Programme @ BDA europe

Dr Gilad Rosner, Founder, IoT Privacy Forum; Expert in Public Policy of IoT & Identity Management; Privacy and Technology Policy Researcher

Rahma Javed, Director of Engineering, Deliveroo

A wave of disruption is currently sweeping the globe in the wake of the Covid-19 pandemic. First came the health crisis, now comes the financial and economic.

One of the ways in which business leaders are looking to survive the storm is to expand data-led initiatives which can discover new opportunities, support continuity, improve productivity, and seek out new customers.

We discuss:

• Competing with new entrants
• Realising structural cost advantages
• Adopting a digital-first culture
• Amending your product offerings
• Creating convenience for customers
• Leveraging digital technologies
• Sensing market shifts
• Innovating faster

As efforts to expand cross-departmental access to data continue to gain traction, businesses are finding the transition from acquisition to revelation difficult to master as the gaps between data experts and the wider business remain.

We address:

• Telling the right story to the right people
• Turning data into information
• Ease of access
• Visualise at speed
• Deploy data from multiple sources
• Update in real-time

Rui 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?

Effective data lake management allows you the opportunity to govern, access and explore big data in an accessible and tangible way which gives real benefit to the business.

A failure to properly format your data lake architecture will result in an inability to reduce costs, productively analyse data sets, integrate data sets into existing infrastructure and handle governance and security in a way which supports business growth.

We address how to:

• Secure a competitive advantage
• Prioritise data variety
• Adopt an agile approach to data analysis
• Foster business and IT collaboration
• Better understand customer behaviour
• Identify consumer trends

Mario Meir-Huber, Head of Big Data, Analytics & AI, A1 Telekom Austria Group

Big data now dominates the enterprise technology landscape. This domination is reflected in IT spend. Despite such investment, many big data-led projects fail to move from pilot to production.

We address, the common pain points experienced when delivering big data projects, why many fail to move on from phase one and what those who are starting out in their big data projects can do when problems occur.

Organisations need to respond to the evolving ways in which Hadoop is being deployed within businesses. From its origins as a batch processing platform for use cases to reporting on operational workloads, its duties and capabilities have expanded significantly.

We address the role of needs-driven modern-reference-architecture in identifying context-rich data, supporting decision making, increasing business opportunities, and establishing multiple use cases with their own design and unique purpose.

Ismail Yildiz, Expert Data Scientist – AI & ML Engineer, Turkcell

Image/video processing is the most popular area in the world of AI. As AI capabilities develop at pace so too do previously impossible ideas become feasible to implement.

In the last 2 years, we have implemented many image/video processing projects in order to increase revenue or cost-saving. Examples include but are not limited to Passport Fraud Detection, Video Voting, Face Authentication, Car Park Slot Suggestion, Photo Social Media Tag Recommendation, Emotion Analysis, Recruitment Scoring on Video Resumes, etc. The most important thing regarding these projects is that they are already in production and generating revenue. How synthetic data compares to masking and anonymization

Click here to view the seminar sessions

Sanne Menning, Lead Data Scientist, Albert Heijn

Identifying new areas of growth through data-led initiatives, as well as amending an existing portfolio, opens up many opportunities for increased revenue generation.

From improving existing products to establishing wholly new offerings, it is the enterprise which is willing and able to be innovative and creative that will continue to enjoy a healthy balance sheet.

However, for many, getting from proof of concept to production is a task which is fraught with risk and failure.

We address:

• Opportunity identification
• Proof of concept
• Minimum viable product
• Sustainable software solution

Machine learning is a vital business technology and its evolution from automated tasks to more complex endeavours such as autonomous vehicles has produced a lack of accessibility.

We address:

• Automate traditional ML processes
• Enable multiple users
• Multi-discipline accessibility
• Develop accurate models
• Interpretation and explanation infused models
• AI production at speed

Bauke Brenninkmeijer, Data Scientist, ABN AMRO Bank

With tabular data being over 90% of data that businesses encounter, high grade synthetic tabular datasets are extremely valuable.

Generative Adversarial Networks have been very impressive in the visual domain but are also slowly gaining traction for the tabular domain.

In this talk, I’ll discuss how GANs work for tabular data and what the specific challenges in this domain are.

Additionally, I’ll discuss the role of synthetic data in large companies and corporates, where the GDPR and information security plays a big role.

• GANs can create high quality synthetic tabular data
• Synthetic data can alleviate GDPR restrictions
• Synthetic data can alleviate data access problems
• Synthetic data can improve data science projects by generating additional data
• How synthetic data compares to masking and anonymization

Muhammad Usman Haider, Solution Architect Big data & Analytics, Telenor

Big data solutions structured in a modular fashion allow you the opportunity to better design and drive business through the democratisation of accessibility in a scalable, secure, and cost-effective way.

Such solution designs help you to deliver value by securing extra Capex and Opex which are sourced from descriptive, diagnostic, predictive, and finally perspective analytics.

• Costly legacy solutions with limited value and canned use cases
• New Era big data Solutions
• Driving Value from big data & analytics stack
• Reduction in overall TCO
• Telecom / Financial/Insurance use cases

Analytics in the form of BI have taken on an added importance as the Covid-19 crisis has led to an environment in which businesses need to explore new opportunities for growth in the face of dwindling revenue in existing product offerings.

The right BI platform has the ability to integrate every key aspect of operations from advertising, supply chains, support, and social media management, which supports cost efficiency, productivity, and revenue generation.

We address:

• Diversity in data sources
• Plow through data in real time
• Produce insights previously unimagined
• Increase profit margins
• Location-aware & location-based services

Jimmy Hong, Director, Software Engineering (Data), LinkedIn

Madhumita Mantri, Staff Technical PM, Data Platform & Products, Artificial Intelligence, LinkedIn

As a technology and engineering company, LinkedIn has excelled at using data to build solutions that better the lives of our members.  Over the years, we have built a broad and diverse data environment to support both dozens of customer/members facing products as well as business functions across our entire value chain (sales, marketing, customer operations, etc.).  Supporting all of this is our big data ecosystem, which together processes over 7 trillion messages and 2+ Petabytes of data per day.

In this presentation, we will describe a governance framework around key pillars of availability, security, user experience (UX), and engagement paths.  To put this framework into practice, we formed a Data User Council in 2020, partnering with our customers and stakeholders to structure and align activities that drive value through these channels.  We will discuss lessons and share the best practices we have learned as part of this journey.

• Problem context and key gaps
• Approach to establish a disciplined workflow for data governance
• Frameworks and approaches to drive a data-centric culture
• Results, outcomes, and looking towards the future