Rock Stars of Data Analytics Conference Agenda
Please see the Speakers page for bios.
Morning Session: 8:30 a.m. – 12:30 p.m.
Chief Scientist of Software Development
Data Infrastructure Engineering Director
Chief Data Science Officer
Netflix Former Director of Analytics
Panel Discussion: 1:30 p.m. – 2:30 p.m.
Joshua Greenbaum (moderator)
Enterprise Applications Consulting
Director of Analytics, Product Management, and Hadoop Strategy
Vice President, Business Analytics Solutions
Chief Information Officer Portfolio Management/Senior Director Application Development
Afternoon Session: 3:00 p.m. – 5:30 p.m.
Catalyst IT Services
Senior Vice President, Products
The Limits of Big Data
It is clear that the volume of data being amassed on individuals, on things that dwell at the edge of of the Internet, and on objects and processes that make up the fabric of the universe is beyond mortal comprehension. It would seem, therefore, that our only path to understanding is to employ the aid of our computational assistants, in whom we place our confidence and our trust. At times, that confidence and trust is well-earned: there are non-obvious insights that can only be discovered by tireless algorithms that intrinsically possess no human bias. However, it is also clear that our confidence and trust is, at times, intensely misplaced and misguided. In this spectrum between discovery and damage lies our responsibility to engage the engines of big data analytics in ways that contribute to the human spirit. This presentation examines that spectrum, discusses what is possible and what is not, and offers advice on what to do.
Scaling Self-Serve Analytics
LinkedIn has a diverse big data ecosystem based on Hadoop and relational databases, which supports a very large team of data scientists, analysts, and engineers to extract insights and build data products from massive amounts of data. These include derived data applications like PYMK (people you may know), recommendation products and analytical dashboards. In this talk, Greg will describe the challenges involved in building a self-serve analytics ecosystem by integrating storage and compute platforms, data acquisition and management, and reporting and visualization tools. He'll also share experiences dealing with complexities in data management such as data discovery, data lineage, and the tools we have built to address those.
Big Data, Physics, and the Industrial Internet: How Modeling & Analytics are Making the World Work Better
A new industrial revolution is coming, as information technology increasingly joins with human minds, machines and business processes, resulting in dramatic improvements in productivity, living standards, and efficient use of resources. This presentation describes what GE and its partners are doing to accelerate this revolution by combining traditional and emerging big data approaches with physics and engineering to improve how the world works.
New Challenges in Data Science: Geospatial Analysis
In this session, Lyft data science leader Chris Pouliot relates how he leveraged his experience working at Google and Netflix - for example, transitioning from calculating the probability that a user will play a movie based upon their viewing history to a more challenging problem at Lyft - for example, predicting car demand by Lyft users at a specific location in San Francisco. If this done accurately, it optimizes the system, benefiting both passengers and drivers.
How Far Can We Trust Big Data Analytics?
Like all monumental technological breakthroughs, Big Data Analytics has the power to be used for the greater good and for the not-so-greater good. The technology that can find new cures for cancer, create enormous efficiencies in business operations, or spot a terrorist before he strikes can also be used to gain illicit commercial advantage, taken down a power grid, or suppress basic human rights on a global scale. This panel will discuss the intersection of Big Data Analytics, business practices, social norms, and ethics with the goal of providing guidelines for meaningful action for business executives, developers, service providers, and users.
How Big Data is Reducing Workforce Turnover
This session discusses ways in which big data and analytics are being used to transform hiring and team assembly. Learn about platforms that have been focused on hiring and team assembly in healthcare, software development, and call centers, how those platforms operate, what their adoption challenges have been, and what outcomes they have achieved. With examples including data from hospital systems including Adventist Health, Loma Linda, and LifeBridge and software engineering efforts for enterprises including Red Hat, Nike, and Starwood, this session details the ins and outs of how data can be used to transform hiring, productivity, and quality of teams in various verticals.
Enabling Unstructured Information Analysis of Big Data
Unstructured data has always posed a series of unique challenges for traditional methods of information management. Traditional data analysis techniques are of limited utility when approaching social media sentiment problems, or in trying to analyze customer relationship narratives, or in analyzing message traffic. The result is unanalyzed troves of data that have high relevance to organizational performance. Dell's Kitenga group is focused on unlocking insights from big data by acquiring and enriching the data using intelligent processes that scale over distributed computing infrastructure. Enrichment leads, in turn, to new opportunities for data engagement through interactive examination of big data.
Deriving Operational Intelligence from Machine Data
Big data comes from machines. IT systems, and technology infrastructure—websites, applications, servers, networks, mobile devices, and the Internet of everything—generate massive amounts of machine data, which represents one of the fastest growing and most complex parts of big data. Unlike traditional structured data, machine data is highly diverse and dynamic, and is generated at very high velocity. During this talk, I will discuss the importance of collecting and analyzing machine data through a scalable search-based architecture. I will highlight several real-world examples to demonstrate how companies are deriving operational intelligence from machine data to mitigate cybersecurity risks, reduce operational cost, and deepen customer understanding.