September 26, 2019

1 World Trade Center

74th Floor 285 Fulton St New York, NY 10007

RegisterAbout

Learn how to make everyone more productive with data, from advanced practitioners to business users.

If data is only useful to highly-skilled data practitioners, it’s not living up to its original promise of making your organization smarter, faster, and more effective. Let’s fix that, together.

Join other executives like you for an afternoon of discussions on best practices, use cases, and cutting-edge tools around data practices. Learn from leaders in data who have engineered organizational change or implemented data practices at scale in an afternoon of presentations, and engage face-to-face with your peers at an evening reception.

RSVP today as spaces are limited. We look forward to seeing you!

data.world

 

data.world is the modern catalog for data and analysis. Choose data.world to wake up the hidden data workforce within your enterprise, multiply your data’s value, and create a data-driven culture faster. Bring together employees of all roles, backgrounds, and skills to work collaboratively using the tools they already love. data.world uses a knowledge graph to keep data connected to everything people need to find, understand, and use it. As a result, your data, analysis, and expertise become more discoverable, trustworthy, and reusable. Visit data.world to learn more.

Schedule

12:30 pm – 1:30 PM

Registration

 

1:30 pm – 2:00 PM

Precision – A Cross-Platform Affinity Model for Marketing Optimization

With a portfolio of brands ranging from Nickelodeon to Comedy Central, Viacom distributes and markets content that appeals to all demographics across the globe. Still, finding the right digital audiences for tune-in marketing is a challenge. In this session, Sergey will walk through how his team built Precision, a web application built for digital marketers at Viacom to create smarter audience targets, leveraging a panel-based dataset that combines digital content consumption with linear TV viewing.

Speaker Bio

Sergey Fogelson enjoys applying his quantitative skills to large-scale data intensive problems and mentoring junior colleagues. In his current role, he manages a team of data scientists responsible for measuring the quality of the assets available at a large media corporation. He also enjoys sharing and communicating what knowledge he has. To that end, he teaches data science courses, both in-person and online, at several bootcamps/hacker academies. In the past, Sergey built custom large scale data products for clients in the financial industry, led a team that developed data products at a small cybersecurity startup, and worked at a digital advertising startup. Sergey obtained his Ph.D. in Cognitive Neuroscience from Dartmouth.

Sergey Fogelson

VP Data Science & Modeling

Viacom

2:05 pm – 2:35 PM

The Oracle and the General: machine learning and optimization for automating decision-making

When scoping out data products, a common mistake teams make is to focus on the machine learning side, neglecting what the actual product should do. For instance, surfacing a limited number of recommendations that have to be not only “good”, but also sufficiently different from each other may be a totally different problem from a recommender where the user may be willing to scroll down a longer list, as long as the quality of most of the results are good enough. A given machine learning model may provide impressive results, but depending on the problem at hand, it may not actually provide information that is useful. A better way of designing data products is by separating the decision-making part of the product from obtaining the information that is used to make better decisions. Here, decision-making will usually take the form of some constrained optimization problem, and the role of machine learning is improving the information that feeds into this problem. In this talk, I will go over a few examples where seemingly subtle differences in product constraints or use cases result in different solutions, as well as provide the audience with tools to help formulate a product in terms of a constrained optimization problem.

Speaker Bio

Marianne is the Staff Data Scientist for operations at Bowery Farming, where she develops algorithms for robotics and supply chain to help Bowery grow delicious crop efficiently. She holds a Ph.D. in applied mathematics, and has worked as a data scientist for several startups in London and New York.

Marianne Hoogeveen

Staff Data Scientist

Bowery Farming

2:35 pm – 3:05 PM

Coffee Break

3:05 Pm – 3:35 PM

Data Journalism & The Future of Local News

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, The Associated Press will present a case study in how they have been able to leverage data journalism and a community of publishers – leveraging the infrastructure of data.world – to advance the power of fact-based journalism.

Speaker Bio

Ken is Director of Product at The Associated Press, driving new product development around emerging platforms and local media. He is proud to work alongside a talented team of journalists, helping to ensure their stories get delivered to the right audiences in the right way. He is particularly passionate about data journalism, voice interfaces and multimedia journalism.

Prior to AP, Ken was a Product Manager at The Nielsen Company where he managed social media analytics products.

Ken is a computer science graduate from Villanova University and is actively involved in organizations like NYC Media Lab and YearUp.

Ken Romano

Director of Product

Associated Press

3:40 pm – 4:10 PM

Beyond word clouds: Data Visualization of Guggenheim Modern Art books

A big part of the data and some may say, the most interesting data is unstructured text: news articles, blog posts, emails, Twitter posts, log files, and countless other publications. While in the last couple of years we have made amazing progress in algorithmic analysis and display of quantitative data, unfortunately there are few data visualizations that help us work with pure text besides the notorious work clouds. This talk will cover techniques for text analysis and more interesting strategies to visualize a document or a group of documents.

Speaker Bio

Anna Nicanorova is Director of Annalect Labs – space for experimentation and rapid software development within Annalect. The objective of the lab to build marketing software and algorithms based on the latest developments in tech as well as needs of Omnicom agencies. Anna is Co–Founder of Books+Whiskey meetup and volunteer coding teacher with ScriptEd (Science Skill Center High School). She holds an MBA from the University of Pennsylvania – The Wharton School and BA from Hogeschool van Utrecht. In her free time, she can be found art-hunting in museums or climbing very tall mountains.

Anna Nicanorova

VP Engineering

Annalect

4:15 PM – 5:00 PM

Data Literacy Panel Discussion

Moderator
Patrick McGarry, data.world, Head of Data Literacy

Panelists
Sophia Tee, Verizon – Principle Data Scientist
Roy Ben-Alta, Amazon – Head of WW Data & Analytics/ML and Robotics Practice
Ayan Bhattacharya, Deloitte Consulting – Advanced Analytics Specialist Leader
Nick Hart, Data Foundation – President

5:00 PM – 6:00 PM

Wrap-up/Networking

Register

When

September 26, 2019
12:30pm – 6:00pm

Where

1 World Trade Center
285 Fulton St, 74th Floor
New Yor, NY 10007

Contact Us