Big Data Analytics

10 March 2020

Van Der Valk Hotel, Utrecht




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

  • Data governance deficits
  • From project to production
  • Algorithmic content platforms
  • Self-service analytics
  • Data logistics
  • Natural language searching
  • Data visualisation storytelling
Conference Chair’s Opening Address
Data governance: addressing the democratic deficit

Poor data governance leads to a lack of collaboration across the enterprise environment, which in turn leads to an increased risk of duplicated activity as departmental silos become even more entrenched.

Exhaustive productivity also leads to variations in data reliability, redundancies in data not being identified and delays in BI processes.

We address:

  • 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 govern innovation, disruption and changing business strategies
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.

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)
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
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
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

Questions to The Panel of Speakers
Refreshment Break Served in the Exhibition Area
Support decision making processes with evidence: natural language searching

As enterprises seek to democratise business user access to data in order to drive evidence-based decision making, many are employing natural language search.

We address the issue of ever-growing data repositories, the need of business leaders to be able to circumvent the naturally technical language employed by data analytics experts, how NLS speeds up decision making processes and provides the evidence necessary to support data-driven decisions.

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

Data visualisation: driving insights and connecting data

One of the key concerns for data scientists is translating data into stories which business leaders across the enterprise can mind map. Successful data visualising leads to a shared understanding of the purpose, role and potential of data visualisation.

As data scientists tackle the vexed issue of transforming data into digestible visualisations and manage the forces of complexity and simplicity, its important to reflect upon how best to produce your findings.

We address:

  • What is the story you wish to tell?
  • Ordering your building blocks
  • Accommodating the simple and the complex
  • Allowing a space for dialogue with other business units
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
Seminar Sessions
Networking Lunch Served in the Exhibition Area

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

  • AI in major industries: real world augmentation
  • Building and retaining your customer base
  • Predictive modelling in retail management
  • Turning algorithms into business advantage
  • Benefits of machine-based learning
  • Big data analytics market: 2020 and beyond
Conference Chair’s Afternoon Address
AI in action: real world augmentation

The banking and finance sectors are one of the most important business environments in which to apply and rely upon AI and machine learning to help mine data, predict behaviour and produce tangible information which can be acted on.

Using the example of ING’s Katana initiative, we explore the use of AI in trading and investing and its evidenced capability to power decision making when buying and trading bonds.

We will explore its key accomplishments, ranging from aggregating vast amounts of data from multiple historic and real-time sources to predict the winning price and identify the most promising trades, to improving trade market capitalisation by as much as 20% and ensuring prices are 20% more accommodating.

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
Deploying a predictive model in retail demand management

Anticipatory shipping may well be the closest that the retail sector has come to predicting the future purchasing preferences of consumers.

Paired with predictive analytics tools and a massive trove of customer data, an increasing number of retailers are adopting an anticipatory shipping process to ensure popular items remain in an effective limbo to cut down on shipping to delivery times.

Using the example of a prominent time to consumer model, we address:

  • Online upselling and cross-selling
  • Area-specific relevancy and suitability
  • Regional shipping hubs
  • Strategically placed warehouses
  • Cost-benefit to the business
Questions to the Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
Machine learning meets big Pharma

Developments in machine learning are having a revolutionary impact on pharmaceutical R&D.

By blending improvements in ML predictive behaviour modelling, greater integration of diverse sources of information which complements such modelling, and effectively anchoring the human between the two in order to create a dynamic three way relationship, machine learning is helping to deliver data-driven decisionmaking on which experiments to perform, identify cost reduction due to historical data relevancy and discover new biology through analytical analysis.

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
Big data analytics market: 2020 and beyond

Through 2020 and beyond, the big data analytics market will continue to grow at a compound growth rate of 11.7% as ever more organisations seek to monetise data.

From improving upon existing internal processes to capturing more of the external forces which drive consumers, businesses and other third-party actors towards your organisation will increasingly be driven by the insights secured from the analysis of big data.
In our closing address, we will explore the future projections for the market, the leading enterprise forces utilising it within their organisations and how this is set to impact the company-consumer dynamic.

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.