DATAx

10:45am – 11:15am

PANEL DISCUSSION: Preserving the Virtue of Your Data – (Track 1)

Do not assume that everyone in your company values data in the same way. Each department has its own unique relationship with data. Implementing clear processes around data is the only way to safeguard your company’s most valuable asset. In this session, topics of discussion will include ▪ Incorporating data governance into your business strategy ▪ Developing a culture of data citizens ▪ Processes for preserving data quality

10:45am – 11:15am

USE CASE: Demystifying Applied ML – Building Frameworks & Teams to Operationalize ML at scale – (Track 2)

Comprehending how to match the deep knowledge of a subject matter expert to the technical application of ML programs remains a major barrier to applied ML in the workplace. Join us as we examine best practices on how to scale operationalizing ML to solve complex problems. In this session, topics of discussion will include: ▪ Outlining your business Objectives: defining goals and success criteria. ▪ Providing the right technology and tools ▪ Structuring and training your teams

Dr. Nels Lindahl Director, Clinical Decision Systems
CVS Health

10:45am – 11:15am

USE CASE: How Strava is Using Data Visualization to Fuel Growth – (Track 3)

The Strava dataset grows by millions of activities a day and comprises trillions of GPS data points; this quantity of data is gold for businesses. Discover how user data at Strava is transformed into innovative visualizations of human performance: heatmaps, fitness stats, mobile routes, and even urban planning/mobility. Hear first-hand how they are able to scale visualizations, and detail how data sets are analyzed and transformed into business insights the company uses to fuel its growth. In this session, topics of discussion will include: ▪ How to effectively scale visualization ▪ Using Data Visualization for increase growth ▪ Extracting quality data from large datasets

Cathy Tanimura Senior Director, Analytics & Data Science
Strava

10:45am – 12:25pm

WORKSHOP: Moving from Raw to Relevance in Real Time – (Track 4)

Abstract soon to come! Hosted by Micro Focus

Theresa Melvin Chief Architect, AI-Driven Big Data Solutions
Hewlett Packard Enterprise

11:20am – 11:50am

Self Service Enablement – Empowering Data Discoveries Across the Organization – (Track 1)

Achieving the level of a data driven organization opens up a new world of possibilities for your organization. With a stable data governance strategy in place and a workforce fluent in data you are able to empower your workforce to drive data focused initiatives across the organization. In this session, topics of discussion will include: ▪ Defining your data governance strategy ▪ Establishing a level of data fluency within your organization ▪ Training on the tools and techniques required to effectively analyze data

11:20am – 11:50am

How to Effectively Accelerate ROI on Your AI Investments – (Track 2)

Every organization wants to use data as its core advantage, but struggle with scaling and accelerating their data initiatives. Many of the challenges can be attributed to the fact that the data, and its applications are advancing faster than the technologies used to process and design them. Join us as we dive into the root cause of many common data challenges and incorporate applied patterns to solve them. In this session, topics of discussion will include: ▪ How to design efficient data models that will enhance the performance of your AI/ML tools. ▪ How to successfully use applied patterns to address the root cause of your data challenges ▪ Real-World solutions to scale and accelerate AI/ML processes

Ronak Shah Head of Data Engineering
Coursera

11:20am – 11:50am

PANEL DISCUSSION: Dirty Data – Strategies for Improving the Quality of Your Data – (Track 3)

Are you confident in your organizations data? If the answer is NO, then you have a major problem. High quality data can increase business performance for your organization and validate key business decisions Learn best practices for protecting the quality of your data. In this session, topics of discussion will include: ▪ Increasing business performance ▪ Improving data quality ▪ Protecting our data

11:55am – 12:25pm

USE CASE: Business Strategies to Burst the Bias Bubble – (Track 1)

Collaborative decision making under uncertainty happens daily in business. Machine learning obscures bias behind the complexity of its algorithms. Bias is corrosive because it hides in metrics and the systems we use to make decisions every day. It gives us false confidence that our decisions are data driven when in reality, they are bias driven. Even data scientists often don’t understand the data and models well enough to prevent biased outcomes. Autonomous cars are 5% more likely to hit PoC because their training data wasn’t diverse enough. Amazon abandoned their automated candidate screening system because it was biased against protected classes. Join us as we dive into what you can do to control bias within your organization. In this session, topics of discussion will include: ▪ Creating a culture that strives to reduce bias ▪ Using Machine Learning systems to reduce uncertainty ▪ Reducing uncertainty around key decisions by controlling bias

Vin Vashishta Data Scientist, Strategist, Author
V2 Machine Learning Consulting

11:55am – 12:25pm

PANEL DISCUSSION: Powering Data with Cutting Edge Technology – (Track 2)

We all know that there is no shortage of data, but collecting, cleaning and extracting relevant insights is a big problem that many companies struggle with. In this session, topics of discussion will include: ▪ The future of the stack ▪ Budgeting for your company’s future. ▪ How to source the best technology for your business

11:55am – 12:25pm

Using Natural Language Processing to Obtain Valuable Insights – (Track 3)

Many industries are employing natural language processing techniques to aid with recognizing trends and industry predications. These techniques have enabled some companies to solve common business challenges and also reveal new opportunities for company growth. Join us to learn effective natural language processing approaches. In this session, topics of discussion will include: ▪ Organizing information according to context ▪ Preparing your data ▪ Predicting lifetime customer/user value

12:30pm – 1pm

AI + Humans Working Together to Drive Revenue – (Track 1)

When it comes to AI collaboration is key. The fear that humans will be replaced by the machines is still very prevalent in the workforce. The reality is AI is a tool and humans will always be needed to manage the tools. Data leaders must spearhead the change in mindset around the implementation of AI. Working together machines will provide the consistency and speed to uncover patterns in real-time and humans will provide the logic to extract insights that will help companies make better informed decisions. In this session, topics of discussion will include: Fear of the machines ▪ Scaling AI across an organization ▪ Upskilling your workforce

12:30pm – 1pm

PANEL DISCUSSION: Communicating Value to Your Internal Stakeholders – (Track 2)

The realm of data and AI is still very new and evolving daily. Practitioners are constantly challenged with staying abreast of new technology, regulations and how it all affects the company. In this session we will address effective ways to communicate the value of data & AI to internal stake holders. In this session, topics of discussion will include: ▪ Technology advances ▪ GDPR & CCPA and the effect on data ▪ Winning stakeholder buy-in

12:30pm – 1pm

Creating Game Changing Algorithms to Gain a Competitive Advantage – (Track 3)

Algorithms have allowed companies like Uber and Stitch Fix to disrupt industries and change the way consumers spend their time and money. Many data teams struggle to develop smart algorithms that deliver game changing actionable insights. Discover the methodology that goes into creating algorithms that can significantly impact your business. In this session, topics of discussion will include: ▪ Finding relevant data sources ▪ Testing and validation ▪ Identifying relevant business objectives

2pm – 2:30pm

USE CASE: Getting Your Data House in Order: Critical Issues in Advancing Data Governance – (Track 1)

Data is the asset of the present – and the future. Whether it’s generating insights, driving innovative product development or improving decision making, the need for and dependence on big data has never been greater. Data is a critical strategic asset for any company looking to leverage the power of AI, machine learning, and other advanced analytics. However, for companies that were not “data-first,” implementing a data governance program that both maximizes the quality of their data and addresses the obligations created by the changing regulatory landscape remains a challenge. This session will explore the critical legal and practical issues a business must consider as it develops and implement compliant and effective data programs. In this session, topics of discussion will include: ▪ How to structure a data governance program that addresses their current regulatory obligations to comply with new privacy and security obligations ▪ Best practices in creating a data-focused business culture ▪ Real-world examples of trends, challenges, opportunities and developments to come in data collection and use

Jessica B. Lee Partner, Cp-Chair, Privacy, Security & Data Innovations
Loeb & Loeb

2pm – 2:30pm

PANEL DISCUSSION: Is AI the Right Fit for Your Business? – (Track 2)

There is still a lack of understanding of how AI & ML should be used within organizations. Many companies have spent considerable amounts of money (blown budgets) on AI/ML technology and have not seen any ROI on these investments. Leaders are tasked with sourcing new technology, but they need to know that the technology is the best fit for the business. This session will help you manage your expectations around AI/ML and provide you with realistic goals for how AI/ML can fit your business objectives. In this session, topics of discussion will include: ▪ AI on a shoestring budget ▪ Sizing AI to fit your business ▪ Managing stakeholder expectations around AI

2pm – 2:30pm

The Value is in the Details – (Track 3)

When it comes to obtaining the best insights from your data the value is often hidden in the minor details. Data collection is only the beginning of the data journey; your data is useless if it is incomplete or queries aren’t structured properly. Learn the varies methods companies are using to uncover the valuable nuggets of information that their data holds. In this session, topics of discussion will include ▪ Importance of Data integrity ▪ How to effectively structure your data ▪ Best Practice for uncovering value in your data

2pm – 3:30pm

Data Visualization – (Track 4)

In this immersive workshop you will get hands on training on some of the latest tools in data visualization. Learn how to transform data into knowledge that is relevant for decision making. Topics of discussion will include: ▪ Visual Data Storytelling ▪ Which tools are most effective for your industry? ▪ Extracting the most relevant information to showcase

2:30pm – 3pm

USE CASE: A Guide to Building a Data Driven Culture – (Track 1)

When it comes down to it, organizations struggle with becoming data driven and making it a part of their culture. Hiring a team of data scientists does not fix this problem outright. Success is dependent upon building the right processes, democratizing data and resetting the mentality across the organization. These problems are often more difficult to solve than the tech and math related to data science. Join us in the discussion of a how to guide for building a data driven culture. In this session, topics of discussion will include: ▪ Understand the data maturity model and where to start. ▪ Get an overview of processes and people requirements to drive a data driven culture. ▪ Understand the citizen data scientist movement and the realistic expectations of people outside of data science

Daniel Gremmell Vice President, Data Science
Plated

2:30pm – 3pm

PANEL DISCUSSION: Deploying Machine Learning in the Wild – (Track 2)

Join in on this discussion as representatives from some of today’s leading-edge company’s share how they are developing and deploying ML applications with success. Hear the backstories behind the journey; pitfalls, abandoned projects, small wins and successful processes that worked. In this session, topics of discussion will include: ▪ Managing stakeholder’s expectations when developing machine learning ▪ Structuring your team for effective ML implementation ▪ Preparing your data for algorithm training – Do you know what problems you want to solve?

2:30pm – 3pm

USE CASE: The Intuition Behind Machine Learning in Marketing – (Track 3)

Since the year 2013 important breakthroughs and advances in technology have made it possible to run sophisticated predictive models capable of classifying images, text, and sound. Technology has brought to reality self-driving cars, chat bots and a host of other AI powered devices. In this session we will present key insights that will help you make AI/ML more beneficial to your marketing efforts. Through real world case studies, the session will demystify the core technologies around ML and illustrate how to successfully apply the technology. In this session, topics of discussion will include: ▪ How to think and interpret predictive models ▪ What metrics we use to evaluate models ▪ Specific case studies in optimization, channel attribution

Mario A. Vinasco Director BI and Analytics
Credit Sesame

3pm – 3:30pm

PANEL DISCUSSION: Systems Integration – Getting Your Systems to Work in Harmony – (Track 1)

As companies grow so does the technology used to support them; with that growth comes a myriad of systems implementation. Many companies are currently struggling with getting their systems to communication. This lack of communication has made it challenging to easily access all aspects of captured data. Discover real-world examples of how some companies are navigating this communication roadblock. In this session, topics of discussion will include: ▪ Navigating system integration ▪ Growth and technology ▪ Digital transformation

3pm – 3:30pm

USE CASE: Using ML and AI to Produce Real-Time, Scalable, Informed Decisions – (Track 2)

The ability of ML and AI to provide real-time, scalable decision-making support to executive strategy is changing the business landscape. In the healthcare sector, patient privacy, data interoperability, and high stakes patient health ramifications intertwine to create a challenging environment in which to use advanced computation techniques. Join us to learn how Johns Hopkins Healthcare’s model, Callisto has been instrumental in predicting suitable health management programs for THEIR patients that deliver concrete return on the overall health of the patient. In this session, topics of discussion will include: ▪ The unique challenges within the healthcare sector that other industries can benefit by understanding ▪ How the runaway cost structures can be fundamentally inverted by targeting pain points with focused ML/AI ▪ How advancements in AI has allowed the healthcare sector to leapfrog into the 21st century as a fully-integrated player in the cyber community

Romy Hussain Director of Healthcare Economics and Data Science
Johns Hopkins Healthcare

3pm – 3:30pm

USE CASE: Streaming (Real Time) Analytics – (Track 3)

Join us as we unbox how TD Banks is taking advantage of streaming analytics in real time. Through real world case studies, you’ll learn how they are using this quick time sensitive process as a competitive edge. In this session, topics of discussion will include: ▪ Five Levels of Streaming Analytics Maturity ▪ Advantages and Disadvantages of streaming analytics in real time ▪ Supportive open source technology

Bharti Bhardwaj Delivery Director
TD Bank

10:45am – 11:15am

EXECUTIVE ROUNDTABLE DISCUSSIONS: Leadership Strategies for Today and Beyond – (Track 1)

These roundtable sessions provide a great opportunity for data leaders to exchange ideas around common industry challenges. These intimate facilitated off the record table discussions give leaders the opportunity to problem solve with peers and discuss strategies around implementation and the future of data and AI. In this session, topics of discussion will include: ▪ Exchange of ideas around how you and your fellow industry peers are implementing data strategy within your organizations ▪ Exchange of ideas around current data challenges ▪ Exchange of ideas around leadership and team design

10:45am – 11:15am

PANEL DISCUSSION: Ethical Risks in AI – (Track 2)

There are many factors that can bring into question the ethics of your data programs, such as bias, inconsistency, security, and transparency to name a few. The existence of these risks affects whether your AI systems should be trusted. Join us for a conversation around ethics and the role it plays in your AI strategy. In this session, topics of discussion will include: ▪ Defining an AI strategy ▪ How to effectively build trust in your data ▪ Bias in algorithms

10:45am – 11:15am

USE CASE: Augmenting Customer Experience with ML in eCommerce – (Track 3)

The eCommerce industry has been completely transformed by the advancements in AI and ML. The ability to recommend and personalize products have allowed the industry to grow rapidly. With this growth the ability to effectively harness data from digital platforms have become increasingly challenging. In this session, topics of discussion will include: ▪ The advantages and disadvantages of personalized vs. non-personalized recommendations ▪ Challenges involved with harnessing data for customer recommendations on digital platforms ▪ Primary considerations when deciding to implement a product recommendation engine

Asma Farooq Machine Learning Product Lead
Ebay

10:45am – 12:45pm

A Practical Introduction to Natural Language Processing – (Track 4)

A practical introduction to (NLP) Natural Language Processing is becoming an essential skill for organizations to gain a competitive edge. By enabling services like chatbots, text-based decision support systems, semantic analysis, threat detection and question answering systems NLP tools are being used to help people get insights from unstructured data and operate more effectively. For this reason, it has become an essential tool for many new business functions. In this immersive workshop you’ll be introduced to NLP techniques and tools with their real-world applications. In this session, topics of discussion will include: ▪ Learn and understand popular NLP approaches with interactive examples ▪ Understand how to clean and handle unstructured text data ▪ Complete NLP tasks including text classification, sentiment analysis, text clustering, topic modeling and semantic visualizations

Carlo Lipizzi PhD, Graduate Engineering Management & Systems Analytics Program Director School of Systems and Enterprises
Stevens Institute of Technology

11:20am – 11:50am

PANEL DISCUSSION: Structuring Data Teams to Maximize Impact – (Track 1)

A strong data team affords you the ability to effectively solve for business problems and leverage the rich resources within data to influence your company’s direction for the future. Lean in on this discuss with Data Science leaders as they share their struggles and successes in organizing data science teams. In this session, topics of discussion will include: ▪ How to best organize data teams within an organization ▪ The key to hiring and retaining top notch talent ▪ Team fluency around financial and business metrics

Meghan Anzelc Head of Data & Analytics
Spencer Stuart

11:20am – 11:50am

ASK THE EXPERTS ROUNDTABLE DISCUSSIONS: Data & AI Wellness Check – (Track 2)

These intimate solutions focused roundtables offer you a great opportunity to check in with industry leaders about the state of y our data and AI health. These off the record discussions are great ways to get guidance for your most pressing questions around your data and AI journey. In this session, topics of discussion will include:  Current data and AI challenges you and your in dustry peers are fa cing  Sourcing AI for your business needs ▪ How to incorporate AI/ML in to the your organization

11:20am – 11:50am

USE CASE: How Tencent Games is Successfully Shaping Business Strategies & Maximizing Income – (Track 3)

At Tencent Games, all decisions are data driven. Data mining techniques are applied to huge data sets collected from 200+ games to provide end to end big data analytic solutions. In this session , you will learn how they apply various predictive modeling techniques throughout the customer life cycle to improve user experience and optimize marketing. They will present use cases on new user acquisition, user monetization, user retenti on , churn pre dictions, and item recommendation In this session, topics of discussion will include:  How big data transforms the gaming industry  Data science solutions at scale throughout user life cycle  How to choose an appropriate algorithm for a sp ecific problem

Qiaolin Chen Director of Data Science
Tencent Games

11:55am – 12:25pm

USE CASE: Making Data Science – Scoping, Sizing, Hiring & Retaining an Internal Data Science Capability – (Track 1)

Developing an internal data science capability requires a major cultural shift and a strategic mapping process which align your business objectives. In order to be effective, you need technical infrastructure that support new processes and organizational structure that can alter business practices that create measurable efficacy. In this session, topics of discussion will include: ▪ Opportunities for data science to identify and prioritize value for your organization ▪ Recruiting, retaining, and goal setting for team members ▪ Budgeting and hiring for business objectives

Haftan Eckholdt Chief Data Officer & Chief Science Officer
Understood.org

11:55am – 12:25pm

USE CASE: Measuring Incremental Value of a Business Using Machine Learning Algorithms – (Track 2)

Wayfair launched the CastleGate program, a leading drop-ship operation in 2014, helping 300+ suppliers boost sales by delivering products to customers faster, safer, and at a lower cost than ever before. As they expanded their supplier partnership footprint in CastleGate, it was critical for them to quantify incremental dollar impact of entering a product in the program. This translated into a challenge for their analytics team due to the absence of historical experimental design. Join us for this session as they share the journey of this project. In this session, topics of discussion will include: ▪ The importance of showing incremental dollar impact ▪ How to push forward in the absence of historical information ▪ How to realistically boost sales

Ankit Mangal Associate Director, Web Analytics
Wayfair

11:55am – 12:25pm

PANEL DISCUSSION: How to Creatively Convey Your Data Discoveries to Non-Technical Stakeholders – (Track 3)

Gaining support from internal stakeholders is the life or death of many initiatives. Often, projects are rejected because stakeholders don’t have a clear understanding of its possibilities. Discover effective ways to use data visualization to influence stakeholder buy-in. In this session, topics of discussion will include: ▪ Using data visualization as a selling tool ▪ How to win over non-technical stakeholders ▪ Data Visualization tools

12:30pm – 1pm

Leading with Data – Creating a Business Strategy Where Data is King – (Track 1)

In order to identify how data can be instrumental in solving key business priorities you much first clarify the business drivers where data can deliver value. Your strategy should clearly demonstrate data’s ROI. As data continues to be imbedded in all industries the only way to maintain relevance within your industry is to create a business strategy lead by data. In this session, topics of discussion will include: ▪ Where do your data decisions originate top down or bottom up? ▪ Where are you in your data journey? ▪ Clarifying business objectives

12:30pm – 1pm

USE CASE: Managing Bias in Predictive Models – (Track 2)

AI-based approaches to prediction are often seen as an unbiased decision-making process. However, algorithms have frequently been found to have biased predictions. Whether due to statistical issues in data collection or labeling, or because of human bias in training data, these issues can impact individuals at a tremendous scale. In this session, we will highlight examples of biased predictive algorithms and cover primary sources of bias in the development of predictive models. We will also discuss best practices that can be used to assess for, and hopefully limit, bias in predictive models. In this session, topics of discussion will include: ▪ Understand the limitations of predictive models and the risk for bias in AI ▪ Describe key risks in data or approach to model development that can lead to bias in predictive algorithms ▪ Develop internal methods to assess for and limit

Wade Schulz, MD, PhD Assistant Professor of Laboratory Medicine & Computational Healthcare Researcher
Yale University School of Medicine

12:30pm – 1pm

PANEL DISCUSSION: How to Achieve a Seamless Digital First Experience – (Track 3)

In today’s digital marketplace the majority of the digital audience are anonymous. This makes it extremely difficult for many customer facing organizations to get a good understanding of its customer base. Find out how some of the biggest brands are curating effortless digital experiences that is allowing them to attract and retain customers at high rates. In this session, topics of discussion will include: ▪ Collecting data around the digital experience ▪ Best practicing in digital analytics ▪ Creating a seamless digital experience

2pm – 2:30pm

USE CASE: “Data for Good” an Essential Building block for a Healthy Data Culture – (Track 1)

The “Data for Good” movement is increasingly becoming a critical component to many organization’s corporate social responsibility initiative. The benefits of this movement include improved employee engagement, increased brand visibility and greater community engagement. Viz for Social Good is one of the many organizations whose mission is to help non-profit organizations understand their data through attention-grabbing and informative visuals. Discover how to harness the talents of data and analytics specialists to impact social change. In this session, topics of discussion will include: ▪ Benefits of incorporating Data for Good into your organization to improve data culture ▪ How to use your expertise and talents to impact humanity ▪ Defining Data for Good and methods of engagement within your organization

Vanitha Lucas Board Member
Viz for Social Good

2pm – 2:30pm

PANEL DISCUSSION: Human Centered AI – (Track 2)

Join us for an in-depth look at how machines are taking lessons from their human counterparts. Discover how machines are becoming more humanized in order to be effective at finding the best insights. In this session, topics of discussion will include: ▪ Human-centered design ▪ Human behavior patterns ▪ Effective implementation of emerging technology

2pm – 2:30pm

USE CASE: Application of Image Based KNN and Anomaly Detection to Product Type Classification – (Track 3)

Application of Image Based KNN and Anomaly Detection to Product Type Classification In e-commerce product catalog, product types have a significant influence on customer journey starting from product discovery. Image-based classification approaches do not have high enough precision by itself to be used in production. On the other hand, text-based classification models are challenged by noisy input data, flattened product type hierarchy, and certain kitchen sink product types. By combining image based KNN with anomaly detection, the speaker will demonstrate how to detect product type errors in an unsupervised manner. In this session, topics of discussion will include: ▪ How to use anomaly detection to validate golden data set ▪ How to optimize recall/precision of image based KNN ▪ How to build end-to-end misclassification detection pipeline for large scale catalog

Binwei Yang, PhD Distinguished Engineer, Merchant Technology Data Science,
Walmart Labs

2pm – 3pm

WORKSHOP D: The Neural Network Playground – (Track 4)

In this hands-on workshop attendees will have an opportunity to build their very own neural network. We’ve created a safe space for you to play around and test your skills and theories. Whether it’s your first time or you are a pro; I’m sure you’ll gain great insights from your conference colleagues. In this session, topics of discussion will include: ▪ Popular tools for building networks ▪ Best practices in data science and infrastructure ▪ Effective uses of neural networks

2:30pm – 3pm

PANEL DISCUSSION: Creating a Data Science Center of Excellence – (Track 1)

There are so many aspects that go into creating a data science CoE. Funding, who will spearhead the effort and building the best teams. In this session you’ll hear how companies are stepping up to the challenge of taking charge of their data. In this session, topics of discussion will include: ▪ Funding your Data Science CoE ▪ Data Culture ▪ Curating a team of unicorns

2:30pm – 3pm

USE CASE: Developing Data Science to Add Value – (Track 3)

The goal of every data science team is to make a significant impact on the business. Teams work hard building some amazing and extremely useful tools that never see the light of day and their potential impact never realized. Join us as we uncover how to organize the development process to keep engagement high and demonstrate added value along the way. In this session, topics of discussion will include: ▪ Obstacles faced by data scientists their data projects ▪ How to restructure development, get accurate feedback and demonstrate value ▪ How to keep stakeholders involved during project journey. From exploration to implementation

Morgan Cundiff Data Scientist
ShopRunner