Day 1 - 28 November 2018
Big Data Strategies: Chairman’s Welcome
Chief Architect and General Manager of US Operations
Yixue Squirrel AI Learning
10:00AM - Day 1
Keynote: From Model to Implementations – Our approach to AI Adaptive Education
Adaptive learning, is an educational method which uses computer algorithm to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. Yixue Squirrel AI Learning (Yixue) combines the advanced AI technology with custom-build and calibrated learning contents to offer after-school tutoring and supplementary academic programs, through both online and connected learning centers, to K-12 students in China, the largest education market in the world.
Sr. Vice President
10:30AM - Day 1
Senior Data Analyst
10:30AM - Day 1
Chief Data Officer
Santa Clara County
10:30AM - Day 1
Panel: Creating a data-driven culture
- Establishing evidence-based decision making as a core part of the digital workplace
- Defining a clear strategy to leverage data whenever and wherever possible to enhance business efficiency and effectiveness.
- Putting big data at the heart of your business
- Closing the data literacy gap, hiring talent and getting buy in from the top.
Global Leader, Strategic Alliances & Talent Acquisition, Data Science & Informatics
11:45AM - Day 1
Establishing and developing a highly competitive Data Sciences team
- Getting the right skills balance – what new competencies are required?
- Developing the leadership skills to catalyse data sciences adoption across the organization.
- Employee engagement – approaches to boost retention of top data sciences talent.
Making Sense of Unstructured Data
Structured data only accounts for about 20 percent of stored information. The rest is unstructured data – includes texts, blogs, documents, photos, videos, etc. In this presentation, I will talk about *analytical methods and tools, to analyze unstructured data, *that data scientists may use to gather and analyze information that doesn’t have a pre-defined model or structure. Traditional analytical processes are not adequate to fully understand unstructured data an as such, I want to dwell on some of the newer methods such as semantic analysis and natural language processing to analyze unstructured data. I will talk about the best practices that has worked for me in my quest to untangle unstructured data as well as do shallow dives into Recurrent Neural Networks (RNN) and Convolutional Neural Networks and how deep learning is helping at identifying patterns in unstructured data.
Panel: Building an Effective Big Data Strategy
- What business problems are you trying to solve?
- Data governance – what data to use, what other sources are there?
- Bring together data, analytics, people and frontline tools to create business value
- Dealing with legacy
Beyond Big Data: Strategic Change Management Considerations
Why do so many companies struggle to incubate and activate an effective big data and advanced analytics organization? Much of the time, failure to capture value is due to inadequate change management strategies resulting from (1) improper project lifecycle, (2) lack of true data-centric decision making, and (3) inability of business and technical leaders to work in lockstep. I discuss these strategic considerations and more, tying various outcomes to case studies I have observed in industry.
Lessons learned on experimentation and decision making
Experimentation is difficult. Even when you successfully run an experiment, it can be a challenge to interpret the results. This talk will outline some of those challenges as told through anecdotes from my time at DoorDash.
- Should you run an A/B test, or is there another method that’s more effective?
- Do your results match your hypothesis?
- Do you understand the causal mechanism?
- How do you use what you’ve learned through experimentation to improve on the product?
Architecting a modern data pipeline framework to accelerate and scale your analytic applications
Big Data/ Analytic applications rely on data , most of time and resources of them were spent on bringing together existing data sources and when appropriate, transform , enrich them with other data sets. I will explore the architecture and design patterns for a modern data pipeline framework and share the observations of technologies assessment.
Solo: Scaling Business Insights on Over a Billion Connected Devices
Netflix users stream on a multitude of device platforms that have differing hardware and application characteristics. Characteristics such as operating system, hardware profiles, connected devices, and Netflix client versions can have significant effects on the user’s streaming experience. The Netflix Device History Service (DHS) is the scalable solution we built for tracking devices and their changing characteristics. This session will explain why DHS was needed to grow our business globally, go in depth on the architectural underpinnings, and describe the key analytic use cases DHS enables.