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Day 1

Registration and breakfast

Introduction from Chair

Alex Weber Award-winning host, motivational comedian speaker .

Real time data analysis and ML

- Understanding time series forecasting
- Machine learning models for time series forecasting
- How to implement the models using open source libraries

Subha Tatavarti Head of Product and Strategy PayPal

Machine learning & AI best practice for forecasting

- Understanding time series forecasting
- Machine learning models for time series forecasting
- How to implement the models using open source libraries

Ilkay Altintas Chief Data Science Officer San Diego Supercomputer Center

Machine Learning and Artificial Intelligence at Marketing@Uber

Uber is part of the logistical fabric of more than 700 cities around the world. Whether it’s a ride, a sandwich, or a package, we use technology to give people what they want, when they want it.

Uber spends hundreds of millions of dollars in acquisition and retention and we are constantly optimizing the allocation of these budgets and performing experimentation.

We use AI in creative ways to:
- Improve the signal on A\B experiments and have better reads and insights
- Advanced segmentation of customers by propensity to act, churn, open an email
- Cross sell predictions
- Models of resurrection and reactivation
- Natural Language to provide insights on content
- Loyalty programs

In this talk, I will discuss how predictive models are used across these areas

- How to think and interpret predictive models
- What metrics we use to evaluate these models
- The tools and technologies we use
- Specific case studies in optimization, channel attribution

Mario Vinasco Marketing Analytics Manager Uber

Discussion table session 1

Choose a 30-minute discussion from across all three streams and learn from the experience of the discussion leader, your fellow delegates, and share your own perspective. Each discussion group is limited to seven participants.

Management

Using Data and Machine Learning to Make Consumer Insights

• How to connect your strategy to customer analytics?
• What is a customer centric view and why is it important?
• Which predictive approaches and models can you use?
• How to drive engagement and retention?

Rich Fox VP Data Science & Analytics Apex Parks

Placing large data in platforms

Vadim Kutsyy Data Science Strategic Architect PayPal

What data should we collect

Zheng Shao Staff Data Scientist LinkedIn

Insight

Getting closer to the customer and region with analytics

· real-time big data analytics
· data science workflows for e-commerce CRM strategy
· understanding customer behavioral patterns
· machine learning for personalized recommendations
· collaborative-filtering and content based recommender systems

Long Pei Data Scientist LinkedIn

From Insight to Action: Effective storytelling with data

Data can be overwhelming and complicated. Often there is so much, it is difficult to isolate a message and tell a story in a meaningful way. While analysis can help drive better business decisions, the results must be conveyed in an effective manner in order to drive action. Despite what data lovers may believe, not everyone is excited by numbers. This roundtable discussion will allow for the exchange of ideas around effective techniques for storytelling with data.

• Knowing your audience
• How the brain processes information
• Explain the importance of translating data into a story
• Share best practices for crafting data narratives.
• Share best practices for designing strong data-driven visualization
• Getting Started

Sheri Marshall Head of Global Analytic Capability Development General Motors

Success factors of Analytics

Prioritizing business needs
Hiring the right talent
Leveraging existing data assets
Establishing standards
Building upon previous studies (internal and external)

Ruben Quiñonez Associate Director, Advanced Analytics AT&T Entertainment Group

Distractive driver and zero crash initiative

• Drivers media listening behaviour
• ADAS features like following distance, line drifting, brake behaviour and some drivers eye gazes will be discussed in good and bad. Are they really helping us to decrease the number of accidents. How close are we to reach our ZERO crash goal.
• Is radio volume changing behaviour a distracting factor? What has been found? Demographical similarities and differences
• How will these findings factor in insurance industry
• Future projections

Meltem Ballan, Ph.D. Data Scientist General Motors

Morning Coffee

Discussion table session 2

Choose a 30-minute discussion from across all three streams and learn from the experience of the discussion leader, your fellow delegates, and share your own perspective. Each discussion group is limited to seven participants.

Management

Using Data and Machine Learning to Make Consumer Insights Actionable

• How to connect your strategy to customer analytics?
• What is a customer centric view and why is it important?
• Which predictive approaches and models can you use?
• How to drive engagement and retention?

Rich Fox VP Data Science & Analytics Apex Parks

Placing large data in platforms

Vadim Kutsyy Data Science Strategic Architect PayPal

What data should we collect

Zheng Shao Staff Data Scientist LinkedIn

Insight

Getting closer to the customer and region with analytics

· real-time big data analytics
· data science workflows for e-commerce CRM strategy
· understanding customer behavioral patterns
· machine learning for personalized recommendations
· collaborative-filtering and content based recommender systems

Long Pei Data Scientist LinkedIn

Distractive driver and zero crash initiative

• Drivers media listening behaviour
• ADAS features like following distance, line drifting, brake behaviour and some drivers eye gazes will be discussed in good and bad. Are they really helping us to decrease the number of accidents. How close are we to reach our ZERO crash goal.
• Is radio volume changing behaviour a distracting factor? What has been found? Demographical similarities and differences
• How will these findings factor in insurance industry
• Future projections

Meltem Ballan, Ph.D. Data Scientist General Motors

From Insight to Action: Effective storytelling with data

Data can be overwhelming and complicated. Often there is so much, it is difficult to isolate a message and tell a story in a meaningful way. While analysis can help drive better business decisions, the results must be conveyed in an effective manner in order to drive action. Despite what data lovers may believe, not everyone is excited by numbers. This roundtable discussion will allow for the exchange of ideas around effective techniques for storytelling with data.

• Knowing your audience
• How the brain processes information
• Explain the importance of translating data into a story
• Share best practices for crafting data narratives.
• Share best practices for designing strong data-driven visualization
• Getting Started

Sheri Marshall Head of Global Analytic Capability Development General Motors

Success factors of Analytics

- Prioritising business needs
Hiring the right talent
- Leveraging existing data assets
Establishing standards
- Building upon previous studies (internal and external)

Ruben Quiñonez Associate Director, Advanced Analytics AT&T Entertainment Group

Becoming a data-driven business

- Building and redefining your data strategy
- Creating the cultural change and removing barriers
- Adopting new technology

Samith Gunasekara Senior Manager - Data Product Initiatives Boeing

Predicting disk failures to improve IaaS availability

High service availability is crucial for cloud systems. A typical cloud system uses a large number of physical hard disk drives and solid state drives. Disk errors are one of the most important causes that lead to service unavailability. Disk error (such as reallocate sector error and long access latency) can be seen as a form of gray failure, which are fairly subtle failures that are hard to be detected, even when applications are afflicted by them. In this talk, we will introduce an approach to predict disk errors proactively to avoid severe damage to the cloud system. The ability to predict faulty disks enables live migration of existing virtual machines and allocation of new virtual machines to the healthy disks, therefore improving service availability. To build an accurate online prediction model, we utilize both disk-level sensor (SMART) data as well as system level signals. We develop a cost-sensitive ranking-based machine learning model that can learn the characteristics of faulty disks in the past and rank the disks based on their error-proneness in the near future. We evaluate our approach using real-world data collected from a production cloud system.

Youjiang Wu Senior Data Scientist Microsoft

Lunch

Understand your customer, online and offline

Nowadays it is paramount for companies to understand their customers better, both for organizations that have large online or offline presence. What are the keys to gain a holistic and accurate understanding of your customers? How testing can help both online and offline in proving out causality and creating predictive power? What are the challenges in applying the learnings more broadly?

Zheng Shao Staff Data Scientist LinkedIn

Discussion table session 3

Choose a 30-minute discussion from across all three streams and learn from the experience of the discussion leader, your fellow delegates, and share your own perspective. Each discussion group is limited to seven participants.

Management

Placing large data in platforms

Vadim Kutsyy Data Science Strategic Architect PayPal

What data should we collect

Zheng Shao Staff Data Scientist LinkedIn

Using Data and Machine Learning to Make Consumer Insights Actionable

• How to connect your strategy to customer analytics?
• What is a customer centric view and why is it important?
• Which predictive approaches and models can you use?
• How to drive engagement and retention?

Rich Fox VP Data Science & Analytics Apex Parks

Insight

Getting closer to the customer and region with analytics

Long Pei Data Scientist LinkedIn

Distractive driver and zero crash initiative

• Drivers media listening behaviour
• ADAS features like following distance, line drifting, brake behaviour and some drivers eye gazes will be discussed in good and bad. Are they really helping us to decrease the number of accidents. How close are we to reach our ZERO crash goal.
• Is radio volume changing behaviour a distracting factor? What has been found? Demographical similarities and differences
• How will these findings factor in insurance industry
• Future projections

Meltem Ballan, Ph.D. Data Scientist General Motors

Success factors of Analytics

- Prioritizing business needs
Hiring the right talent
- Leveraging existing data assets
- Establishing standards
- Building upon previous studies (internal and external)

Ruben Quiñonez Associate Director, Advanced Analytics AT&T Entertainment Group

From Insight to Action: Effective storytelling with data

Data can be overwhelming and complicated. Often there is so much, it is difficult to isolate a message and tell a story in a meaningful way. While analysis can help drive better business decisions, the results must be conveyed in an effective manner in order to drive action. Despite what data lovers may believe, not everyone is excited by numbers. This roundtable discussion will allow for the exchange of ideas around effective techniques for storytelling with data.

• Knowing your audience
• How the brain processes information
• Explain the importance of translating data into a story
• Share best practices for crafting data narratives.
• Share best practices for designing strong data-driven visualization
• Getting Started

Sheri Marshall Head of Global Analytic Capability Development General Motors

Discussion table session 4

Choose a 30-minute discussion from across all three streams and learn from the experience of the discussion leader, your fellow delegates, and share your own perspective. Each discussion group is limited to seven participants.

Management

Placing large data in platforms

Vadim Kutsyy Data Science Strategic Architect PayPal

What data should we collect

Zheng Shao Staff Data Scientist LinkedIn

Using Data and Machine Learning to Make Consumer Insights Actionable

• How to connect your strategy to customer analytics?
• What is a customer centric view and why is it important?
• Which predictive approaches and models can you use?
• How to drive engagement and retention?

Rich Fox VP Data Science & Analytics Apex Parks

Insight

Distractive driver and zero crash initiative

• Drivers media listening behaviour
• ADAS features like following distance, line drifting, brake behaviour and some drivers eye gazes will be discussed in good and bad. Are they really helping us to decrease the number of accidents. How close are we to reach our ZERO crash goal.
• Is radio volume changing behaviour a distracting factor? What has been found? Demographical similarities and differences
• How will these findings factor in insurance industry
• Future projections

Meltem Ballan, Ph.D. Data Scientist General Motors

Getting closer to the customer and region with analytics

· real-time big data analytics
· data science workflows for e-commerce CRM strategy
· understanding customer behavioral patterns
· machine learning for personalized recommendations
· collaborative-filtering and content based recommender systems

Long Pei Data Scientist LinkedIn

From Insight to Action: Effective storytelling with data

Data can be overwhelming and complicated. Often there is so much, it is difficult to isolate a message and tell a story in a meaningful way. While analysis can help drive better business decisions, the results must be conveyed in an effective manner in order to drive action. Despite what data lovers may believe, not everyone is excited by numbers. This roundtable discussion will allow for the exchange of ideas around effective techniques for storytelling with data.

• Knowing your audience
• How the brain processes information
• Explain the importance of translating data into a story
• Share best practices for crafting data narratives.
• Share best practices for designing strong data-driven visualization
• Getting Started

Sheri Marshall Head of Global Analytic Capability Development General Motors

Success factors of Analytics

- Prioritising business needs
Hiring the right talent
- Leveraging existing data assets
- Establishing standards
- Building upon previous studies (internal and external)

Ruben Quiñonez Associate Director, Advanced Analytics AT&T Entertainment Group

How Predictive Analytics Will Save Millions of Lives

- How does audience analytics solve the disconnect
- Focusing on consumer actions
- Sharing data across departments

Mohammad Shokoohi-Yekta Senior Data Scientist and Adjunct Faculty Apple

Building a Data-Driven Team - How to Hire, Train, and Retain Talent

Data Science is undoubtedly the most exciting opportunity for companies to take advantage of. However, given the relative newness of the field, there are many companies struggling to create long-lasting value from their investments in their Data Science teams. As an executive or hiring manager, it's critical to know why you're hiring for data scientists, what problem they're solving; and to properly integrate that team into other core personnel of your business so that they are set up to succeed. In this talk, we'll explore important differentiations with regard to Data Analytics, Data Science, and Machine Learning, how to understand what skillsets are right for a variety of business problems; and how to properly hire, train, and retain talent.

Andrew Savage Head of Partnerships, Senior Career Advisor Metis

Closing remarks from Chair - Drinks

Alex Weber Award-winning host, motivational comedian speaker .

Day 2

Registration and breakfast

Introduction from Chair

Alex Weber Award-winning host, motivational comedian speaker .

From Academia to Industry: What Makes You Successful!

-Successful predicting
- Variation management

Yashar Mehdad Data Science Manager Airbnb

Smart Cites

- Leverage the best Smart Cities AI and IoT tech
- Talent to help make more informed decisions

Innovation in Infrastructure: How the Smart City Really Thrives

There’s been plenty of discussion around sensors and data management in the smart city – but what about the core of all urban environments, infrastructure? We discuss how investments in infrastructure – and often the infrastructure we can’t see – positively impact smart city initiatives and make a substantial difference in the day-to-day lives of citizens.

Adam Tank Director of Smart Cities Suez

Panel: Data analytics being the driver of your organisation

- Starting with why this is necessary
- Overcoming being overwhelmed with data
- Developing vision and strategy

Yashar Mehdad Data Science Manager Airbnb
Adam Tank Director of Smart Cities Suez

Morning Coffee

Redefining your data strategy

- How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
- What are the best practices along the way?
- How do you avoid the most treacherous pitfalls?

Clifton Roberts Director, Cloud Policy Intel

A New Ecosystem Approach to Improve Data Science Success

- The dynamic nature of deep learning methods
- Creating greater personalisation of customer analytics
- Improving accuracy and performance in applications

Dr Alex Liu Chief Data Scientist IBM

Product Insights on Spotify Ad Studio

- Overview of Product Insights at Spotify - How user research and data science come together
- Establishing analytics on a new product - How to be forward looking when gathering data
- Delivering actionable & high value insights - How to work with small data & derive value

Matthew Farkas Senior Data Scientist Spotify

Data science best practice

1. How do you create and define your data strategy?

2. How has the implementation of data/machine learning impacted your business as a culture and was there any barriers that you need to remove?

3. Adopting new technology: Is there any software, hardware or external assistance that helps your day to day role.

Clifton Roberts Director, Cloud Policy Intel
Dr Alex Liu Chief Data Scientist IBM
Matthew Farkas Senior Data Scientist Spotify

Lunch

Advanced Data Analytics

Ramkumar Ravichandran Director - Data Science & Analytics Google

Transform the freight rail industry. Innovations in the industry

- Innovations in the industry
- Dealing with large amounts of data
- Understanding the advanced database platform

Fred Ehlers CIO Norfolk Southern Corporation

Deploy Machine Learning Models to Production

Danial Sabri Dashti Senior Machine Learning Scientist Amazon

You have data. Now What?

Companies are moving towards making data driven decisions. Is having data enough to becoming data driven? Clearly, the answer is no. Not only do we need the right data, but it imperative that right steps be taken to convert data into actionable insights. Often, not enough thought is given to this aspect, therefore, rendering their data to be somewhat useful to useless. However, with a little discipline to follow a few steps consistently, you can help your organization make better decisions. In this talk, the presenter will share a framework to set the right KPIs (Key Performance Indicators), gather the relevant data and develop an action plan that supports your business goals. This is a versatile framework that can be applied to product launches, incremental feature releases, systems availability and operations.

Amitha Krishnappa Senior Manager, Analytics Walmart Labs

Data methods, doubts and future Q&A

1. How do you pick the right one to deliver the greatest impact for your business, as applied over your data?

2. What are the best practices along the way?

3. What are some of your pitfalls? How do you avoid the pitfalls?

Ramkumar Ravichandran Director - Data Science & Analytics Google
Fred Ehlers CIO Norfolk Southern Corporation
Amitha Krishnappa Senior Manager, Analytics Walmart Labs

Closing remarks from Chair - Coffee

Alex Weber Award-winning host, motivational comedian speaker .