June 10 - 11, 2020

DATAx

JUNE 10-11 | SAN FRANCISCO, CA

2 DAYS | 4 TRACKS | WORKSHOPS | UNLIMITED NETWORKING

DATAx San Francisco 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.

Team discounts are available :

3-4 People: 30% off

5+ people: 50% off 

Email enquiry@argyleforum.com for details. [REGISTER NOW]

EARLY BIRD TICKETS EXPIRE IN:

2020 DATAx SPEAKERS

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What to Expect at DATAx SF 2020

Conference Topics

Strategy and Leadership, Artificial Intelligence, Machine Learning, Deep Learning, Structured and Unstructured Data, Data Driven Culture, Data Governance, Computer Vision, Data Visualization, Natural Language Processing, Data Personalization, Neural Networks, Cryptographic Algorithms, Blockchain, IoT, Customer and Marketing Analytics, Robotics

Interested in speaking? Please submit your complete abstract to Show Producer Sherry Robinson srobinson@argyleforum.com.

Audience Details

Our audience will be 400+ business leaders, strategists, and practitioners looking for insights, collaboration, and solutions on how to utilize data to make informed business decisions.

Insights

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.

Collaboration

Connect with 400+ data leaders across all industries and experience levels to expand your network.

Meet the disruptive startups and innovators who are changing the world.

Solutions

Discover cutting-edge tools and techniques.

Immerse yourself in automation, data, and mobile technologies that will unleash your potential.

DATAx Tracks

TRACK 1: Strategy & Leadership in the Age of Data Dominance

The use of data is reshaping the way companies are doing business. In the age of data dominance business leaders must ensure that data is at the forefront of any business decision.  Is your company’s leadership equipped with a data focused strategy plan?  Discover how data leaders are defining data governance, establishing data fluency and best practices across their organization.

 

TRACK 2: Data Science & Artificial Intelligence Working Together

Artificial Intelligence and data are like PB&J perfect together.  When AI & data combine amazing insights are discovered. In this track you’ll hear how leading-edge companies are using AI powered data to solve business challenges and make better data informed business decisions.

TRACK 3:  Data Utilization: The Art of Extracting Valuable Insights

Data collection is only the beginning of the data journey. Extracting insights is where the real value exists. Join us to learn the methods companies are using to uncover valuable information within their data.

TRACK 4 – Workshops

Agenda

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Day 1 Wednesday 10th

 

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Starting at 7am

7:30am – 8:30am

Networking Breakfast

Starting at 8am

8:30am – 8:45am

Opening Remarks

8:45am – 9:15am

Does Your Data Team Play Well with Others?

How data interacts with other business functions varies from company to company. Organizational structure plays a significant role on the impact data can have on your organization. As titles change and evolve so must the roles and responsibilities to suite changes in business goals. Hear first-hand how leading Chief Data Officers are structuring strong data teams. In this session, topics of discussion will include: ▪ Organizational structure ▪ Evolution of roles and responsibilities ▪ How data science works across functions

Starting at 9am

9:15am – 10am

KEYNOTE ADDRESS: How Data is Influencing Change

The pace of change in the world is breathtaking and leading this change is DATA. How are you charting a path to ensure that data and technology works for us rather than against us? Data Scientists have the power to effect major changes, but first they must adopt new approaches. Join us to learn first-hand how to use data to influence change.

DJ Patil Former U.S. Chief Data Scientist – Head of Technology
Devoted Health

Starting at 10am

10am – 10:45am

Morning Networking Break & Expo Round Robin Challenge

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: Exploring the Race to Capitalize on Untapped Big Data Reserves to Develop Smarter, Data-driven Decisions – (Track 4)

AI solution reliance on modern socio-economic trends is one of the most provocative factors for data-driven analytics. Businesses are relying on insights extracted from Big Data, substantiating data as the new form of currency. This paradigm shift has corporations racing to capitalize on their untapped big data reserves to develop smarter, data-driven decisions capable of improving every business aspect. The complexity of moving to data-driven frameworks is often underestimated. HPE’s AI-driven Full Stack Data Science program works strategically to develop custom, high performing, end-to-end, real-time analytics solutions focused on facilitating client’s success making intelligent, data-driven decisions from their amassed data In this session, topics of discussion will include: • Learn how AI-Driven Full Stack Data Science pipelines power the Extreme-scale Market • Explore the real costs associated with Free and Open Source Software • Understand how Commercial Products offset many development complexities Hosted by Micro Focus

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

Starting at 11am

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

USE CASE: Explainable Artificial Intelligence (XAI) – (Track 2)

Deep learning algorithms have achieved high performance accuracy in many complex domains. Due to the nested non-linear structure of deep learning algorithms, these highly successful models are usually applied in a black-box manner, i.e., no information is provided about what exactly causes them to arrive at their predictions Explainable Artificial Intelligence (XAI) creates interpretable models while maintaining a high level of learning performance, thereby enabling users to understand, appropriately trust the underlying models. The session will provide a comprehensive overview of XAI concepts along with three use case demonstrations across biomedical, natural language processing and security applications. In this session, topics of discussion will include: • Creating interpretable models while maintaining high accuracy • How to leverage explainability to increase trust in model decisions? • How XAI enhances understanding of “black box” models & feature engineering?

Sherin Mathews Senior Data Scientist
Mcafee

11:55am – 12:25pm

Augment, Don’t Automate: Drawing Insights from Customer Feedback Using Natural Language Processing – (Track 3)

Companies are frequently faced with large amounts of unstructured text data, like forum comments or product reviews. Important trends can emerge in these datasets, but it can be time-consuming to read through comments, and keyword matching frequently misses critical nuances. We’ll discuss how we’ve approached this problem at Google using Natural Language Processing, with examples of the approach applied to open datasets. We’ll explore how this fits into the ML project lifecycle, with examples of common pitfalls. Finally, we’ll highlight how to use this technology as part of a “human in the loop” approach to supercharge your existing team members.

Peter Grabowski Software Engineering Manager
Google

Starting at 12pm

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

USE CASE: Using Machine Learning to Get Personal – (Track 2)

Learn how GSN Games uses machine learning to integrate behavioral data, survey data and psychographic data to generate game-play personas for their players. These personas are used to help inform and validate product features and in-game experiences, to make their games more fun and rewarding for players In this session, topics of discussion will include: • Data goes beyond behavioral actions • Customers have different aspirations and motivations when they engage with products • A successful product is keenly informed by customer aspirations

Andy Veluswami Vice-President of Data Engineering & Data Science
GSN Games (a division of Sony Pictures Entertainment)

12:30pm – 1pm

Effective Algorithms to Gain a Competitive Advantage – (Track 3)

The right algorithms allow companies to disrupt industries and change the way consumers spend their time and money. Just hiring a data science team is not enough to develop effective algorithms. Sometimes, data science teams struggle to find the right data for the problem, struggle to solve the problem well enough, or struggle to implement the solution. In this session, topics of discussion will include: • Finding the right problems & Setting the right, realistic objectives • Testing and validating results • Engaging stakeholders to use the algorithms & customers to understand and trust the algorithms

Elizabeth (Betsy) Barton Director of Data Science
InHome Delivery, Walmart

Starting at 1pm

1pm – 2pm

Networking Lunch

Starting at 2pm

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

USE CASE: How to Save your Company from Drowning in Unimpactful Insights – (Track 3)

With many organizations now relying on a multitude of data sources to inform their decision-making, the expectation has now been set on stakeholders and leaders to depend on, and act on, a perpetually growing technology stack and volume of data. Despite the competitive advantage that clean data can bring to an organization, there also exists a real risk of drowning in an abundance of interesting, but ultimately unimpactful, data and insights. In this talk, we will go through two detailed use-cases of how Getty Images combined Data Science and business needs to build a comprehensive suite of data-based frameworks and products to track, monitor and alert on e-commerce performance, and to help understand how customers consume our vast collection of imagery content. In this session, topics of discussion will include: • The importance of building business-agnostic frameworks and applications • How to understand customer behavior using NLP and unsupervised learning • How to leverage time series modeling to move from a reactive to proactive state when monitoring e-commerce performance

Thomas Vincent Head of Data Science
Getty Images

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?

Elizabeth (Betsy) Barton Director of Data Science
InHome Delivery Walmart

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

Starting at 3pm

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

3:30pm – 4pm

Afternoon Networking Break

Starting at 4pm

4pm – 4:30pm

USE CASE: Measuring the Success of Artificial Intelligence and Machine Learning

Both the producers and consumers of AI and ML recognize the importance of linking data insights to successful business initiatives. However, many organizations fail to close the gap between the technical teams who produce complex data products and the non-technical business leaders who need to make decisions to achieve business success. In this session I will walk you through case stories that will address the good and the bad in evaluating the success of data-driven decision-making. In this session, topics of discussion will include: • Engaging business leaders in Data Fluency • Building a culture of Business Fluency among data leaders and technical teams • Defining a strategy for measuring the success of AI and ML programs

Jeremy Welland, PhD Global Head of Data & Analytics
PayPal

4:30pm – 5pm

PANEL DISCUSSION: Addressing Bias in AI: How “Woke” Are Your Algorithms?

Data leaders are acutely aware of bias in data collection and they are wrestling with effective ways to ensure that their data processes are bias free. Join us for a conversation about bias and how leaders are navigating this major data quality issue. In this session, topics of discussion will include: ▪ Reducing bias in machine learning ▪ How to handle explainability ▪ Data governance

Starting at 5pm

5pm – 6pm

Networking Cocktail Reception

Who attends our events

We bring the world’s data community together

Location

San Francisco Marriott Marquis

Venue & Hotel Block Info

SAN FRANCISCO MARRIOTT MARQUIS | 780 Mission Street San Francisco, CA 94103

CONFERENCE VENUE

Whether you are in town for business or leisure, San Francisco Marriott Marquis welcomes travelers to Northern California with exceptional service, spacious hotel rooms and suites and a prime downtown location. From our towering hotel, guests have easy access to numerous attractions, including Oracle Park, the Moscone Center and the sights of SoMa. Inside, you can relax and recharge in contemporary rooms and suites that enjoy impressive views of the city.

HOTEL BLOCK

We have negotiated a discount rate of $359 per night for DATAx attendees at the San Francisco Marriott Marquis. You can book in the event room block using this link.

 

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