The One Event that Brings Together the Experts who Give you the Realistic Answers.

  • Can you use big data analytics to respond to changing business conditions?
  • Can you really predict future outcomes?
  • Can you make decisions based on 100 percent data rather than instinct?
  • Can you trust the answers you get?
  • Can you protect your data?

October 21, 2014 - San Jose, California


 

Don't Let Big Data Analytics Bog You Down

Cybersecurity Badge

Everyone says this is the era of big data analytics. But what does that mean to you and your business? Can you really ask questions of massive amounts of data and get actionable answers? How far can you trust big data analytics when consequences could have extensive ethical and financial impact on you as well as on global populations and businesses?

 

Rock Stars of Big Data Analytics a Success

Rock Stars of Big Data Analytics at the San Jose Civic in the Silicon Valley drew nearly 200 attendees and four sponsors. The one-day gathering of well-known data analytics authorities explored all aspects of analytics, including building a data-driven culture, launching data platforms, and ensuring privacy. The event included case studies, discussions, lunch roundtables, networking, a cocktail reception--and a live deejay, in keeping with the event's signature "Rock Stars" format.

View presentations and video from the action-packed day.

"Excitement levels were high and the audience was really engaged with the content," said Chris Jensen, IEEE Computer Society Director of Marketing & Sales. "There was that proverbial buzz you always hope to find when you come to a conference; a lot of great questions and insightful answers, and even during the breaks, attendees continued the discussions."

The speakers tackled big data analytics opportunities and challenges in a variety of industry sectors from social media, ridesharing, and human relations to technology research and the Industrial Internet. Companies ranged in size from fast-growing startups such as Lyft to well-established companies like IBM, GE, and Oracle.

speakers


Greg Arnold
Arnold
Mike Ames
Ames
 
Mark Davis
Davis
Grady Booch
Booch
 
Dan McClary
McClary
Matthew Denesuk
Denesuk
 
Satyam Priyadarshy
Priyadarshy
Chris Pouliot
Pouliot
 
Guido Schroeder
Schroeder
Michael Rosenbaum
Rosenbaum
 
Stuart Williams
Williams
 

Mike Ames

Director of Analytics, Product Management, and Hadoop Strategy
SAS

Mike Ames leads SAS' Data Science and Advanced Analytic Technologies product management team. Current initiatives of interest include: in-memory advanced analytics, Hadoop, search, and entity analytics. Since joining SAS in 2002, Ames has led the development of a variety of fraud and compliance solutions for clients in the financial services industry. His areas of interest include machine learning, distributed computing and event stream processing.  Mike holds a BBA in economics and did his graduate work in computer science at the University of Georgia, he earned an MBA from the University of North Carolina Chapel Hill.

Greg Arnold

Data Infrastructure Engineering Director
LinkedIn

Greg Arnold oversees LinkedIn's Data Infrastructure team, building scalable platforms for online storage, streams, and data analytics.The Analytics Infrastructure team, in particular, builds analytics processing engines and the core data warehouse, powering reporting and dashboards to complex collaborative filtering recommendations, people you may know, and skills pages. The team contributes to open source, including Hadoop and data ingestion platforms such as Camus, and will open-source a new real-time analytics serving platform in late 2014.

Grady Booch

Chief Scientist of Software Development
IBM

Grady is recognized internationally for improving the art and the science of software development and is creating a major transmedia project for broadcast, titled "Computing: The Human Experience." Grady served as architect and architectural mentor for numerous complex software-intensive systems around the world. The author of six best-selling books and several hundred articles on computing, Grady has lectured on topics as diverse as software methodology and the morality of computing. He is an IBM Fellow, an ACM Fellow, an IEEE Fellow, a World Technology Network Fellow, and a Software Development Forum Visionary.

Mark Davis

Distinguished Engineer
Dell

Mark Davis was appointed Distinguished Engineer at Dell following the acquisition of his big data analytics company, Kitenga. Mark founded Kitenga and served as CTO prior to the acquisition, designing and building the core Hadoop-based engine for the enrichment and analysis of unstructured data. Mark's career has included helping spin Inxight Software out of Xerox PARC (acquired by Business Objects), as a Program Manager for SharePoint and enterprise search at Microsoft, and as a DARPA researcher focused on computational linguistics, search, and machine learning. Mark is the big data lead for IEEE's Cloud Computing Initiative and serves on the executive committee of the IEEE Intercloud Testbed.

Matthew Denesuk

Chief Data Science Officer
GE Software

Matt Denesuk is the Chief Data Science Officer at GE Software and uses data science to dramatically improve industrial processes. He leads the Data Science Center of Excellence and is building a science consulting organization to support and expand the GE portfolio of Predictivity solutions. Matt previously led Smarter Planet Modeling & Analytics at IBM, where he led a strategic effort in Equipment Health Monitoring & Management to improve the management of heavy industrial operations, resulting in an Equipment & Operations Management Platform, as well as a linked consulting services practice and a global community of data scientists and researchers. Matt was concurrently a partner in IBM's Venture Capital Group, where he covered domains across big data & analytics, energy & environment, and service technology. Prior to joining IBM, Matt founded several startup companies in the Consumer Products space, establishing global manufacturing networks and retail distribution through major big-box retailers.

Dan McClary

Principal Product Manager for Big Data and Hadoop
Oracle

Prior to joining Oracle, Dan McClary served as Director of Business Intelligence at Red Robot Labs in Palo Alto, Calif. He previously was as a Visiting Scholar at Northwestern University and the Howard Hughes Medical Institute, where his research in Complex Systems focused on applying Hadoop to large-scale graph problems. McClary received his PhD in Computer Science from Arizona State University, where his work centered on adaptive optimization of mobile and ad hoc networks. He holds an M.S. in Computer Science from Arizona State, focused on hard real-time schedulability in distributed systems and was founder of imgSurf, a biometrics and electronic medical record company. McClary contributes to open source projects such as Tinkerpop Blueprints and Apache Spark.

Chris Pouliot

VP Lyft
Former Netflix Director of Analytics

Chris Pouliot is the Vice President of Data Science at Lyft, where he leads a team of Data Scientists whose charter is to help add statistical firepower to some of Lyft's more complex analyses and algorithms. Previously he was the Director of Algorithms and Analytics at Netflix and a statistician at Google.

Satyam Priyadarshy

Chief Data Scientist
Halliburton

Satyam Priyadarshy is a pioneer in the fields of data science, big data, analytics, and emerging technologies. He is currently chief data scientist at Halliburton's Landmark PSL, leading expansion of integrated workflow capabilities and other data development initiatives. Priyadarshy has appeared as a speaker at several international conferences, and has written, co-authored, presented, and published numerous research papers in peer-reviewed journals and magazines. He has held various leadership positions in AOL, Network Solutions, Acxiom Corp. prior to joining Halliburton. Priyadarshy is an adjunct faculty at Georgetown University and senior fellow at the International Cyber Security Center at George Mason University. He is advisory board member at multiple organizations including Big Data Summit and Virginia Tech's MBA Board. Priyadarshy holds a PhD from the Indian Institute of Technology in Bombay and an MBA from Virginia Tech.

Michael Rosenbaum

CEO
Catalyst IT Services

Mike is the Founder and CEO of Catalyst IT Services. Catalyst delivers onshore services for differentiation and innovation work at costs equivalent to sending that work offshore. By applying big data to team assembly, Catalyst provides applications services at 3x the productivity, half the defect rates, and fractions of normal costs. Catalyst has been recognized by Gartner and other analysts as a leader in onshoring, agile, and mobile. The Wall Street Journal, Bloomberg BusinessWeek, and others have written about Catalyst's use of big data to transform the application services outsourcing space. Mike also oversees Catalyst's sister company, Pegged Software, which applies big data to team assembly in healthcare and reduces employee turnover in hospitals and long term care facilities by 45-77 percent.

Guido Schroeder

Senior Vice President, Products
Splunk

Guido Schroeder has served as Splunk's Senior Vice President, Products since April 2012. Prior to joining Splunk, he was with SAP Labs, an enterprise application software company, where he served as Senior Vice President Development, Technology Innovation Platform BI from 2008 to 2012; as Vice President Development, SAP NetWeaver BI Client Suite, from 2007 to 2008; as Vice President Development Suite Optimization Analytics from 2006 to 2007; as Vice President Development, SAP NetWeaver Imagineering from 2004 to 2006; and as Director Development, BI Advanced Technologies from 2000 to 2004. Guido holds a MSc and a PhD in Physics from the University of Kiel in Germany.

Stuart Williams

Vice President of Research
Technology Business Research Inc.

Stuart Williams' research focuses on IT business models and innovation at the intersection of vendor go-to-market strategies and enterprise customer behavior, budgeting and satisfaction. Prior to this role, Stuart was the director of TBR's Software and Cloud practices, leading a team of analysts in deep-dive research into business models, innovation and benchmarking best practices across a landscape of vendors. Since joining TBR in 2005, he launched multiple customer research streams on public, private and hybrid cloud, business intelligence platform adoption, and high-performance appliances. He is an expert on technology commercialization, business strategy, and competitive analysis. As an analyst he led coverage of Oracle, Microsoft, SAP and IBM, as well as disruptive new vendors across the software and cloud landscapes. His commentary and insights are widely quoted in the press, including The Wall Street Journal, Fortune, USA Today, The Street, ZDNet and CNET.

agenda


Big Data Analytics Badge

Grady Booch

Chief Scientist of Software Development
IBM

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.

Greg Arnold

Data Infrastructure Engineering Director
LinkedIn

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.
 

Matthew Denesuk

Chief Data Science Officer
GE Software

Big Data, Physics, and the Industrial Internet

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.

Chris Pouliot

VP, Lyft
Netflix Former Director of Analytics

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.
 

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.

Peter Hoopes

VP and GM BIRT Analytics Division
Actuate Corp.

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
 

Satyam Priyadarshy

Chief Data Scientist
Halliburton

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.

Michael Rosenbaum

CEO
Catalyst IT Services

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.

Mark Davis

Distinguished Engineer
Dell

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.

Guido Schroeder

Senior Vice President, Products
Splunk

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.

Stuart Williams (moderator)

Vice President of Research
Technology Business Research Inc.

Mike Ames

Director of Analytics, Product Management, and Hadoop Strategy
SAS

Dan McClary

Principal Product Manager for Big Data and Hadoop
Oracle

Panel Discussion: 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.

sponsors


Actuate
Cloudera
Adaptive Computing
SC Magazine
Cloudera
eSentire
KD Nuggets
InsideBIGDATA
FierceBigData
 
 
 
 
 

 

Computing Now
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