE LEARNING AND AI
- Identifying trends and patterns of activity in data for improved outcomes
- Implementing Big Data architectures – strategy, opportunities, challenges
- Taking advantage of disruptive technologies like Artificial Intelligence, Machine Learning, Predictive Analytics, and the Internet of Things
- Behavioural Insights, customer analytics and CX
- Improving business forecasting and decision-making
- Turning data into new visibility and business intelligence
- Gaining real-time actionable insights from your data
- Building an agile, responsive and powerful Big Data architecture
Conference Chair’s Opening Address
Professor Marc Salomon, Dean of the Amsterdam Business School (ABS), Director of the MBA in Big Data & Business Analytics and Member of the Big Data Alliance Board
Data governance strategies in the age of disruption
Anwar Mirza, Global Head of Data Governance, TNT
The outline of a Data Governance programme, framework and methodology that is embedded into the core of a company’s foundation.
- A formal approach to tangibly valuing the cost of poor quality data
- Exposure of obvious business white spots through the identification of ‘must do’ data governance controls
- How to prepare yourself for governing innovation, disruption and changing business strategies.
Delivering maximum value from data science projects through productionalizing machine-learning models
Teren Teh, Vice-President, Customer Analytics, Barclays
Deploying your machine-learning models to production is a crucial step in transforming data science research into real value for your company.
This presentation will guide you on how to make the most of your data science projects and successfully deploy your models.
Finding the Right Use Case for Your First Machine Learning Deployment
Mahmoud Hassan, Machine Learning Engineer, Facebook
How can your organisation operationalise a model driven by data science, deep learning, and machine learning? For all the hype around machine learning, it can often be difficult to find the right area to deploy it in your own organisation. Using case studies of enterprises that have demonstrated the value of ML programmes, we explore key areas you should consider to unlock business value.
Improving Your Training Data for ML in Production
Kasper Knol, Data Scientist, Ford Motors
Having accurate results is of tantamount importance when you are looking to deploy ML models in production. And the easiest way to improve accuracy is to improve your training data. This presentation examines the practical steps you can take to create better ML results, looking at:
- Simple steps you can take to vet input data
- Finding the right kind of datasets for your use cases
- Using clusters and other visual tools to spot discrepancies
- Learning from feedback and testing to achieve high accuracy in production
Finding the Right Cloud Provider for Your Big Data Architecture
This presentation examines the key questions you need to ask any cloud provider before embarking on a cloud based big data architecture. We cover how you can:
- Prepare for scalability and changing needs in a fast moving environment
- Achieve high performance for varying workload needs
- Ensure high security and compliance with strict privacy requirements
Questions to The Panel of Speakers
Morning Networking and Refreshments Served in the Exhibition Area
What makes a good data visualisation?
Martijn Scheele, Head of Data & Analytics, Dutch Railways
Isabel Bevort, Data Scientist-Researcher, Dutch Railways
Data visualisations are still the best way of transforming the insights gleaned from tangled masses of data into stories that the rest of your organisation can understand and act on.
Data visualisers are often caught between two competing forces: creating graphs and visuals that are simple to read on the one hand, and making sure they are complex enough to reflect the realities of the data available on the other. This presentation shows how you can overcome this by:
- Starting with the story you want to tell from the data, and building from there
- Balancing simplicity and complexity in data visualisation
- Incorporating feedback from other business areas into your designs
Using Predictive Analytics to Better Forecast Financial Changes
This presentation explores the successful deployment of a predictive analytic model in a large enterprise to better forecast internal financial movements. We look at:
- Implementation: identifying what you need to know from previous datasets and evaluating if your model will be able to answer these questions
- Fine tuning and testing: improving the accuracy of your model
- Deployment: acting on predictions and overcoming challenges
Questions to the Panel of Speakers Delegate movement to the Seminar Rooms
Networking Lunch Served in the Exhibition Area
Session TWO – ENTERPRISE DATA MANAGEMENT IN ACTION
- Creating a successful data programme in complex organisations
- Data science team management
- Master data management, governance and privacy
- The role of the Chief Data Officer
- Data ethics, GDPR, security and privacy
Conference Chair’s Afternoon Address
Becoming a Data Driven Organisation
Manuel de Francisco Vera, Head of Big Data & Analytics, Bookerzzz
Creating a data driven organisation requires more than just good data governance and analytics, it demands a fundamental change to the way your organisation acts and makes decisions. This presentation looks at the steps you can take to make this change by:
- Expanding operations and ensuring executive buy-in by demonstrating value
- Breaking through traditional processes to create a culture of evidence based decision making
- Overcoming data silos to ensure analytic insights are shared and acted on at every level
Deploying Data Science Teams in Complex Business Environments
This talk explores the way data science teams can be deployed effectively in complex, geographically dispersed, and technical business environments. Using our own past successes and failures as a roadmap, we discuss:
- How centralised, embedded and ‘hub and spoke’ models cope with business complexity
- Ensuring skills overlap in teams as well as depth of knowledge
- Improving communication between data and operations teams
- Challenges of geography and how to overcome them
Better Data Governance, Better Data Results
Not much has changed since Charles Babbage was asked if putting the wrong figures into his computer would yield correct answers – yet many are still guilty of expecting optimum insights from poor quality data.
Good data governance is at the heart of making sure you avoid this trap and that your data quality remains high, your processes are robust, and the results you produce are reliable. Join this session for an in-depth look at how enterprises can improve their data governance essentials.
Questions to the Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
How can the Role of Chief Data Officer Drive Innovation and Change?
The Chief Data Officer is critical to ensuring that value is extracted from enterprise data, data quality processes are robust and that data and insights are accessible to the entire organisation.
But as the CDO evolves from a data steward to the key figure responsible for digital transformation, business culture and monetisation, we explore the steps all CDO’s needs take in order to be a true force for innovation and change in the enterprise.
Data Collection and Privacy Legislation: Are They Opposing Forces?
Privacy offers an exciting opportunity for data scientists and practitioners to improve their data collection practices and relationships to benefit the industry overall. We explore:
- Improving relations between data subjects and processors and how this will lead to better quality data
- Where will the “data privacy battles” take place?
- How can data collectors use privacy legislation to improve their processes for long term advantage?
Implementing Self Service Analytics for Large Enterprises
Dr Mils Hills, Associate Professor in Risk, Resilience and Corporate Security, University of Northampton Business School; Senior Subject Matter Expert, NATO
The advantages of widening the pool of people who can benefit from your data is self-evident. But the challenges of implementing self-service analytics in a large organisation can be difficult, daunting and if done incorrectly – damaging when untrained users draw the wrong conclusions from data.
In this presentation we consider:
- The best environment to support a self-service approach
- Best practice on how to safely democratise your company data
- The required technology, people and processes to guide self-service
- How to avoid the pitfalls
Closing Keynote: Is it Time for a Data Code of Ethics?
Marc Steen, Senior Research Scientist: Human-Centred Design; Responsible Innovation; Ethics in Big Data, Algorithms and Artificial Intelligence, TNO
As the world is rocked by the revelations of bulk data collection, behavioural tracking, and alleged manipulation of elections, this closing keynote asks what this means for the data industry.
How should data scientists and individuals who create data analytics tools respond?
We consider if a code of ethics is the best way to ensure the data industry continues to innovate in a morally responsible manner, and what this could look like.
Questions to the Panel of Speakers
Closing Remarks from the Conference Chair
Conference Closes, Delegates Depart
Whitehall Media reserve the right to change the programme without prior notice.