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Programme

Session One: Big Data’s potential for disruptive innovation

  • Exploring the latest trends in the big data space
  • Impact of data on business models and profitability
  • Disruptive technologies like AI, Machine Learning, IoT and their application to solving business problems
  • Leading large scale data-driven transformation across your enterprise
  • Achieving ROI from your data projects
  • Moving to real-time analysis for better responsiveness and forecasting
  • Security, compliance, privacy and transparent use of data
  • Turning data into new visibility and business intelligence
  • Utilising predictive analytics for impactful action
  • Widening data applications to achieve specific business outcomes – from personalised customer journeys to marketing and more
  • Elevating your data
09.10
Conference Chair’s Opening Address
09.20
Why Data Matters for your Digital Transformation

Somita Yogi, Director of MI, Data Strategy and Data Science/Analytics, Post Office

Data and Digital Transformation enable one another. According to Forbes, “by 2018, 35% of IT resources will be spent to support the creation of new digital revenue streams and by 2020 almost 50% of IT budgets will be tied to digital transformation initiatives.”

We look at how your organisation can:

  • Use data to solve strategic business problems
  • Use advanced analytics, ML and AI to build intelligent business processes
  • Drive digital transformation and monetise data assets while being mindful of information governance post-GDPR
09.40
Interactive Visualisation Tools: Making Sense of Big Data

Visualisation is a critical component of driving employee engagement. Interactive elements of visualisation have multiple uses and benefits, including better comparative analysis and forecasting which allow businesses to make quicker and more efficient decisions.

We explore:

  • Visualisations ease of usability and quicker identification of solutions to business issues
  • How to find patterns and trends for impactful action
09:55
Why are AutoML and augmented analytics an integral part of the future of ML?

Dr Stylianos Kampakis, Expert Data Scientist, Research Fellow, UCL Centre for Blockchain Technology

A large part of the history of computer science is around automation. Computers were initially used for simple repetitive number crunching. As science advances we move on to the automation of more complex tasks.

Machine learning has managed to automate tasks such as labelling of images and audio, and is now moving on to more complex endeavours such as autonomous vehicles.

However, machine learning remains a complex subject. The next stage in the evolution of ML is improving accessibility. AutoML and augmented analytics play a key role in this trend.

10.15
Harnessing Machine Learning for Data Cleansing

Businesses often approach data cleansing with a trial and error method. There is a growing need for scalable and principled data cleaning solutions within enterprise, this session explores machine learning as a scalable solution for data cleansing:

  • Scalability by automated data matching, avoiding errors and manipulation in new and existing data sets
  • Automating data matching by using a learning model to predict matches which consequently sees the algorithms work better
  • Taking a holistic approach compiling all available rules and scripts into signals and features to enable clearer process guidance
10.30
How to Avoid the Perils and Pitfalls of Big Data Projects

Bryan Barrow, Big Data and Analytics Programme Manager, Dyson

Big Data has been promoted as a potential game changer for businesses and will soon come to dominate IT spend. However, the majority of British Big Data projects fail to realise their full potential. Many businesses are still stuck taking their first tentative steps / dipping their toes into the Big Data lake consequences.

This talk will discuss the perils and pitfalls of delivering Big Data programmes, for those about to embark on the journey, so that they reduce the risk of failure and accelerate deliver results.

  • Why Internal politics, not lack of skills, is your biggest challenge
  • Why you need to start small but design to scale
  • How to Make Security, Privacy and Governance your Friends
  • Developing your Big Data roadmap
  • Big Data starts with clean data
10.50
Questions To The Panel Of Speakers
11.00
Refreshment Break Served in the Exhibition Area
11.30
Drowning in the Data Lake? Ignore it instead...

Julian Highley, Director of Data and Analytics, Clarks

Retailers have an unprecedented amount of data available to them both big and small: customer transactions, web browsing, CX program tracking, competitor range, price and store location data through search terms and web scrapping, general search, weather, economic indicators – the list goes on. But, how do you take all of this information and make better business decisions as a result rather than being overwhelmed by a deluge of KPI?

Julian will be discussing approaches to creating a data network that enables the company to understand what impacts revenue and enables you to direct investments with confidence.

  • Forget Big Data – Focus on data that will deliver value
  • There are too many measures that organisations are looking at today – cut them down to those that are really important
  •  While significant effort is put into linking data, “soft links” can yield greater value
  • How suppliers can now deliver value has fundamentally shifted to providing clients access to data rather than tools
11.50
Big Data DevOps

Little is said about combining Big Data with DevOps however trends in data are shifting. In this session we look at integrating DevOps with Big Data, the challenges and the benefits:

  • Breaking down siloes between departments to improve collaboration and using resource coordination as key driver for implementation
  • More effective planning of software updates earlier in the cycle to decrease error rates which enables increased efficiency
  • The benefits for both DevOps teams and Data teams are mutually exclusive in particular the case of the DevOps feedback process
12.05
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
12.15
Seminar Sessions

(To view topics see the seminars page)

13.00
Networking Lunch Served in the Exhibition Area

Session Two: Making Data the Centrepiece of your Business

  • Exploring common pitfalls and how to avoid these
  • Solving Critical Challenges and Fulfilling your Strategic Vision
  • Cultivating a data-driven culture, people, and skills
  • Managing and implementing a secure and scalable Big Data architecture
14.00
Conference Chair’s Afternoon Address
14:05
Ethics and Impact, the 'Humanity' in Data science

Satya Singh, Senior Product Manager – Data, Marketing and Automation, Hotels.com

How do we ensure that we don’t forget about the impact of data science socially? How do we make sure that data science has a positive social and emotional impact? Well, we need to understand that data is not objective if there is no human in the loop. Data is a mere tool to build fancy products which can become great products if we think of ethics and impact on society in general.

14.20
Implementing a Data-Driven Culture

Data driven decisions take the guesswork out of business tactics. We drill down into the key components of a data driven culture, by exploring how to:

  • How to assess your current data culture to identify your businesses urgent goals
  • Set data priorities – mapping out steps to lead to complete data absorption
  • Segment data – identify key streams of data and which sources provide the most valuable information
  • Streamline your data and avoid inaccurate reporting by applying a cross analysis data system which will also reduce costs in your data storage
  • Trust your data – depersonalise decision making and introducing A/B Testing to measure and determine success metrics.
14.35
When Cryptography Meets Big Data Analytics

Aisling Connolly, Cryptography and Privacy Researcher, Information Security, École Normale Supérieure

The introduction of the GDPR and high-profile data breaches have thrown privacy into the spotlight on the data stage. Traditional business models are being disrupted, and many methods for data analytics are no longer plausible. It is difficult to see how to move forward in this age of information, where privacy must remain a core principle.

At the same time, what was once an innovation barrier, cryptography has seen major advances over the past decade. We are no longer confined to either seeing ‘all or nothing’ when looking at data, but instead, can define how much, or how little we want to see.

These cryptographic advances open whole new avenues of exploration for data, and for privacy-preserving analytics. This talk will give a brief overview of such technologies.

• Cryptography is no longer a barrier to Innovation
• We can blindly go where no one has gone before
• Examples of Zero-Knowledge Computing
• In terms of Big Data, what does this allow?

14:50
Improving Employee Performance with Big Data

This session explores how business leaders can develop a solid corporate training strategy by utilising data analytics.

Learn how to:

  • Measure and track employee performance
  • Deploy metrics throughout the enterprise landscape
  • Develop a panoptic business perspective

 

15.05
Questions to the Panel of Speakers
15.15
Afternoon Networking and Refreshments served in the Exhibition Area
15.45
The Impact of The Four V’s on Datacentre Infrastructure

The masses of data are driving development of storage hardware and networks in DC’s, ultimately creating continuous innovation in areas such as capacity and security.

We look at:

  • How to scale out without affecting performance: Analysing the flash based storage system for optimal real time performance
  • Techniques to create cost effective storage: E.g Data redundancy and data de-duplication
16.00
Securing your Big Data with Machine Learning

Securitising data sets tend to be an afterthought for many enterprises as they often struggle to identify and deploy the most appropriate technologies required to successfully complement their existing architecture.

We look at the growing role of Machine Learning and its practical application to the enterprise landscape.

16.15
Closing Key Note: What’s Next for Big Data?

What does the future have in store for the data-centric enterprise?

We examine the forward momentum of big data in the coming months and years.

16.30
Questions to the Panel of Speakers
16.40
Closing Remarks from the Conference Chair
16:45
Conference Closes

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