Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
22–24 May 2018: Tutorials & Conference
London, UK
Schedule: Data-driven business management sessions
9:00–12:30 Tuesday, 22 May 2018
Location: Capital Suite 12
Level: Non-technical
Secondary topics:
Visualization, Design, and UX
Radhika Dutt (Radical Product),
Geordie Kaytes (Fresh Tilled Soil),
Nidhi Aggarwal (Radical Product)
Average rating:
(4.00, 2 ratings)
These days it’s easy for companies to say, "We measure everything!” The problem is, most popular metrics may not be appropriate or relevant for your business. Measurement isn’t free and should be done strategically. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal explain how to align measurement with your product strategy so you can measure what matters for your business.
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9:00–17:00 Tuesday, 22 May 2018
Location: Capital Suite 4
Paul Lashmet (Arcadia Data),
Anthony Culligan (SETL),
Konrad Sippel (Deutsche Börse),
Paul Lynn (Nordea),
Mikheil Nadareishvili (TBC Bank),
Olaf Hein (ORDIX AG),
Robert Passarella (Alpha Features),
Louise Beaumont (Publicis Groupe | techUK | NPSO),
Alistair Croll (Solve For Interesting),
Robert Passarella (Alpha Features),
Christina Erlwein-Sayer (OptiRisk Systems),
Angelique Mohring (GainX),
Saeed Amen (Cuemacro),
Gisele Frederick (Zingr.io)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry.
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9:00–17:00 Tuesday, 22 May 2018
Location: Capital Suite 2/3
Dan Jeavons (Shell),
Hollie Lubbock (Fjord),
Jivan Virdee (Fjord),
fausto morales (Arundo),
Marty Cochrane (Arundo),
Jane McConnell (Teradata),
Paul Ibberson (Teradata),
Michael Troughton (Conduce),
Jonathan Genah (DHL Supply Chain),
Allison Nau (Cox Automotive UK),
Dave Fitch (The Data Lab),
Maria Assunta Palmieri (Data Reply ),
Niranjan Thomas (Dow Jones),
Erik Elgersma (FrieslandCampina),
Viola Melis (Typeform),
carme artigas (Synergic Partners),
Nuria Bombardo (Pepsico)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.
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11:15–11:55 Wednesday, 23 May 2018
Location: Capital Suite 10/11
Level: Beginner
Secondary topics:
Transportation and LogisticsAverage rating:
(4.45, 11 ratings)
Because in-house data science teams work with a range of business functions, traditional data science processes are often too abstract to cope with the complexity of these environments. Alberto Rey Villaverde and Grigorios Mingas share case studies from easyJet that highlight some unpredictable hurdles related to requirements, data, infrastructure, and deployment and explain how they solved them.
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11:15–11:55 Wednesday, 23 May 2018
Location: Expo Hall
Level: Beginner
Secondary topics:
Media, Advertising, EntertainmentAverage rating:
(3.75, 4 ratings)
In the era of 24-hour news and online newspapers, editors in the newsroom must quickly and efficiently make sense of the enormous amounts of data that they encounter and make decisions about their content. Daniel Gilbert and Jonathan Leslie discuss an ongoing partnership between News UK and Pivigo in which a team of data science trainees helped develop an AI platform to assist in this task.
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12:05–12:45 Wednesday, 23 May 2018
Location: S11B
Level: Beginner
Secondary topics:
Data Platforms,
E-commerce and Retail,
Transportation and Logistics
Mao Baolong, Yiran Wu, and Yupeng Fu explain how JD.com uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component. To give just one example, one framework, JDPresto, has seen a 10x performance improvement on average.
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12:05–12:45 Wednesday, 23 May 2018
Location: Capital Suite 15/16
Level: Intermediate
Secondary topics:
Telecom,
Time Series and Graphs
Ira Cohen (Anodot)
The mobile world has so many moving parts that a simple change to one element can cause havoc somewhere else, resulting in issues that annoy users and cause revenue leaks. Ira Cohen outlines ways to use anomaly detection to track everything mobile, from the service and roaming to specific apps, to fully optimize your mobile offerings.
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12:05–12:45 Wednesday, 23 May 2018
Location: Capital Suite 17
Level: Non-technical
Average rating:
(3.12, 8 ratings)
A data-driven enterprise maximizes the value of its data. But how do enterprises emerging from technology and organization silos get there? Teresa Tung and Jean-Luc Chatelain explain how to create a data-driven enterprise maturity model that spans technology and business requirements and walk you through use cases that bring the model to life.
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14:05–14:45 Wednesday, 23 May 2018
Location: Capital Suite 7
Level: Intermediate
Secondary topics:
Security and Privacy
Joshua Patterson (NVIDIA),
Chau Dang (NVIDIA)
Joshua Patterson and Mike Wendt explain how NVIDIA used GPU-accelerated open source technologies to improve its cyberdefense platforms by leveraging software from the GPU Open Analytics Initiative (GOAI) and how the company accelerated anomaly detection with more efficient machine learning models, faster deployment, and more granular data exploration.
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14:05–14:45 Wednesday, 23 May 2018
Location: Capital Suite 10/11
Level: Intermediate
Manas Ranjan Kar (Episource)
Average rating:
(3.00, 3 ratings)
Episource is building a scalable NLP engine to help summarize medical charts and extract medical coding opportunities and their dependencies to recommend best possible ICD10 codes. Manas Ranjan Kar offers an overview of the wide variety of deep learning algorithms involved and the complex in-house training-data creation exercises that were required to make it work.
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14:05–14:45 Wednesday, 23 May 2018
Location: Capital Suite 14
Level: Non-technical
Average rating:
(5.00, 2 ratings)
Artificial intelligence systems are powerful agents of change in our society, but as this technology becomes increasingly prevalent—transforming our understanding of ourselves and our society—issues around ethics and regulation will arise. Jivan Virdee and Hollie Lubbock explore how to address fairness, accountability, and the long-term effects on our society when designing with data.
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14:55–15:35 Wednesday, 23 May 2018
Location: Capital Suite 14
Level: Beginner
Secondary topics:
Visualization, Design, and UX
Brian O'Neill (Designing for Analytics)
Average rating:
(4.00, 2 ratings)
Gartner says 85%+ of big data projects will fail. Your own company may have even spent millions on a recent project that isn’t really delivering the value or UX everyone hoped for. Brian O'Neill explains why CDOs, PMs, and business leaders who leverage design to prioritize utility, usability, and customer value will realize the best ROIs and demonstrates how to start evaluating your UX.
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14:55–15:35 Wednesday, 23 May 2018
Location: Capital Suite 15/16
Level: Non-technical
Average rating:
(4.67, 9 ratings)
Our lives are being transformed by data, changing our understanding of work, play, and health. Every organization can take advantage of this resource, but something is holding us back: us. Kim Nilsson and Phil Harvey explain how to build a successful data culture that embeds data at the heart of every organization through people and delivers success through empathy, communication, and humanity.
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14:55–15:35 Wednesday, 23 May 2018
Location: Capital Suite 17
Level: Beginner
Dean Wampler (Anyscale)
Average rating:
(4.00, 2 ratings)
Streaming data systems, so called fast data, promise accelerated access to information, leading to new innovations and competitive advantages. But they aren't just faster versions of big data. They force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices. Dean Wampler outlines what you need to know to exploit fast data successfully.
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16:35–17:15 Wednesday, 23 May 2018
Location: S11B
Level: Intermediate
Secondary topics:
Text and Language processing and analysisAverage rating:
(2.00, 1 rating)
DevOps and QA engineers spend a significant amount of time investigating reoccurring issues. These issues are often represented by large configuration and log files, so the process of investigating whether two issues are duplicates can be a very tedious task. Ran Taig and Omer Sagi outline a solution that leverages NLP and machine learning algorithms to automatically identify duplicate issues.
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16:35–17:15 Wednesday, 23 May 2018
Location: Capital Suite 15/16
Level: Intermediate
Average rating:
(5.00, 2 ratings)
Jude McCorry and Mahmood Adil offer an overview of Data Collaboratives, a new form of collaboration beyond the public-private partnership model, in which participants from different sectors exchange data, skills, leadership, and knowledge to solve complex problems facing children in Scotland and worldwide.
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17:25–18:05 Wednesday, 23 May 2018
Location: Capital Suite 15/16
Level: Non-technical
Richard Goyder (IMC Business Architecture | Scaled Insights),
Barry Singleton (IMC Business Architecture)
Average rating:
(3.60, 5 ratings)
Big data analytics tends to focus on what is easily available, which is by and large data about what has already happened, the implicit assumption being that past behavior will predict future behavior. Organizations already possess data they aren’t exploiting. Barry Singleton and Richard Goyder explain how, with the right tools, it can be used to develop far more powerful predictive algorithms.
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17:25–18:05 Wednesday, 23 May 2018
Location: Capital Suite 17
Level: Intermediate
Secondary topics:
Managing and Deploying Machine Learning
David Talby (Pacific AI)
Average rating:
(4.00, 1 rating)
Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.
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12:05–12:45 Thursday, 24 May 2018
Location: Capital Suite 15/16
Level: Intermediate
Martin Goodson (Evolution AI)
Average rating:
(4.25, 4 ratings)
How can AI become part of our business processes? Should we entrust critical decisions to completely autonomous systems? Drawing on projects from businesses and UK government agencies, Martin Goodson explains how to increase confidence in AI systems and manage the transition to an AI-driven organization.
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14:05–14:45 Thursday, 24 May 2018
Location: Capital Suite 8/9
Level: Intermediate
Kostas Kloudas (data Artisans)
Average rating:
(2.25, 4 ratings)
Complex event processing (CEP) helps detect patterns over continuous streams of data. DNA sequencing, fraud detection, shipment tracking with specific characteristics (e.g., contaminated goods), and user activity analysis fall into this category. Kostas Kloudas offers an overview of Flink's CEP library and explains the benefits of its integration with Flink.
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14:05–14:45 Thursday, 24 May 2018
Location: Capital Suite 12
Level: Beginner
Kaylea Haynes (Peak )
Deciding how much stock to hold is a challenge for hire businesses. There is a fine balance between holding enough stock to fulfill hires and not holding too much stock so that overall utilization is too low to achieve the return on investment. Kaylea Haynes shares a case study on forecasting the demand for thousands of assets across multiple locations.
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14:05–14:45 Thursday, 24 May 2018
Location: Capital Suite 15/16
Level: Beginner
Average rating:
(5.00, 1 rating)
What are the latest initiatives and use cases around data and AI? How are data and AI reshaping industries? How do we foster a culture of data and innovation within a larger enterprise? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries to answer these questions and more.
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14:55–15:35 Thursday, 24 May 2018
Location: Capital Suite 7
Level: Intermediate
Secondary topics:
Financial Services,
Managing and Deploying Machine Learning
Hope Wang (Intuit)
Average rating:
(4.00, 3 ratings)
A machine learning platform is not just the sum of its parts; the key is how it supports the model lifecycle end to end. Hope Wang explains how to manage various artifacts and their associations, automate deployment to support the lifecycle of a model, and build a cohesive machine learning platform.
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14:55–15:35 Thursday, 24 May 2018
Location: Capital Suite 12
Level: Non-technical
Average rating:
(4.60, 5 ratings)
Cox Automotive is the world’s largest automotive service organization, which means it can combine data from across the entire vehicle lifecycle. Cox is on a journey to turn this data into insights. David Asboth and Shaun McGirr share their experience building up a data science team at Cox and scaling the company's data science process from laptop to Hadoop cluster.
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14:55–15:35 Thursday, 24 May 2018
Location: Capital Suite 15/16
Level: Non-technical
Secondary topics:
Data Platforms,
Managing and Deploying Machine Learning
Simon Chan (Salesforce)
Average rating:
(4.00, 1 rating)
The promises of AI are great, but taking the steps to implement AI within an enterprise is challenging. The secret behind enterprise AI success often traces back to the underlying platform that accelerates AI development at scale. Based on years of experience helping executives establish AI product strategies, Simon Chan helps you discover the AI platform journey that is right for your business.
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14:55–15:35 Thursday, 24 May 2018
Location: Capital Suite 14
Level: Intermediate
Chen Salomon (Playbuzz)
Average rating:
(4.00, 1 rating)
A/B testing is the foundation of data-driven decision making. In today's world, advertising is crucial to a website's revenue, so it is even more important to measure the effects of changes correctly. Chen Salomon demonstrates how to correctly design and implement an advertisement A/B testing and shares pitfalls, potential biases related to advertisement metrics, and possible mitigations.
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16:35–17:15 Thursday, 24 May 2018
Location: Capital Suite 15/16
Level: Intermediate
Ketan Gangatirkar (Indeed)
Average rating:
(5.00, 1 rating)
Quantitative measurement is the key to scaling businesses, processes, and products and making them better. It sounds easy: just pick a number and improve it. However, actually choosing a metric is an exploration of a many-dimensional space with no map and no guide. Until now. Join Ketan Gangatirkar to learn how to choose the right metrics so you can build a better product and a better business.
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16:35–17:15 Thursday, 24 May 2018
Location: Capital Suite 17
Level: Intermediate
Kevin Sigliano (IE Business School )
Average rating:
(5.00, 1 rating)
Financial and consumer ROI demands that business leaders understand the drivers and dynamics of digital transformation and big data. Kevin Sigliano explains why disrupting value propositions and continuous innovation are critical if you wish to dramatically improve the way your company engages customers and creates value and maximize financial results.
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