Skip to navigation Skip to main content

Day 1

Light Breakfast and Registration

Craig Fryar - Chairperson Overview

Data and Intelligence - Powering Decisions and Applications Through Insights

Ambient intelligence and ubiquitous data are the fuel catapulting us into a brave new world. The lines between virtual and reality blur. Assets become fungible. Hierarchies dissolve, new roles and professions emerge. Humans are continuously connected, not just to each other. And we will see democratization of everything, everywhere. We are at the beginning of a digital transformation journey with a material impact on the world as we know it. Technology is not a limiting factor: If its not available today it will likely be crowdsourced tomorrow. You, at the center of this change, need to decide whether you'll be victim or hero. Let's figure out how to navigate this whitespace 

Gahl Berkooz Chief Analytics Officer Acorns

Transforming IoT Using Dynamical Machine Learning

IoT is transforming manufactured objects into services; Machine Learning has played a key role in realizing this transformation. New in-stream, closed-loop applications in Industrial IoT are forcing us to develop new approaches to IoT Data Science where machine learning that can adapt to variations over time becomes essential. Dynamical ML is the key to continuous learning, adapting to “new normal” as machines age as well as providing a “Digital Twin” leading to continuous “closed-loop” performance improvement. 

PG Madhavan Principal Data & Analytics Scientist General Electric

Power to the People: Why Search is the Next Frontier for Modern BI

2 billion humans use search every day to book flights, trade stocks, apply for jobs and more. But when we come into work, we have to wait days or even weeks to get the insights we need. 

Sierra Borghi Regional Account Director ThoughtSpot

Evolution of Enterprise “Big Data” Warehouse

My talk will focus on how Big Data technologies have helped evolve Data and BI solutions from Predefined models, Dashboards and Reports to Exploratory and Self Service Analytics. This has resulted in realizing our abilities for unstructured data optimization - add new data, see new patterns, ask new questions.

Prashanthi Paty Head, Data and Personalization GoDaddy.com

Featured Start-up 5 minute Introduction - Alation

Morning Coffee Break - Exhibition Area

A use-case driven approach to Data Analytics Infrastructure and Cloud Deployment Strategies

Data Analytics has matured from BI into a wide variety of use cases leveraging machine learning, Analytics-of-Things, real-time analytics etc. while consumption models have matured from on-prem to cloud to hybrid. A “one-size-fits-all” Infrastructure strategy will not suffice as data volumes and use cases grow. In this talk, we will begin with the maturing data analytics use-cases across industries. We will look at key workload requirements for new data analytics platforms. We will then discuss the Infrastructure/ Hybrid Cloud strategies for each of these workloads that can economically scale to PBs of data while ensuring regulatory compliance and operational efficiency. 

Sai Devulapalli Head of Data Analytics Line-of-Business Dell

Jobs, not clusters - Google's approach to Big Data

Taking months to provision more resources for Hadoop? A bad query takes down your Hive cluster? YARN resource manager doesn't solve all your management problems? Difficult democratizing data access within the organization? In this session Tino discusses how Google solves these and other classic Hadoop problems through the notion of job-scoped workloads.

Tino Tereshko Big Data Lead, Office of CTO Google Cloud

Expert-Support Data Science

Most high-profile data science wins are deployed as fully automated systems.  Consider a recommendation engine.  Once deployed, there is no human involved, meaning there is no step in the process for a human to manually replace the algorithmic recommendation with an intuitively-derived alternative.  Despite the successes of fully-automated systems, corporations and governments are spending considerable resources to build data science systems to support, rather than replace, human decision making. 
In this talk, I will discuss fundamental differences between fully automated and expert-support data science, and best practices around planning, staffing, and proving ROI. 

Jay Yonamine Head of Data Science, Global Patents Google

Lunch

Putting AI to work

The AI revolution caught many people by surprise. With rapidly improving AI accuracy and sophistication, the applications appear to be limitless. However, getting started with AI is not an easy task. In this talk, we are going to look at the most common AI use cases which are getting implemented by leading enterprises right now. We are also going to discuss the best way to get started with your own AI efforts.

Alex Ermolaev Head of AI NVIDIA

Data Science Platforms: Your Key to Actionable Analytics

The number of inefficiencies in the data science workflow is staggering. From the number of disparate tools, to the challenges with deploying models into production, data scientists are simply not set up for success. To combat these inefficiencies, a new category has emerged-data science platforms. In this talk, we will discuss the key components of a data science platform and showcase how they are enabling organizations to realize the potential of their data science teams.

Ian Swanson Founder & CEO DataScience.com

The Role of the Chief Data Officer In Product Innovation

Knowing what Product to invest in, develop, market, as well as knowing who to sell it to doesn't happen by accident.  It happens with all things 'Data'.   In this presentation, we will take a look at the role of a Chief Data Officer from a 'Product First' perspective.  

Ann Kennedy Chief Product Officer ShareThis

Machine Learning Lifecycle Management or How to Make Your Models Self-Aware

Offering a first-rate experience to the online shopper requires the combination of many complex and inter-dependent machine learning algorithms. In the age of big data and shortage of tech talent, a robust automated model management system is key to empowering data scientists and engineers alike to spend their time on the development of new algorithms rather than on the maintenance of models already in production. This talk covers the architecture and components of a system able to programmatically identify and diagnose model weaknesses, catch potential problems in real-time and automatically expire and retrain models without any human intervention.

Jennifer Prendki Principal Data Scientist Walmart

Afternoon Coffee Break - Exhibition Area

Building a Data Advantage

Behavior change is hard, and to date, even the most effective in-person programs include little (or no) personalization.

Gian Gonzaga Chief Data Officer Earnest

Why You, Your Partners and Your Customers Don't Make Data Driven Decisions

Companies and organizations talk a lot about how data can empower their employees, partners, and customers to make good decisions. However, lot's of recent research suggests in cognitive biases and behavioral economics suggest that people often don't make rational decisions. This talk will look at some of the most common of these biases and how you can counter (or even use them!) to good effect.

Sheridan Hitchens Vice President, Data Products Ten-X.com

Using Privacy by Design to enable Data Strategy

Privacy by Design (PbD) is a strategic approach to project engineering that embeds privacy into early-stage development to better support product design and data use, and to minimize compliance issues down the road. Learn how to enable your business’s data strategy through PbD: understanding what data you have, where it is, and why; aligning data collection with customer expectations; reducing risk to that data; building products that have privacy 'baked in'; and providing business differentiation between you and your competitors. 

Sharon Anolik Strategic Privacy Advisor U.S. Department of Homeland Security

Networking Reception - Exhibition Area

Day 2

Light Breakfast and Registration

Craig Fryar - Chairperson Overview

How to recruit, build and deploy global end-to-end data teams that perform

In this session, we start by examining both technological and human elements of the end-to-end data pipeline using modern data architecture. We will follow the introduction by decomposing the key elements that develop and sustain successful data teams in a global environment.The presentation then proceeds to highlight the need for different skill sets, management styles and unique micro skill-sets needed to succeed in different phases and stages of data value creation pipeline. We not only touch on employee-contractor mix, but also examine a real-life successful internship program, that laid the foundation for a successful big data platform in the cloud.We conclude by discussing how to manage the team for ‘here and now’ vs. future trends in a global setting, while also doing an in-depth examination of the pros and cons of data team’s deployment models and its impact on the overall enterprise.

Siamak Amirghodsi Vice President, Data Management & Analytics OCC

Driving Insights and Innovation Through Data

A passionate, visionary leader in the data space driving insight and innovation, having built teams from the ground up and with a proven capability in strategy and execution. Offer over two decades of experience with a core focus on Strategy, Governance, Transformation, Data, Digital, Program and Risk.

Ali Bouhouch CTO & VP, Enterprise Architecture Sephora

How Research Universities Leverage Data for Mission, Value and Partnership

Research universities sit in unique positions to think about data as a way to drive value and public benefit.  At the University of California with i10 research campuses, $5B in sponsored research, more than 200,000 students, more than 14M patient records and generating 5 new inventions per day – data represents both a necessity and opportunity across the public university’s broad mission.  Hear about the strategies being pursued and how they are leveraged across domains to bring data into value.

Tom Andriola Vice President & CIO University of California, San Francisco

Building Data Science: Transitioning Companies to Data Driven Methodology

Scott Sokoloff, Chief Data Officer at OrderUp, shares his experiences and insights in how to create a data driven culture. Told through a lens of use cases and personal insights; Sokoloff, engages with the audience to tailor this keynote to the specific questions of those in attendance. Key takeaways from the session are “Know Your Business Objectives,”  “Invest in Internal Resources” & “Build the Infrastructure for Success” Audience involvement is encouraged and everyone is asked to bring any use case or practical concerns they may have in transitioning their company towards a data driven culture.

Scott Sokoloff Chief Data Officer Newsela

Morning Coffee Break - Exhibition Area

How Data Can Be Used For Strategic Advantage

Jeremy's goal is to help organizations understand the value of their data, how to turn that data into information, and how that information can be used to gain a strategic advantage.

Jeremy Steinhauer VP, Data Science IOTAS

Deliver Analytics Insights To Enhance Customer Experience

Delivering a superior customer experience doesn’t happen overnight; it starts with building analytics tools and technologies, data integrations, trainings and improved processes. Tushar will present how her team set foundation for customer experience analytics and insights and now utilizes data from various sources including traditional digital analytics, customer feedback, session replay, server logs to build comprehensive picture of end-to-end customer experience. Her team also conducts experiments to test various hypothesis to find best solutions.

Tushar Shanbhag Head of Data Products LinkedIn

Tips for Building a Successful Data Strategy

A panel centered around sharing of knowledge from global leaders in building and implementing an effective data strategy into an organization. Panelists will share their perspectives on tricks, tips, culture, and philosophy when implementing a strategy.

Panel Session Industry Experts Big Data Panel Session

Lunch

End of Summit