BDA EUROPE

Big Data Analytics

10 March 2020

Van Der Valk Hotel, Utrecht

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Programme

Session one – developing your big data strategy, building your capabilities and realising your goals

  • Data democratisation
  • From project to production
  • how to become data driven
  • Getting beyond the piloting stage
  • A solid data foundation for big data reporting & analytics
  • How to make value out of IoT data
  • Achieving accuracy in data visualisation
  • Data visualisation story telling
09:00
Conference Chair’s Opening Address

Nigel Cameron, President Emeritus, Centre for Policy on Emerging Technologies

09:15
Data to the people – data democratisation enables data driven organisations

Bertram Pamminger, Head of Engineering Data Products, Daimler AG

In this decade, “big data” and “data driven” were some of the biggest business buzzwords. We all want to turn our company into a data driven company; we all want to leverage the value of the data we possess.

The keys to a data driven organisation are the balance of data governance and data democratisation, i.e. enabling the broad majority of your organisation to access and generate value out of your organisation’s data.

In this talk, Bertram Pamminger will share Daimler Truck’s approach of Process Embedded Analytics – bringing analytics functionality into the operational processes and enabling better decisions and more targeted actions.

• To enable a data driven organisation, the balance between data governance and data democratisation plays a crucial role
• With Process Embedded Analytics, Daimler Trucks is bringing analytics functionality into the operational processes
• Thereby enabling better decisions and more targeted actions

09:35
Achieving C suite level buy in for big data investment: the ROI challenges

Determining upfront a tangible ROI on big data is one of the greatest barriers to securing C suite level buy-in. Determining what to build, purchase, borrow or rent is a major task which can create barriers to progress.

We detail how best to address leadership capacity constraints which undermine many analytics leaders’ efforts when seeking to mobilise your C suite for big data investment.

09:50
Becoming data driven and data valuation: from the new gold to reality

Sébastien Oldenbroek, Data Product Owner, Nationale-Nederlanden

Every company is focused on becoming data driven in order to extract value from the “the new gold”. Does everyone in the company know what data driven actually is, besides a buzz word? Is it enough to have data engineers and scientists to make data the new gold or is it a long chain which needs to be managed? What does that require from your organisation?

If data is valuable, can we quantify it in an economic sense? Can we measure the Return on Information?

I will address these questions based on my experience and perspective.

  • What does data-driven mean?
  • Chain thinking and cultural change
  • Data valuation methods, how to quantify data into money with theoretical and practical examples
10:05
Getting beyond the piloting stage: from project to production

Related to the issue of securing the necessary investment to support big data initiatives is the importance of ensuring you move from the piloting stage to production in an ordered and timely fashion.

We address:

  • Opportunity identification
  • Proof of concept
  • Minimum viable product
  • Sustainable software solution
10:20
A solid data foundation for big data and reporting & analytics within the modern enterprise

Sander Hermsen, Senior Enterprise Architect-Data & Reporting, Rabobank

Almost all enterprises have invested significantly in their IT for reporting and analytics. This investment is characterised by a large and diverse set of solutions from different vendors which are insufficiently aligned.

As a result, Data logistics – the flow of data within the organisation – is not properly organised and parts of it are unknown. This current state is blocking the move towards a more modern digital organisation. Fortunately, the move to the cloud can enable these organisations to tackle this current state.

In my talk I will focus on how to simplify data logistics and the architecture, how to implement governance on data (usage) and the required organisational capability.

  • Simplification of data logistics and technical architecture
  • Being in control of data and the usage of it
  • Management of interfaces and API management
  • Develop your own capability for agile delivery and operationalization of results

What internal culture and skills do you need to have within your organisation to realise a solid data foundation

10:35
How to make value out of IoT data

Dani Marouane, Enterprise Architect, IoT & Big Data Domain architect, Damen Group

We live in the connected Era! The digital world has never been closer to the physical world due to the vulgarisation of IoT technology. Wearables, sensors and phones generated more data than all other systems combined in the last 5 years, which means that it comes with its own challenges.

  • How to make value out of this ocean of connected data?
  • Is data the real new oil?
  • What are the best practices? the do’s and don’ts
  • Let’s discuss the architecture!
  • How to consume this value within and outside of an organisation?
10:50
Questions to The Panel of Speakers
11:00
Refreshment Break Served in the Exhibition Area
11:30
Can you trust what you see in data visualizations? Because they can lie!

Ben de Jong, Enterprise Adviser-Data Visualisation & Business Intelligence, ABN AMRO Bank

A lot of investment is directed towards data and analytics, with visualisation forming an important part of the whole data process.

Making good visualisations requires attention, knowledge and expertise. Without this, our message will not be conveyed accurately.

It all starts with the choices which are made when one is designing a visualisation. Very often the wrong choices are made. It could be that data visualisers follow the standard options of the software they are using, or that they have taken influence from some interesting ideas somewhere else and re-used them. But are you aware that the intended message is sometimes lost? By making yourself aware of the common mistakes made you may be able to identify them and advise accordingly.

• Why is data visualisation important?
• What are the common pitfalls for ‘bad’ visualisations?
• How to avoid lying charts

11:45
What makes a good data visualisation? An example

Isabel Bevort, Data Scientist, Dutch Railways 

Do you struggle with understanding certain graphs or dashboards? Do your own graphs get repeatedly misunderstood?

As data professionals we are continually telling stories of the information that is contained in the data. How do we make sure that we tell the right stories to the right people, and that our stories are understood? In order for our information to be useful, we need to know how to tell the stories right.

Through an example, I will show you the basic principles of good data visualisation and my own considerations in deciding how to tell a story.

  • Basic principles of good data visualisation based on Stephen Few
  • Data visualisation is the “how” of telling the story of the information from data
  • Example – simple visualisation but high amount of information
12:00
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
12:15
Seminar Sessions
13:00
Networking Lunch Served in the Exhibition Area

Session two – Putting your data to work with the right technology

  • The added value of a measurable key result
  • Building and retaining your customer base with AI
  • Adopting an agile data science approach
  • Algorithmic content programming
  • Eliminating the guess work in labour-intensive exercises
  • Embracing analytics-a practical use case
14:00
Conference Chair’s Afternoon Address
14:05
Objective impact – the added value of a measurable key result

Polina Abdoulina, Senior Data Scientist | OKR Program Lead, TNT Digital / FedEx

With the increasing gathering of data, new opportunities arise to better steer decision-making based on data and facts. Adopting an agile mindset by setting quarterly goals and creating a highly aligned focus among departments becomes common in many companies.

To stay ahead within extremely fast-changing markets, data is an essential element in creating this focus and maximising results.

This talk is about:

  • How to set value adding OKRs;
  • How data and metrics should support your OKRs;
  • Steps you can take to create a data-driven company
14:20
Building and retaining your customer base with AI

This presentation explores practical applications of Marketing Intelligence Modelling and applying them in the cloud to better retain and understand your customer base, as well as your growth potential.

  • Using predictive modelling for churn events
  • Classification vs Survival Analysis
  • Setting up machine learning process in the cloud
  • Do’s and don’ts for optimal ROI
14:35
Which keys strike a winning melody? – Adapting the agile way of working towards data science and analytics

Sarah Westphal, Senior Product Owner, a.s.r. analytics lab

While Agile originally found its way into the business through software development, it is currently establishing itself in other areas of the value chain as well. In its core, agile is a collaborative approach between cross-functional teams. These teams design and build minimal viable products (MVPs) and features, test them with customers, and refine and enhance them in rapid iterations.

Does this approach also suit the more R&D focused approach of the analytics and data science teams? And can agile data science live up to its promise and deliver an effective process flow where product improvements are realized quickly?

Based on her experience of introducing agile to data and analytics teams within a.s.r. (a large scale Dutch insurance company), Sarah explores:

  • What are the key features for a successful agile data science approach?
  • How can these principles be applied into the data science development process?
  • What mistakes should be avoided?
14:50
Questions to the Panel of Speakers
15:00
Afternoon Networking and Refreshments served in the Exhibition Area
15:30
Algorithmic content-personalising messaging in a smart and programmatic way

Pieter van Geel, Director of Data, Greenhouse (part of WPP}

Nearly all online marketing KPI’s are determined by price of the impression and effectiveness of its message. Determining what message is the most effective for different audiences or context and then exploiting this learning as early in this process as possible is key in maximising the effect of online marketing campaigns.

Multiple creatives containing different messaging can be turned into one ‘smart’ Algorithmic Content creative. In this creative we provide the logic to optimise it towards a given goal over a given dimension.

Algorithmic Content is platform agnostic and can include an onsite component as well. The process and impact of Algorithmic Content in media execution will be explained in the presentation.

  • Multi-variant (bandit) testing
  • Segmentation at scale
  • Media optimisation (media efficiency)
15:45
Eliminating the guess work in labour-intensive exercises

By successfully deploying disruptive technologies such as AI into historically human-led, labour-intensive activities, businesses are able to save money, increase efficiencies, free up its human capital to perform other important duties and create the space for innovation and collaboration; through which the establishment of new product offerings can typically be realised.

We explore:

  • Time and reduction costs
  • Identifying the best course of action
  • Eliminating guesswork
  • Making processes more accurate and timelier
  • Using historical data to understand patterns
16:00
Embracing analytics – a practical case

Arno Van Sloun, Lead Data Scientist, ARAG

Organisations often find it difficult to implement analytics within their processes. In order to overcome this fear-of-failure and to catalyse the change needed, organisations need to embrace the use of analytics. By doing so, analytics can contribute to cost reduction, overall improvement of informed decision making and lower implementation risks for new products.

In this presentation, Arno van Sloun will share his thoughts on the maturity levels of analytics and the life cycle of data science projects. In addition, a practical case on prescriptive analytics will be demonstrated.

  • Clustering of (historical) data to make various processes more accurate
  • Using machine learning techniques to retain and build a healthy client base / portfolio
  • Prescriptive analytics combined with a manual verification step for raising acceptance of analytics
16:15
Questions to the Panel of Speakers
16:25
Closing Remarks from the Conference Chair
16:30
Conference Closes

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