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Day 1

Registration and breakfast

Introduction from Chair

Alex Weber Award-winning host, motivational comedian speaker .

Morning Keynote: The Personalization Cookbook - What does it take to get it right!

Although Personalization figures at the top of "to get right" list of strategic objectives, hardly a few get it right. It's much more than a bunch of algorithms, product-market fit, a pretty front end design or even lots of incentives. It's complete re-architecting of the soul of the organization! It's a journey from being in love with the product that was created and finding the customers to solving a "user's problem" sustainably, efficiently and effectively. This talk will be a walk through of the process of creating a vision, the strategic goals and execution of a Personalization program with Data Driven Optimization. It will touch upon the advancement along the Analytics Maturity Curve from the right metric creation for the Strategic Objectives, shoring up the Data Platform, a "Learn-Listen-Test" framework of iterative execution and finally scaling with Machine Learning solutions. The intention is to share the lessons along the journey with the audience and take back insights from their own personal experience of pulling this off.

Ramkumar Ravichandran Data Science Manager Google

When Can We Trust a Decision Made by a Machine: Building Trustable AI and Detecting Misinformation

With the advent of machine and deep learning, explainability and interpretability has become paramount to traceable and justifiable and explainable results:
when someone's house gets foreclosed, some one is sentenced, an insurance claim is denied, a mortgage application is denied. There are legal, human, organizational, IP and societal implications of leveraging the augmented intelligence of machines in supporting highly complex and previously only open to highly educated, highly skilled experts in medicine, underwriting, law, adjudication, etc.

The need to create and train unbiased or minimally biased datasets for deep learning in the interests of Fairness, Accessibility and Transparency requires best practices that are not known to organizations embarking on machine learning and AI activities.

In this session we will cover the methods and techniques and best practices to detect, manage and mitigate bias in datasets, deep neural network training, application integration. We will also explore the cognitive biases that lead us to curate date to create less than trustworthy blackbox AI systems, and how to avoid them.

We will explore in detail (code level as well as architecture), the project AlternusVera which detects fakeness or deliberate misinformation in a body of text.

Ali Arsanjani Vice President, Artificial Intelligence Deep Context

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

Lost in Thought - Garbage in Garbage out

Analytics is the key component of any and all organizations. We all understand the importance clean data prior to any type of predictive model. The lack of success with predictive analytics starts at the base of the data and how it comes in and is cleaned. In a perfect world beginning to end is seamless with data is entered into any system, however reality is different. This short session is a look in the beginning of success of locating gaps, cleaning data in order to move to that next stage, where successful predictive analytics can make a difference.

Richard Hardin Principal Data Scientist VSE Corporation

Morning Coffee

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

Harnessing Data Scientists in Your Organization

• Difference between data scientist and data analyst
• When you need a data scientist?
• Common mistakes in hiring data scientists
• How to effectively use data scientists in your business? • How engineers and scientist should work together?

Danial Sabri Dashti Senior Machine Learning Scientist Amazon

Using Data and Machine Learning to Make Consumer Insights Actionable s

• 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

Leverage testing to understand customers and make profitable decision

What’s common and unique about online and offline businesses.

Zheng Shao Staff Data Scientist LinkedIn

Data Science Best Practice

Overall, how do you define data science?
- How do you think about data science automation?
- Can you name one easy mistake data scientists usually make?
- What is the best composition of a best data science team?
- Can you name a special difficulty of communicating data science results?

Dr Alex Liu Chief Data Scientist IBM

Reason Predictive Analytics Fail in Industry

1. The journey of Data Science in practice
2. Where we are today in consumption
3. Reasons why only portions of the market is embracing it
4. How can it be better

Sheela Siddappa Global Head- Data Science Robert Bosch

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

How to find and train Data Scientists, and reduce the risk of brain drain.

- Is my analytics organization ready for predictive analytics and machine learning?
- Do I need a Data Scientist?
- Business Knowledge vs. Statistics Knowledge
- Strategic Alignment
- Resource funnel Partnerships

Jesse Mauser Vice President Data & Analytics Atlas General

predictive maintenance and hardware failure prediction

1. Application of predictive maintenance
2. Common challenges to predictive maintenance
3. Early fault detection and diagnosis
4. Leveraging IoT to predict failures
5. New technologies and industry trends

Youjiang Wu Senior Data Scientist Microsoft

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 PhD Associate Director, Advanced Analytics AT&T Entertainment Group

 Modern technological advancements 

Modern technological advancements that have supposedly made today's communication much easier, but in reality has isolated many of us.

The conveniences of e-commerce, online shopping has inadvertently contributed to the continued lack of interfacing or conventional communication.

Places of work, the 'corporate woke space' will soon be replaced with the majority of tomorrows workers spending more time at home or places of their choice and working unconventional hours that best fit their needs and scheduling. Once again, providing modern conveniences but continuing the trend of isolationism.

The often overlooked, and most effective form of communication, looking at, and talking directly at someone, is becoming a lost and treasured art. We have insulated ourselves, and our natural instinctive forms of linear communication have diminished considerably.

We now interact at warp like speed, but the question I pose to my audience is 'DO WE COMMUNICATE EFFECTIVELY
I provide the answers to how we can reemerge from these barriers we have inadvertently created around ourselves in this remarkable digital era!

Dan Devone TV Personality NBC Sports

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

Challenges towards Consuming Data Science in Industry

Data Science and Analytics is part of planning for multiple organizations. We all understand the importance of prediction and the role it plays in our business. If it performs to the extent we study in books/theory, there would be very less room for improvement. The reality is different from books. The talk focuses on the gaps and the reasons behind the gaps with an industry use case, based on experience.

Sheela Siddappa Global Head- Data Science Robert Bosch

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

Leverage Testing to Understand Customers and Make Profitable Decisions

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 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

Harnessing Data Scientists in Your Organization

• Difference between data scientist and data analyst
• When you need a data scientist?
• Common mistakes in hiring data scientists
• How to effectively use data scientists in your business? • How engineers and scientist should work together?

Danial Sabri Dashti Senior Machine Learning Scientist Amazon

Leverage testing to understand customers and make profitable decisions

What’s common and unique about online and offline businesses.

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

Data Science Best Practice

- Overall, how do you define data science?
- How do you think about data science automation?
- Can you name one easy mistake data scientists usually make?
- What is the best composition of a best data science team?
- Can you name a special difficulty of communicating data science results?

Dr Alex Liu Chief Data Scientist IBM

Reason Predictive Analytics Fail in Industry

1. The journey of Data Science in practice
2. Where we are today in consumption
3. Reasons why only portions of the market is embracing it
4. How can it be better

Sheela Siddappa Global Head- Data Science Robert Bosch

Insight

How to find and train Data Scientists, and reduce the risk of brain drain.

- Is my analytics organization ready for predictive analytics and machine learning?
- Do I need a Data Scientist?
- Business Knowledge vs. Statistics Knowledge
- Strategic Alignment
- Resource funnel Partnerships

Jesse Mauser Vice President Data & Analytics Atlas General

predictive maintenance and hardware failure prediction

1. Application of predictive maintenance
2. Common challenges to predictive maintenance
3. Early fault detection and diagnosis
4. Leveraging IoT to predict failures
5. New technologies and industry trends

Youjiang Wu Senior Data Scientist Microsoft

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 PhD 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

 Modern technological advancements 

Modern technological advancements that have supposedly made today's communication much easier, but in reality has isolated many of us.

The conveniences of e-commerce, online shopping has inadvertently contributed to the continued lack of interfacing or conventional communication.

Places of work, the 'corporate woke space' will soon be replaced with the majority of tomorrows workers spending more time at home or places of their choice and working unconventional hours that best fit their needs and scheduling. Once again, providing modern conveniences but continuing the trend of isolationism.

The often overlooked, and most effective form of communication, looking at, and talking directly at someone, is becoming a lost and treasured art. We have insulated ourselves, and our natural instinctive forms of linear communication have diminished considerably.

We now interact at warp like speed, but the question I pose to my audience is 'DO WE COMMUNICATE EFFECTIVELY
I provide the answers to how we can reemerge from these barriers we have inadvertently created around ourselves in this remarkable digital era!

Dan Devone TV Personality NBC Sports

Empirical Dynamic Modeling: Letting Data Tell Us The Story

The universe can be defined by the data it generates. However, collecting all of the universe’s data is impossible. In this talk, Erik will discuss how he uses mathematical techniques to account for missing data, and use this information with dynamic modeling algorithms to garner a deeper understanding about our complex world. He will then discuss how XYO is creating a unique and powerful dataset that will surely lead to incredible insights when analyzed.

Erik Saberski Data Scientist XYO Network

Afternoon Keynote: Modern technological advancements 

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

Dan Devone TV Personality NBC Sports

Workshop: 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

Networking drinks

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 .

Secrets to Data Science Success

Recruiting and cultivating cross-functional teams for data science projects Identifying high impact projects using design thinking methods, data project workflows and building relationships. Tips and tricks for improving adoption of data-science projects and results in your organization.

Karen Bellin VP, Data & Analytics Mirum Agency

WATERWORLD The Hunt for Automated Design Engineering

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, Digital Transformation Organica Water

Panel: Data analytics being the driver of your organisation

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

Karen Bellin VP, Data & Analytics Mirum Agency
Adam Tank Director, Digital Transformation Organica Water
Michelle Littlefield Program Lead, Digital Services & Analytics City of Redwood City

Morning Coffee

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

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 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.

Dr Alex Liu Chief Data Scientist IBM
Matthew Farkas Data Scientist Spotify
Rich Fox VP Data Science & Analytics Apex Parks

Lunch

City Planning + AI in Silicon Valley: Smart Cities, Startups and Minimum Viable Policies

Thoughtful integration of city planning, smart cities technology and policy development that puts people first is critical for governments and societies to adapt and thrive in an AI-powered world. As major cities around the globe continue to experience population growth, city governments will face increasing challenges to meet the needs of its communities. Advances in artificial intelligence are expected to replace millions of jobs and breakthrough technologies are being deployed faster than public policies, posing both incredible opportunities and unanticipated consequences on society. Breaking data silos across city departments and municipalities, cultivating a data-driven culture and creating a data-skilled workforce within government, as well as building strategic partnerships with the Govtech community, are key to successfully leveraging the full potential of data analytics to help to build a better foundation for the cities of the future.

Michelle Littlefield Program Lead, Digital Services & Analytics City of Redwood City

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?

Fred Ehlers CIO Norfolk Southern Corporation
Amitha Krishnappa Senior Manager, Analytics Walmart Labs
Danial Sabri Dashti Senior Machine Learning Scientist Amazon

Closing remarks from Chair

Alex Weber Award-winning host, motivational comedian speaker .