Chairperson Opening Remarks
How the Chief Data Office can Drive Corporate Transformation
As new forms of technology proliferate, businesses are not only required to manage data at unprecedented rates, but scale understanding across corporate environments with trust at the core. There is a nearly universal directive to have data power everything and improve customer experiences. At AT&T, becoming a data-driven organization required the formation of the Chief Data Office (CDO), combining data supply chain, big data and automation functions. Steve Stine, AT&T’s first CDO, will share how he and his team use data to drive business transformation, uncovering data insights, developing technological advancements and evolving employee skillsets.
Do Organizations need CDOs?
Using data and analytics at Visa
As the amount of data around us increases, so does the number of ways companies can use it to better meet their customers’ needs and to improve business operations. Using more than 140 billion Visa transactions per year, we work with merchants, banks and partners to help them understand their customers better in order to drive loyalty and growth. During this session, we will walk through examples of how we have worked with clients to date; we will also discuss how we are providing insightful data and smarter analytics to members of the payments eco-system to help them better meet their consumers’ needs.
Bringing the benefits of big data to medicine.
With the advent of big data and data analytics technologies, we have seen innovations in almost every field imaginable. Medicine, however, has lagged behind. There are a number of reasons why data technologies have yet to make their mark on the way we do health care. First and foremost are the regulatory concerns. The HIPAA law protects patient information and compliance presents a barrier-to-entry unique to the field. Additionally, companies already on the inside of hospitals and compliance such as those that provide the electronic health record, and medical devices have very different communications protocols, market shares, and willingness to integrate new technologies. Potrero is pioneering predictive health care through our smart system which autonomously measures a variety of relevant vitals in real time, allowing us to perform analytics and impact patient outcomes, finally bringing the benefits of big data to medicine.
Building Enterprise Analytical Capabilities from the Ground up – Challenges and Opportunities for CDOs
Many organizations are struggling with putting together the right organizational structure, technologies and processes to support the growing need for analytics with few good examples available to follow. Chief Data Officers are to define and implement strategies to support these needs. However, CDOs are often inherit unwieldy organizational structures and siloes that are make their task complicated, stressful and, most importantly, rendering them less effective. We will briefly describe the journey UCLA’s CDO took over the past five years to build state-of-the-art analytical capabilities from the ground up. We will also discuss lessons learned, key success factors, and how CDOs can ovoid some of the not-so-apparent mistakes.
Self-Service Analytics has Arrived – But for Who?
In their bid to digitally transform and compete in today’s economy, the average enterprise has increased spending on data & analytics technology to $14M to make data-driven decision a reality. Yet while vendors claim self service capabilities, adoption rates hover at an abysmal 22%. Why? Because according to Gartner “no analytics vendor is full addressing both enterprises' IT-driven requirements and business users' requirements for ease of use.” Business users don’t have the time or inclination to learn a complex, IT-approved analytics tool.This session will focus on why advancements in search & AI-driven analytics are driving the “Third Wave of Analytics,” eliminating the need for technical training while equipping every businessperson with the ability to analyze data quickly and efficiently.
Case Study: Making a profound impact using data
PANEL: Creating a data-driven culture
Panel featuring: · PJ Abhishek, SVP, Revenue Management & Consumer Analytics, Wyndham Worldwide · Anil Earla, Head of Information and Data Analytics, Global IS, Visa · Debarag Banerjee, VP & Head of Data Science, Myntra
Privacy as an ally
The smartest businesses see privacy as an ally, not a roadblock. Good Privacy enables data, supports business differentiation, and drives decisions. Discover how a well-planned privacy strategy can add value across your business, help you understand your business and your data, and serve your clients in new dimensions.
The Perfect CDO
The Perfect CDO excels in business acumen, technology wizardry, and ability to drive change. We describe the evolution of the CDO role: from the early days to how it is today. In addition to the description of the Perfect CDO, which we can all aspire to, we also describe "The Perfect Company for this Perfect CDO" and "The Perfect Company for Data
Data science in legacy orgs
Turning CDO challenges into opportunities for success
Networking Drinks Reception
Registration & Breakfast
Chairperson Opening Remarks
Data and AI in Hollywood: from Script to Execution
Movie studios face a complex landscape. AI won’t make movies better any time soon, but can help make them more successful. Although AI research for digital content has exploded, there is a big research gap for offline transactions in general, and theatrical movies in particular (e.g., which AI models to use, how to use the models, etc). In our recent work we explore deep models to help organize content & market data, and surface it in a language that is useful for content production. Downstream, we explore how deep models can ‘plug’ into tech & media platforms to create adaptive campaigns to drive box office outcomes.
Herding cat pictures, data and insights from the world's largest online community
Reddit is home to 1.1M online communities which cover every topic imaginable and generate more than 5 TB of data per day. For years that data went untouched in a dark and isolated cloud warehouse. No more! Now, with the recent formation of Reddit's data science team, we can unlock that treasure trove of data to solve unanswered mysteries, gain new insights, and delve into the very nature of humanity itself.
Applied AI & the fake news problem
In this talk we explore real world use case applications for natural language understanding with deep learning. We will review a case study for automated “Fake News” evaluation using contemporary deep learning article vectorization and tagging. Technical material will review several modern methodologies for article vectorization with classification pipelines. We close with a discussion on troubleshooting and performance optimization when consolidating and evaluating these various techniques on active data sets.
Fireside chat: Becoming data-driven
From flat files to deconstructed database: the evolution and future of the Big Data ecosystem
Over the past ten years the Big Data infrastructure has evolved from flat files lying down in a distributed file system to a more efficient ecosystem and is turning into a fully deconstructed database. With Hadoop, we started from a system that was good at looking for a needle in a haystack using snowplows. We had a lot of horsepower and scalability but lacked the subtlety and efficiency of relational databases. Since Hadoop provided the ultimate flexibility compared to the more constrained and rigid RDBMSes we didn’t mind and plowed through with confidence. Machine Learning, Recommendations, Matching, Abuse detection and in general data driven products require a more flexible infrastructure. Over time we started applying everything that had been known to the Database world for decades to this new environment. They told us loud enough how Hadoop was a huge step backwards. And they were right in some way. The key difference was the flexibility of it all. There are many highly integrated components in a relational database and decoupling them took some time. Today we see the emergence of key components (optimizer, columnar storage, in-memory representation, table abstraction, batch and streaming execution) as standards that provide the glue between the options available to process, analyze and learn from our data.We’ve been deconstructing the tightly integrated Relational database into flexible reusable open source components. Storage, compute, multi-tenancy, batch or streaming execution are all decoupled and can be modified independently to fit every use case.This talk will go over key open source components of the Big Data ecosystem (including Apache Calcite, Parquet, Arrow, Avro, Kafka, Batch and Streaming systems) and will describe how they all relate to each other and make our Big Data ecosystem more of a database and less of a file system. Parquet is the columnar data layout to optimize data at rest for querying. Arrow is the in-memory representation for maximum throughput execution and overhead-free data exchange. Calcite is the optimizer to make the most of our infrastructure capabilities. We’ll discuss the emerging components that are still missing or haven’t become standard yet to fully materialize the transformation to an extremely flexible database that lets you innovate with your data.
Leading a data team at Pinterest
In this talk, Yongsheng will cover the unified big data and machine learning platform at Pinterest to enable every single engineer to be able to derive trustworthy, actionable insights and apply machine learning to solve complex problems with ease and confidence. Yongsheng will go over the key components in this platform and how they together enable engineers at Pinterest to move fast with high quality to solve a wide array of problems using machine learning. He will also talk about some of the future evolution of this platform in this talk.
Analytics for the 4th Industrial Revolution
At Hitachi’s Center for Social Innovation, we have been using Machine learning and Artificial Intelligence to develop cutting edge solutions and push the envelope in the area of Industrial IoT. These problems range from increasing operational efficiencies, reducing costs to creating new AI enabled products and services. Drawing on this rich experience, we will present a systematic taxonomy of industrial analytics problems. We will walk through the different problem areas and give examples how AI/ML is being used as a tool to address these problems. We will conclude by pointing out new research directions, and exciting new developments in the area of industrial analytics.
Chief Analytics and Data Office at Blue Cross Blue Shield of Louisiana (BCBSLA)
Chief Analytics and Data Office (CADO) was established at BCBSLA to provide the capabilities needed to drive our strategic objectives aimed at enhancing health, affordability and member experience. The CADO provides core functions such as data governance and access to foundational assets that serve as a ‘single source of truth’. Big data and artificial intelligence-based approaches are being developed and utilized to predict the risk of future health related events (e.g. risk of hospitalization) together with recommendations for preventive actions. Within the context of Accountable Care Organizations (ACOs), we are combining data-driven actionable insights with financial incentives to our network providers for coordinating care, improving health outcomes and enhancing quality of care.