Rock Stars of Data Analytics Conference Agenda

 

Please see the Speakers page for bios.

Morning Session: 8:30 a.m. – 11:55 a.m.

The Limits of Big Data

Grady Booch

Grady Booch

Chief Scientist of Software Development
IBM

Scaling Self-Serve Analytics

Greg Arnold

Greg Arnold

Data Infrastructure Engineering Director
LinkedIn

Big Data, Physics, and the Industrial Internet: How Modeling & Analytics are Making the World Work Better

Matthew Denesuk

Matthew Denesuk

Chief Data Science Officer
GE Software

New Challenges in Data Science: Geospatial Analysis

Chris Pouliot

Chris Pouliot

VP,Lyft
Netflix Former Director of Analytics

Lunch 11:55 a.m. - 1:10 p.m.

Lunch Roundtables: 11:55 a.m. - 1:10 p.m.

Dave Jackson

David B. Jackson

Founder and CTO
Adaptive Computing

 

Case Study: Solving Big Data Challenges to Accelerate Insights

Adaptive Computing's customers are the real Rock Stars of Big Data. Adaptive Computing is the leading supplier of workload management software and powers many of the world's largest private/hybrid cloud and technical computing environments with its award-winning Moab optimization and scheduling middleware software. Moab enables its users to perform intense simulations and Big Data analysis more rapidly, accurately and cost-effectively with its technical computing, cloud and big data solutions for Big Workflow applications. Moab gives users a competitive advantage, inspiring the business to pursue game-changing endeavors. During our presentation, Adaptive will share three case studies from medium to large enterprise in the agro science, geospatial and healthcare industries. Adaptive will show you how these customers solved their challenges with dynamic scheduling, provisioning and management of multi-step/multi-application services across HPC, cloud and big data environments to accelerate insights.

Peter Hoopes

 

Peter Hoopes

VP and GM BIRT Analytics Division
Actuate Corp.

Ask. Discover. Act. Big Data Analytics at the Speed of Thought.

The true value of Big Data only comes after you extract valuable insights and relevant answers to your business questions. You need a platform that can perform complex analytics on enterprise data, visualize results and without slowing down systems, interfering with governance needs and relying on IT support. Actuate combines a columnar database technology with pre-built algorithms and gives companies an analytical sandbox to play with their Big Data and discover hidden answers to their business questions. – Advanced Analytics in Real Time

 
 

Panel Discussion: 1:10 p.m. – 2:00 p.m.

How Far Can We Trust Big Data Analytics?

Stuart Williams

Stuart Williams (moderator)

Vice President of Research
Technology Business Research Inc.

 

Mike Ames

Mike Ames

Director of Analytics, Product Management, and Hadoop Strategy
SAS

 

Dan McClary

Dan McClary

Principal Product Manager for Big Data and Hadoop
Oracle 

 

Afternoon Session: 2:00 p.m. – 5:25 p.m.

Deriving Value from Complex Data

Satyam Priyadarshy

Satyam Priyadarshy

Chief Data Scientist
Halliburton

 

How Big Data is Reducing Workforce Turnover

Michael Rosenbaum

Michael Rosenbaum

CEO
Catalyst IT Services

Enabling Unstructured Information Analysis of Big Data

Mark Davis

Mark Davis

Distinguished Engineer
Dell

Deriving Operational Intelligence from Machine Data

Guido Schroeder

Guido Schroeder

Senior Vice President, Products
Splunk

 

 

Networking Reception: 5:25 p.m. – 7:00 p.m.

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, take 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.

Deriving Value from Complex Data

While many industries are having challenges understanding what big data is, the oil and gas industry has thrived well on multiple dimensions of big data, namely, volume, velocity, and variety. The upstream O&G includes exploration and production and has led by leveraging science and first principles. However, the time is right for it to leverage the massive amounts of data from disparate sources to discover new insights that will help improve performance, reduce and predict risks, and innovate in areas of exploration, drilling operations, and reservoir management. In this presentation, Satyam Priyadarshy will touch upon the complexities involved and challenges associated in deriving value from the data. He will briefly mention some successes that show promising signs of leveraging the Big Data Ecosystem.

Case Study: Solving Big Data Challenges to Accelerate Insights

Adaptive Computing's customers are the real Rockstars of Big Data. Adaptive Computing is the leading supplier of workload management software and powers many of the world's largest private/hybrid cloud and technical computing environments with its award-winning Moab optimization and scheduling middleware software. Moab enables its users to perform intense simulations and Big Data analysis more rapidly, accurately and cost-effectively with its technical computing, cloud and big data solutions for Big Workflow applications. Moab gives users a competitive advantage, inspiring the business to pursue game-changing endeavors. During our presentation, Adaptive will share three case studies from medium to large enterprise in the agro science, geospatial and healthcare industries. Adaptive will show you how these customers solved their challenges with dynamic scheduling, provisioning and management of multi-step/multi-application services across HPC, cloud and big data environments to accelerate insights.