NOVEMBER 6-7 | NEW YORK CITY
DATAx New York is a cross-industry event for business leaders, strategists, and practitioners looking for best practices and strategic insights to help increase business growth and gain marketplace advantage.
DATAx provides a unique blend of data-focused content tailored to help you find real-world solutions to common challenges.
Our program is specifically curated to examine the most relevant topics on the minds of data scientists and business decision-makers. With an emphasis on collaboration, DATAx is the event where the technical and strategic conversations that change business models are started.
2 DAYS | 4 TRACKS | DATAX LABS | UNLIMITED NETWORKING
Data has become the commodity of the future and the insights garnered from it are revolutionizing the business landscape and changing the way companies do business.
DATAx will be a unique interactive learning and networking experience, providing actionable takeaways to empower you to improve current processes and solutions and thoughtfully integrate new ones. Building a smarter, stronger, and more powerful tech-backed business model starts here.
The agenda is specifically curated to examine the most relevant topics on the minds of data scientists and business decision-makers, with an emphasis on collaboration and knowledge share.
Bring back actionable insight by participating in two days of candid talks and round tables.
Collaborate with peers in the interactive DATAx Labs. Choose from a series of workshops to fit your business needs and personal goals.
Connect with over 400+ data leaders across all industries and experience levels to expand your network.
Meet the disruptive startups and innovators who are changing the world.
Discover cutting-edge tools and techniques.
Immerse yourself in automation, data, and mobile technologies that will unleash your potential.
Every DATAx stage focuses on how to effectively use data to power business decisions. DATAx provides you with both the strategy and hands-on techniques that can be immediately implemented companywide and across industries.
STAGE 1 – DATA STRATEGY: DRIVING INSIGHTS AND RESULTS
The Data Strategy stage is an immersive thought leadership insights-driven program that fuses together data science and “real world” business strategies.
Designed for Chief Data and Analytics Officers and data champions, these thought-provoking sessions are curated to provide an executive overview of the changing data landscape.
Executives will discover how to effectively leverage data to drive business results.
STAGE 2 – AI/ML TECHNIQUES AND APPLICATIONS
Technical innovation is at heart of DATAx New York.
Join us for the AI/ML: Techniques and Applications stage as we bring together a cross-industry mix of technical leaders and practitioners who are well-versed in Artificial Intelligence and Machine Learning innovation. Discover how their techniques and applications are revolutionizing the way companies use data.
Through real-world case studies you’ll see first-hand how the world’s most innovative brands are using AI and ML to drive business growth and gain marketplace advantage.
STAGE 1 – DATA INSIGHTS: MARKETING AND CUSTOMER ANALYTICS
The Marketing & Customer Analytics stage brings together forward-thinking data and analytics professionals, marketers and product managers for a concentrated program that uses analytics to garner insights that inform key business decisions.
Learn how today’s bleeding-edge companies and innovative experts are using data to build trust and deliver a customized user experience.
Decode the needs of the customer with case studies on how analytics can help to better acquire users, personalize their experiences and build new innovative customer journeys that minimize your company costs and boost your revenue.
STAGE 2 – APPLICATIONS: BUILDING THE DATA PLATFORM
The Building the Data Platform stage provides data scientists with an inside-look at how data-driven companies are creating personalized tools to solve internal challenges.
See technical demonstrations of application and data models and learn how teams have effectively gained internal support and funding for these projects.
Getting value out of your data is more important now 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 and harnessing the power of data with AI and other exponential technologies. You will hear multiple case studies from our client successes and better understand how enterprises like yours are realizing the ‘inside-out’ potential of data-driven outcomes exploited through these exponential technologies at scale.
Cognitive Process Transformation and Data Leader
All retailers want to know their target buyer, but understanding the past and present of their interactions simply isn’t enough, predictive analytics is the next step; • How ML can enable price optimization, product placement and assortment selection • Using machine learning algorithms affectively for generating suggestions for substitute and complimentary items • Utilizing optimization algorithms to reduce store cost by optimizing replenishment cycle and safety stock • Scaling algorithms to generate recommendation for individual stores and monitor their performance
Principal Data Scientist
Organizational leaders are being bombarded 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 opportunity. The best solution for most organizations is to find someone on the inside with the knowledge and network and arm them with a data science center of excellence. In this session, we will share the journey we’ve undertaken at Mount Sinai to create our own DISCO floor, what types of services can we offer on a shoe string budget and how we can quickly deliver value to the business where they need it most – preparing the culture for change.
VP, Chief Data & Analytics Officer
Mount Sinai Health
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
VP, Advertising Analytics
The New York Times
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 seek to lay out possible organizational design considerations and some learnings from implementing data science teams, strategy for developing a data science capability and how Plated integrates data science and business strategic planning. Last, this talk 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.
VP, Data Science
SVP of Products
Developing an internal data science capability requires a cultural shift, a strategic mapping process that aligns with business objectives, a technical infrastructure that can host new processes, and team capability to alter business practices that create measurable efficacy. Join us to learn how to build opportunity maps that lead to hiring plans and infrastructure specification.
Chief Data Officer & Chief Science Officer
Applying machine learning in a business context isn’t always straightforward – you must often trade exactness for actionable results. In this talk, we’ll discuss practical considerations 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 learnings that can be applied across many industries in any business size, from start-up to large corporations.
Head of Data
Sit in on this thought-provoking discussion focused on the use of data analytics to inform the business and increase revenue. In today’s market place companies must find ways to disrupt themselves or create tools that will help them gain marketplace advantage. Learn how businesses are using data science to build new product offerings, improve service and capitalize on new marketing initiatives.
Moderator: Nidhi Gupta
VP Digital Channels and Senior Product Manager
Bank of America
Data Engineering Analyst
Harvard Business School Online
VP Head of NLP and Speech Capabilities Development
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. 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.
Director of Product
Deploying machine learning in production comes with a lot of challenges. For example, doing proper CI/CD for ML is quite hard. At Google Cloud, we’re actively working on providing a technical framework for MLOps which includes all aspects of operationalizing ML in production. In this talk, I will discuss the general concepts of MLOps and CI/CD as they apply to ML in production. I will do a deeper dive into some technical options for CI/CD with ML on GCP, using Kubeflow Pipelines as the reference platform.
Cloud Solutions Architect
Building systems that can scale and adapt to the ever-changing compute, storage and networking landscape is a major challenge in machine learning. Michael will 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 taking dynamic approaches to sensing, evaluation and data routing. He will show how these strategies are implemented in Lytx’s Distributed Machine Learning Platform, which continuously monitors 600,000 fleet vehicles deployed across the world.
Using several real-world examples, we illustrate how we used AI to transform the ERM function from an audit-centric role to a value-add function that proactively identifies and mitigates enterprise-wide risks.
Chief Analytics Officer
Head of Enterprise Risk Management
Global Head of Risk Strategy and Data Analytics
ransfer Learning for Model as a Service 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. We present transfer learning, as well as the steps to optimize it, as a potential solution for reducing investment in training and improved fungibility.
Senior Data Scientist
Software Engineer–Machine Intelligence Lab
General Manager of Machine Learning
To drive revenue, most businesses seek to provide value to their customers through targeted messaging at the right time and place. At the same time, today’s e-commerce landscape is changing at an unprecedented pace. The rise of new international markets, new platform and technologies, combined with constantly evolving user habits and preferences, means that businesses and Marketing units must constantly evaluate and adapt their strategies to remain competitive. Data Science and Engineering can play a key role in this space, and provides significant benefits when looking to operate, market and connect with customers. In this session we will dive into how to best structure a data science team focused on sales and marketing. We’ll discuss practical examples on how to drive real business value through the application of both simple and complex methods, and highlight the importance of applying traditional software engineering principles.
Head of Data Science
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. As Chief Data Scientist, Patrick Surry will present the guiding tenets for Hopper’s data science team, and how a data-driven culture internally translates to a better product for customers.
Chief Data Scientist
Data science teams often face major challenges in operationalizing models, building shared infrastructure, and on-boarding new members. In an effort to solve these issues, the data science team at WW (the new Weight Watchers) created an open source modeling and deployment framework, Primrose (Production In-Memory Solution), to help deploy production models and recommenders. We will discuss Primrose’s design decisions, and how they were made to reflect the technical needs of our organization. We will also dive into how a framework can facilitate the rapid growth of the team, and increase project velocities.
Manager, Data Science
WW (the new Weight Watchers)
Data and analytics professionals have to power to harness their talents to move the needle and influence social change that can impact humanity. Join us as learn how organizations like Viz for Social Good is partnering with your peers to use data in informatively visual ways to help other non-profit organizations effect change around the world. Be inspired to change the world.
Board Member, Viz for Social Good and Director, Business Intelligence CIBC
Viz for Social Good
This talk reflects on the design and architecture of an effective modern big data platform that can ingest, store, and serve 100+ PB of data with minute level latency. We’ll walk you through the typical workflow of a data scientist, data analysts, or ML user at Uber to explore data, discover desired datasets, access the data, run interactive queries, visualize the output, or prepare derived datasets for advanced analytics and machine learning use cases. The audience will leave the talk with greater insight into how things work in an extensible modern Big Data platform and will be inspired to re-envision their own data platform to make it more generic and flexible for their data scientists and analysts.
Engineering Manager, Hadoop Platform Lead
AI has become a cornerstone of digital transformation and is a powerful tool in the marketer’s arsenal. While many feel that the power of AI derives from the algorithms or techniques used to implement learning and modeling, the nature of the data used to train AI algorithms, specifically it’s quality, depth and breadth, impacts outcomes far more than the technique itself. This session will explain this in detail using common marketing and business use cases and real-world case studies. Attendees will increase their understanding of AI fundamentals and leave with knowledge that will positively impact their usage of third party data and their predictive modeling activities.
CEO & Founder
Data is only useful if humans can understand and apply it. In this talk, we will discuss how Airbnb incorporates data UX and design at the infrastructure level in order to democratize data access and comprehension by creating human-centered data experiences. This talk will help leaders understand how an interdisciplinary approach to data and data products can unlock business value. key themes : data dx, humanizing data, making the most of your data by leveraging design and ux research
Eli Brumbaugh and Marie Sbrocca
Design Leader – Data Platform and Research Lead – Data Platform
Technology shifts are forcing today’s enterprises across all verticals to rapidly move from data analytics to insights to proactive, predictive, and preventative actions. Tomorrow’s AI-infused intelligent enterprises will utilize data insights and machine learning in conjunction with foundational enablers like 5G and multi-access edge compute (MEC) to move to real-time processes by exploiting information and events the moment they occur. This talk will explore several use cases and a solutions-centric approach to enabling Real-time Enterprise (RTE).
VP, Big Data, AI, and Location Platforms
Case study – Samsung Electronics demand forecasting * Machine learning based demand forecast * The effect of accurate predictions * Strategies for difficult problems (e.g. shifting seasonality, cold start situation, promotional
Dr. Kimin Oh
Senior Data Scientist
CPG and Retail are in a unique position to leverage AI given the large amount of customer data available. To successfully navigate the path leading to a winning AI strategy, it is imperative that organizations define an 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. This journey is illustrated through a couple of examples, one showing success and the factors responsible, other showing a failure and its causes.
Audience segmentation has been the mainstay of ad targeting for quite some time. Your actions online are tracked and matched with patterns of others with the idea that similar people have similar tastes and propensities to click on an ad. At Vox Media, we are exploring a new way to boost ad performance by looking at the context of the article you are reading at the moment to better enhance ad performance.
Head of Data Science
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