DATAx NEW YORK - Argyle Executive Forum Events
November 06 - 07, 2019
November 6 - 7, 2019





Day 1 Wednesday 6th















7:20am – 8:20am


8:20am – 8:30am

Opening Welcome

Paul Price CEO Argyle Group

8:30am – 9am

CDO KEYNOTE PANEL DISCUSSION: Implementing an Effective Business-wide Data Strategy

Join us for a thought-provoking discussion with some of today’s leading Chief Data Officers. We’ll discuss the thought process behind creating effective data strategies and hear about the business challenges that keep them up at night searching for solutions. Learn first-hand the trials and triumphs that come with the responsibility of being a Chief Data Officers.

Moderator: Daniel Gremmell Vice President, Data Science Plated

9am – 9:30am

GENERAL SESSION: The Future of the Data Stack: How to be Successful in Your Own Transformation

As more companies look to move their data infrastructure to the cloud, supporting developer-friendly processes like DevOps and diverse, agile workloads requires modernizing existing infrastructure that can keep up with ever-changing business requirements. Should companies lift and shift quickly, or take the time to transform existing infrastructure while they can? The answer is not black and white, and will depend not only on technology decisions, but also the business capabilities they enable. In this session we will deep dive into a discuss of the future of the data stack and what it takes to successfully migrate your business data to the cloud.

Tim Sale Big Data & Analytics Cloud Consultant Google Cloud

9:30am – 10am

ROUNDTABLE SESSION: Are you Doing the Right Thing: Overcoming the Streetlight Effect in Models, Teams, and Organizations

It is a dark night on a town street, and someone is searching for their lost keys under the only streetlight… because that is the only place they can see. This kind of observer bias is about doing things because they are familiar and convenient. It happens all the time, everywhere, without any effort. It’s the job of a data scientist to improve decision making. Essential to improved decision making is showing what is not getting done: opportunity. At the most granular level, this means identifying who models do not serve, and building additional models for them. In this hands-on session you’ll work in groups to see what solutions you can come up with when you think way outside the box.

Haftan Eckholdt Chief Data Officer & Chief Science Officer

10am – 10:30am


10:30am – 10:35am

Chairperson Welcome – (Track 1)

Bruce Tyler Cognitive Process Transformation and Data Leader IBM Services

10:30am – 10:35am

Chairperson Welcome – (Track 2)

Michael Phillippi Vice President, Technology Lytx

10:35am – 11:05am

Fueled by Data & Exponential Technologies – The Cognitive Enterprise – (Track 1)

Getting value out of your data is more important than ever before. The digital era requires you to react in real time with model-based tools on an enriched and structured data environment. As the business need for data insights increases – particularly as we witness the rise and proliferation of the Cognitive Enterprise – many organizations are using their curated data to power new intelligent workflows, harnessing the power of data with AI and other intelligent automation technologies. In this session, you will hear multiple Use Cases from IBM’s client successes, and in turn, better understand how enterprises are realizing the ‘inside-out’ potential of data-driven outcomes exploited through these exponential technologies at scale.

Bruce Tyler Cognitive Process Transformation & Data Leader IBM

10:35am – 11:05am

USE CASE: Going Beyond Big Data: Taking ML to the Next Level – (Track 2)

All retailers want to know their target buyers better. However, understanding the past and present of their interactions simply isn’t enough these days and predictive analytics is the next step to better understanding their customers. In this session, topics of discussion will include, yet will not be limited to: • How ML can enable price optimization, product placement and assortment selection • Using machine learning algorithms effectively for generating suggestions for substitute and complimentary items • Utilizing optimization algorithms to reduce store costs by optimizing replenishment cycle and safety stock • Scaling algorithms to generate recommendation for individual stores and to monitor their performance

Hamza Farooq Principal Data Scientist Walmart Labs, Walmart

11:05am – 11:25am

THOUGHT LEADERSHIP: Maximize the Value of Your Data: Transform Your Business – (Track 1)

Sophisticated analytics and machine learning solutions are critical for all businesses looking to compete. These solutions depend on seamless access to all data, independent of data types or data location. Join Oracle thought leader, Robert Dutcher to learn how you can empower your business with a next generation information platform. Discover how business analysts, data scientists and developers can have self- service access to data and analytics, delivering new value with a solution that’s future proof for business innovation.

Robert Dutcher Vice President Oracle Cloud

11:05am – 11:25am

Using AI & ML to Anticipate Patient Needs – (Track 2)

Join us to learn how Memorial Sloan Kettering is using Machine Learning to better understand and anticipate the needs of their patients. Cancer treatment is an immensely complicated and difficult process that can span years. Given this complexity, there is a great risk of missing certain patient needs, leaving the care team to react to a problem that could have been avoided. Discover how Memorial Sloan Kettering is using the knowledge garnered from ML to design innovative clinical programs.

Isaac Wagner Director, Stategy Analytics Memorial Sloan Kettering Cancer Center

11:25am – 11:55am

USE CASE: Designing a Data Science Center of Excellence (CoE) – (Track 1)

Organizational leaders are being flooded with AI and all the hype that goes with it. Many are throwing their hands up out of frustration while others are chasing the wrong projects, wasting time & money, and most notably, missed opportunities. The best solution for most organizations is to find someone on the inside with the knowledge and network to arm them with a data science CoE. In this session, Mount Sinai will share the journey they’ve undertaken to create their our DISCO floor, the type of services they’ve offered on a shoe string budget and they quickly delivered value to the business where it needed it most – as well as prepared for future, with a culture for change built into their strategies.

Michael Berger Vice President, Chief Data & Analytics Officer Mount Sinai Health

11:25am – 11:55am

USE CASE: How The New York Times is Transforming the Business with Machine Learning – (Track 2)

Advertisers tend to focus on finding a given type of person (based on third party data) and targeting them wherever they can find them. We argue that targeting them based on what they are reading and reacting to in the moment can be far more accurate and performative. Over the past two years, NYT has created award-winning ML-based contextual targeting methodologies that take our best asset (our content) and use what we know about it to redefine targeting in a way that’s safe and accurate

Kendell Timmers Vice President, Advertising Analytics The New York Times Company

11:55am – 12:15pm

THOUGHT LEADERSHIP: ModelOps: The Key to Conquering Analytics’ Last Mile – (Track 1)

It’s one thing to develop analytical models. It’s quite another to deploy them. In fact, research shows more than 50% of models developed are never deployed. But with ModelOps, all your models pay dividends. By applying innovative data science techniques and operationalizing analytics, organizations streamline all stages of the model building process – from development to deployment and beyond. Plus, it supports a larger community of analytic practitioners and scales to ensure model integrity as variables change. In this session, you’ll learn how to adopt ModelOps to cross the last mile of analytics.

Sarah Gates Product Marketing Manager SAS

11:55am – 12:15pm

An Organization’s AI/ML Journey- From Experimentation to Implementation – (Track 2)

CPG and retailers are in a unique position to leverage AI given the large amount of customer data readily available to them. However, to successfully navigate the path leading to a winning AI strategy, it’s imperative that organizations define their overall solution approach that includes AI & ML. While rapid experimentation and “fast fail” are the building blocks for long term success in AI, a healthy dose of pragmatism and expectation management are the keys to successful implementation and business value realization from organization-wide AI initiatives. In this session, you will learn about Colgate Palmolive’s AI & ML journey through several Use Cases, one showing success and the factors responsible, other showing failure and their causes.

Rahul Tyagi Former Director Analytics Colgate Palmolive

12:15pm – 1pm

Overcoming Organizational Challenges In Data Science – (Track 1)

Designing an efficient data science team in a business is often complex. There are many decisions and tradeoffs – and resources are limited. This session will address the possible organizational design considerations and some key learnings from implementing data science teams, strategies for developing a data science capability and how to integrates data science and business strategic planning. Addtionally, this session will explore multiple tools, frameworks and techniques outside of a data scientist’s core model creation skill set that allows them to navigate and have a larger impact in a business setting. Key Takeaways: • Increased understanding of various organizational design strategies and their trade-offs • Integration of business strategic planning and data science strategy and how they influence each other • How to identify technical tools and techniques to enable you to be a more effective data scientist in a business setting

Daniel Gremmell Vice President, Data Science Plated

12:15pm – 1pm

PANEL DISCUSSION: Governing Mass Data – Deploying AI in the Highly-Regulated Health Science Environment – (Track 2)

The use of Artificial Intelligence is growing within the healthcare industry and with the increase of this technology comes the need to maintain privacy. Join us for a conversation on how the highly regulated industry of healthcare is navigating the way data is being handled.

Moderator: Bartt Charles Kellerman CEO Global Capital Management

1pm – 2pm


2pm – 2:30pm

A History of Making Data Science: AIG, Amazon & Albertsons – (Track 1)

Developing an internal data science capability requires a cultural shift, strategic mapping process that aligns with business objectives, technical infrastructure that can host new processes, and team capability to alter business practices that create measurable efficacy. Attend this session to learn how to build opportunity maps that lead to hiring plans and infrastructure specification. Key Takeaways: • How to build an opportunity map for data science • How to hire a data science team based on the prioritized opportunity map • How to develop a data science team culture and practice for retention and risk management

Haftan Eckholdt Chief Data Officer & Chief Science Officer

2pm – 2:30pm

Applying Practical Data Science – (Track 2)

Applying machine learning in a business context isn’t always straightforward – you must often trade exactness for actionable results. In this session, Trailspark will discuss the practical considerations that must be involved in model building and deployment. Grasp an understanding of when to be flexible with assumptions, when it’s appropriate to deviate from the textbook, the importance of empathizing with your stakeholders, and leveraging your team to deliver optimal results. Discover actionable practices that can be applied across many industries and within any business size, from start-up to large corporations.

Ilan Man Head of Data Trialspark

2:30pm – 3pm

Human Side of Data – The Application of Design for Data Products – (Track 1)

Data is the thread that connects strategic planning to execution. However, many companies are trapped in the traditional mind set of complexed reports and measurements the are removed from most day to day activates. Whereas innovative company have moved to a point were data is key corporate assets and analytics have become humanized products. With a few basic design steps, everyone company can begin to create the ability to use data to see market dynamic more clearly, understand cause and effect, uncover new white space, create a better relationship with their customer, and transform their business.

Mark Montgomery Global Head, Digital Analytics and Business Insights GSK

2:30pm – 3pm

USE CASE: How the Associated Press Is Utilizing Data Science to Improve Local News Delivery – (Track 2)

Local news is at a crossroads. According to a study by Poynter, trust is up, but so are layoffs and the number of communities without local news coverage. If that’s the case, then how can cash-strapped publishers continue to create journalism that will inform neighborhoods and effect change in their towns and cities? In this session, we’ll look at ways the Associated Press utilizes machine learning to produce more data-driven journalism that publishers can use to tell the stories of their communities. Specific examples will include journalism that drove policy and legislative change, as well as tips and lessons learned on how to blend the power of data with the judgment of humans.

Ken Romano Director of Product Associated Press

3pm – 3:30pm

PANEL DISCUSSION: Using the Power of Data to Drive Business Growth – (Track 1)

In today’s marketplace companies must find ways to disrupt themselves and tap into the tools that will help them gain marketplace advantage. In this thought-provoking session, the discussion will focus on how data analytics can provide powerful business intelligence to an organization and increase its revenue. You will learn how businesses are using data science to build new product offerings, improve their services, and capitalize on new marketing initiatives.

Moderator: Ilan Man Head of Data Trialspark

3pm – 3:30pm

Scalable & Agile Machine Learning Strategies – (Track 2)

Building systems that can scale and adapt to the ever-changing compute, storage and networking landscape is a major challenge in machine learning. In this session, you will learn how to define strategies for creating long-lasting machine learning technologies, including optimizing computing power and costs, using capabilities on the edge and in the Cloud, and dynamic approaches to sensing, evaluation and data routing. You will also be shown how these strategies are implemented, which continuously monitors 600,000 fleet vehicles deployed across the world.

Michael Phillippi Vice President, Technology Lytx

3:30pm – 3:50pm

How Hopper Creates Data-Driven Customer Value – (Track 1)

Hopper’s core value proposition is using data to help customers make smarter travel purchase decisions. This includes airfare predictions, as well as alternative travel recommendations and other data-driven advice. This makes data science central to Hopper’s success. In this session, you will learn about Hopper’s guiding tenets for their data science team, and how a data-driven culture internally translates to a better product for its customers. Key Takeaways: • Innovating to deliver customer value with data • Nurturing a data-driven culture at your company • Delivering powerful data-driven insight in partnership with business, product and engineering teams

Patrick Surry Chief Data Scientist Hopper

3:30pm – 3:50pm

THOUGHT LEADERSHIP: Scale your Data Science Practice with Automation – (Track 2)

A 2019 research report by VentureBeat showed that over 87% of data science projects never make it into production. While there are a number of reasons for this high failure rate, lack of resources is a key issue. In fact, a 2018 report by LinkedIn, showed a shortfall of over 150,000 people with Data Science skills in the US alone. Join dotData VP of Data Science, Aaron Cheng as he discusses the state of data science and its impact on AI and Machine Learning, and how automation can help organizations scale their data science practices – ranging from accelerating feature creation to optimizing machine learning models and pushing them into production.

Aaron Cheng Vice President of Data Science and Solutions dotData

3:50pm – 4:20pm


4:20pm – 4:50pm

PANEL DISCUSSION: Utilizing AI to Transform Enterprise Risk Management – (Track 1)

Using several real-world examples, the session panelists will illustrate how they use AI to transform the ERM function from an audit-centric role to a value-add function that proactively identifies and mitigates enterprise-wide risks. Audience Takeaways: • Data, compute and talent required to successfully transform ERM using AI • Scaling learnings across the enterprise • Benefits of an ERM-focused AI Center of Excellence for enterprise

Aziz Lookman Chief Analytics Officer Rational AI

4:20pm – 5pm

USE CASE: How Nasdaq Transfers Learning for Model as a Service – (Track 2)

Training Machine learning models is a rather expensive task which requires resources – e.g. training data, gpu’s and the technical expertise – to ensure the performance of the model matches the product requirements or industry standards. Once the training is complete the investment may then be suitable only for a unique use case. In this session, you will learn how the Nasdaq transfers learning, as well as the steps they implemented to optimize it, as a potential solution for reducing investment in training and improved fungibility.

Anand Dwivedi Senior Data Scientist Nasdaq

5pm – 6pm

Kick-Off Party and Speaker Meet & Greet

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