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 governance deficits
  • The ROI challenges of big data investment
  • From project to production
  • Self-service analytics
  • Employing an associative model
  • Natural language searching
  • Data visualisation storytelling
09:00
Conference Chair’s Opening Address
09:15
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
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
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
10:05
Expanding access to data: self-service analytics

Expanding the use of data analytics to all business users rather than just those who have possess expert knowledge and understanding of it is key if enterprises are to secure more open access to data, more timely access to data and better-quality data and analysis.

We address the importance of moving towards a self-service analytics model.

10:20
Employing an associative model: supporting an agile analytics environment

The primary concern for enterprise leaders today is not identifying and collecting data but gathering and integrating no matter its origin or format.

Deploying a solution which supports an agile analytics environment provides for a more holistic and context-based platform upon which to drive forward business insights through the application of cross-business decision making.

  • Exploring all the connections in your data
  • Providing users with a complete view of your business
  • Making better informed decisions
10:35
Questions to The Panel of Speakers
10:50
Refreshment Break Served in the Exhibition Area
11:30
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.

11:45
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, it is 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 space for dialogue with other business units
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

  • 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
14:00
Conference Chair’s Afternoon Address
14:05
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.

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
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
14:50
Questions to the Panel of Speakers
15:00
Afternoon Networking and Refreshments served in the Exhibition Area
15:30
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.

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
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.

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.