Simplifying Big Data Analytics for the Business

Tasso Argyros, Teradata Aster
The opportunity exists for organizations in every industry to unlock the power of iterative, big data analysis with new applications such as digital marketing optimization and social network analysis to improve their bottom line. Big data analysis is not just the ability to analyze large volumes of data, but the ability to analyze more varieties of data by performing more complex analysis than is possible with more traditional technologies. This session will demonstrate how to bring the science of data to the art of business by empowering more business users and analysts with operationalized insights that drive results. See how data science is making emerging analytic technologies more accessible to businesses while providing better manageability to enterprise architects across retail, financial services, and media companies.

Technology Strategies for Big Data Analytics

Paul Bachteal, SAS
The exploding volume, complexity and velocity of big data present an increasing challenge to organizations, but also a significant opportunity to derive valuable insights. As organizations are tasked with managing massive data sets, it’s clear that the value of big data will be derived from the analytics that can be performed on it. Analytics is the key to identifying patterns, managing risks and tackling previously unsolvable problems. This presentation provides an overview of how to comprehensively tackle big data, including emerging strategies for information management, analytics, and high performance analytics.

Simplifying Big Data Analytics for the Business

Mayank Bawa, Co-President, Teradata Aster
The opportunity exists for organizations in every industry to unlock the power of iterative, big data analysis with new applications such as digital marketing optimization and social network analysis to improve their bottom line. Big data analysis is not just the ability to analyze large volumes of data, but the ability to analyze more varieties of data by performing more complex analysis than is possible with more traditional technologies. This session will demonstrate how to bring the science of data to the art of business by empowering more business users and analysts with operationalized insights that drive results. See how data science is making emerging analytic technologies more accessible to businesses while providing better manageability to enterprise architects across retail, financial services, and media companies.

Technology Strategies for Big Data Analytics

Bernard Blais, Global Strategist and Principal Manager, SAS
The exploding volume, complexity and velocity of big data present an increasing challenge to organizations, but also a significant opportunity to derive valuable insights. As organizations are tasked with managing massive data sets, it’s clear that the value of big data will be derived from the analytics that can be performed on it. Analytics is the key to identifying patterns, managing risks and tackling previously unsolvable problems. This presentation provides an overview of how to comprehensively tackle big data, including emerging strategies for information management, analytics, and high performance analytics.

Transforming Health Care Analytics - Integrating ‘Big Data’ Analytics into a Health Care Provider’s BI Ecosystem

Dave Brown, Senior Director, Aurora Health Care
The challenges Big Data bring to our existing data integration and analytics platforms are… huge. The shared business imperative to reduce IT expense, while simultaneously maintaining BI service quality is ever-present. However, the ‘new’ business value Big Data unlocks must be pursued to remain competitive.

Most Big Data implementations are external, or alongside the existing Enterprise Data Warehouse (EDW) platform; adding yet more people, more process, and more technology costs to an already stressed financial bottom line.

A new model that blends the technologies of a traditional EDW Ecosystem, with that of Big Data, is a noble cause. Can this be done? In a word, Yes!

This presentation narrates a Hybrid Business Intelligence Ecosystem that combines a message-centric ETL methodology, leveraging an RDBMS to perform all dimension & fact table processing, and then integrates a Big Data platform, enabled with the analytics of SQL-MapReduce & nPath, to power ALL traditional BI reporting, as well as next-generation Big Data analytics – unlocking new data-driven insights, while gaining substantial query performance increases and the lowest Total Cost of Ownership (TCO).

Razorfish Multi-Channel Marketing: Better Customer Segmentation and Targeting

Matt Comstock
Matt Comstock, Vice President Business Intelligence Office, Razorfish
From search to email to social, customers are interacting with your brand across a variety of channels. But what do people do once they view an advertisement or get an email? What common behaviors are displayed once they’re on your site? By combining media exposure/behavior, site-side media, and in-store purchase data, you can understand better the impact media has on driving value to your business. Come to this session to learn how better data-driven multi-channel analysis lets you see what consumers do before they become a customer to understand what content influences which segments of users by media audience. Discover new segmentation and targeting strategies to improve engagement with your brand and increase advertising lift. See how a leader in digital marketing uses a combination of technologies including Teradata Aster, Hadoop, and Amazon Web Services to handle big data and provide big analytics to improve business value.

Apache Hadoop's Role in Your Big Data Architecture

Shaun Connolly, VP Corporate Strategy, Hortonworks
Apache Hadoop has evolved rapidly to become a leading platform for managing and processing big data. If your organization is examining how you can use Hadoop to store, transform, and refine large volumes of multi-structured data, please join us for this session where we will discuss, the emergence of “big data” and opportunities for deriving business value, the evolution of Apache Hadoop and future directions, essential components required in a Hadoop-powered platform, and solution architectures that integrate Hadoop with existing data discovery and data warehouse platforms.

The Big Data Revolution: Integrating Big Data in BI Ecosystems

Wayne Eckerson, Principal of BI Leader Consulting and Director of Research at TechTarget
The Big Data revolution represents the next big leap in business intelligence. It supplements existing data management infrastructures with new types of data, such as Web traffic, social media, sensor, and text, that don’t play nicely in traditional data warehouses. It brings a rich new assortment of products, such as Hadoop, NoSQL, and SQL/MapReduce databases, that can speed the delivery of analytical insights and applications. This presentation will describe the unique characteristics of Big Data, evaluate its role in BI architectures, and examine techniques for integrating it with existing BI products and tools.
You Will Learn:
- What is Big Data and how it will evolve
- Big Data’s impact on existing BI architectures and products
- How Big Data creates a BI ecosystem that finally meets the needs of business analysts
- Four methods of integrating with Big Data products

What Makes A Great Data Scientist?

Bill Franks, Teradata
As enterprises come to understand the value of analytics, more support and funding is being allocated to build these departments. Managers are now faced with the challenge of who to hire. What exactly makes a great analytic professional? Is a Data Scientist a "must have"? Should a candidate have a PhD? Is prior experience in a specific industry vital? Just what is the right fit when creating a successful team? The answers to these questions are still unclear as the value of analytics continues to grow.

In this session, Bill Franks, author of the book, Taming the Big Data Tidal Wave, addresses these and many more questions as he defines the characteristics of high-performing data scientists and great analytics teams.

Evaluating Big Data Predictive Analytics Platforms

Mike Gualtieri

Mike Gualtieri, Principal Analyst, Forrester Research
Great. You have Big Data. Now what? You have to analyze it to find game-changing predictive models that you can use to make smart decisions, reduce risk, or deliver breakthrough customer experiences. Big Data Predictive Analytics solutions are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources. In this session, Forrester Principal Analyst Mike Gualtieri will discuss the key criteria you should use to evaluate Big Data Predictive Analytics platforms to meet your specific needs.

Is It Live, Or Is It Memory? Real-Time Social Analytics

Ian Hersey, Chief Technology Officer, Attensity
Social data moves at very high rates – Twitter, for example, sends out over 500 million tweets per day, and per-second rates can vary widely. For event-based analytics – for example, understanding the social signal emerging as an audience watches a presidential debate or the latest episode of "Homeland" - we need to be able to ingest and analyze the data asynchronously and with very low latency, but we also need to persist the resulting aggregations (sentiment, intent, demographic info, influencers, etc) very quickly while simultaneously maintaining and updating historical views of the data. This is more complicated than it sounds. We will present a system and an approach that balances real-time and historical data and discuss the evolution of the system – i.e., what worked and what didn't.

Solving the Education Crisis with Big Data

Crystal Hutter, Chief Operating Officer, Edmodo
Every day there are more and more examples of how big analytics and data scientists are improving Internet services and experiences in the consumer and enterprise spaces. K12 Education is one of the biggest and most important sectors of the economy and new social learning platforms in the classroom are providing a way to leverage the approaches of big data to improve educational outcomes for all types of students all over the world. Edmodo is the largest and fastest growing educational network with 7,000,000 teachers and students safely connecting, sharing web content and accessing homework, grades and school notices. There are millions of actions that occur daily on the Edmodo platform providing a rich source of structured and unstructured data to analyze.

Cross Industry Lessons from Moneyball Analytics

Ari Kaplan, "Moneyball" advisor to Major League Baseball teams,
President of AriBall

Ari Kaplan is a leading figure in sports analytics. Known throughout the Major Leagues for revolutionizing and modernizing player assessment, Ari's use of analytics and technology helps coaches prepare for games, players understand their strengths and weaknesses, General Managers forecast future performance and risk of player contracts and draft picks, and more.

In this presentation, Kaplan will discuss how professional sports teams and players use analytics and data visualization in the Major Leagues. Through his 23 years of experience in over half of all MLB organizations, he will discuss the changes that took place and where analytics will continue to innovate in the future.

Practical Applications of Visual Analytics

Stephen McDaniel, VP Product Management, Tableau Software
Visual analytics has long been an appealing but esoteric science which seemed better suited for research than business. But today, visual analytics is spreading beyond the research department to the general business population, helping everyday employees gain insight into data in order to solve unexpected problems and challenges. Visual analytics is changing the way people interact with data and the way business intelligence is defined in organizations. In this presentation, we will share real-world examples of how everyday people can and are using visual analytics to solve some of business and society's most challenging issues. We'll also share his vision for the future of visual analytics and identify what's needed to bring visual analytics to the forefront of mainstream data analysis.

Using Big Data to Quantify Loyalty -
'Do you come here often?'

Rafael Mejia, Barnes & Noble
Customer loyalty traditionally has been measured by running surveys to determine the customer’s 'willingness to recommend', satisfaction, etc. Now, with the ability to analyze vast amounts of sales data, online behavior, and reading patterns, Barnes & Noble can measure shifts in customer loyalty at individual  stores, regions, or channels. Methods that have long been available for measuring online behavior are now being applied to their brick-and-mortar stores, for a comprehensive view of customer purchase patterns, enabling Barnes & Noble to improve services that better address an individual customer’s needs.

The Social Media Challenge to Big Data

Alan F. Nugent, Mzinga
Social media has introduced many sources for streaming data, from social interactions on social networking sites, continuous content from micro-blogging, to mobile applications that produce location-based data. This data is being created in real-time, is continuous, voluminous, and always evolving. The impact of the influx of data streaming into data centers introduces new challenges to traditional business intelligence, like the ability to process large amounts of data and make sense of it in near real-time when its impact is most relevant. Thus working with streaming data using traditional business intelligence approaches may not yield great results and at times make problems seem intractable. Technologies, especially around Big Data and social intelligence are starting to shape how some of these concepts can move towards reality. Ultimately these changes will shape and change social intelligence for the online connected world. In this world, the dynamic and evolving relationships we hold with each other, places, movements, etc will define the data from which insight and opportunities for innovation, engagement and ROI from social media and our behaviors are derived.

Big Data Decision-Making

Gayatri Patel, eBay
The wonders of what data can do for an organization is measured in the productivity and competitiveness of their team's decisions. Some believe more data is the key. Agreed...but good decisions require more than just deriving intelligence from big data. In this dynamic market, the need to socialize and evolve ideas with other teams, quickly correlate information across sources, and test ideas to fail fast early are strong enablers to gain competitive footing. eBay¹s analytic and technology advancements garners insights and approaches that continue to help our employees tell their "data stories" and make better decisions.

Using Data to Manage in Today’s Chaotic Environment

DJ Patil, Data Scientist, Greylock Partners
The ability to manage and leverage data has never been more critical to business. At the same time, the volume and types of data have grown dramatically in the past few years. The choices for technologies, people, and processes are complex. In this keynote session, Dr.DJ Patil will talk about how to manage through all this chaos.

Trust and Influence in the Complex Network of Social Media

William Rand, University of Maryland
The dramatic feature of social media is that it gives everyone a voice; anyone can speak out and express their opinion to a crowd of followers with little or no cost or effort, which creates a loud and potentially overwhelming marketplace of ideas. The good news is that the organizations have more data than ever about what their consumers are saying about their brand. The bad news is that this huge amount of data is difficult to sift through. We will look at developing methods that can help sift through this torrent of data and examine important questions, such as who do users trust to provide them with the information and the recommendations that they want? Which tastemakers have the greatest influence on social media users? Using agent-based modeling, machine learning and network analysis we begin to examine and shed light on these questions and develop a deeper understanding of the complex system of social media.

Turning Big Data to Business Advantage

Mohanbir Sawhney, Northwestern University
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage. By analyzing data that was previously unavailable or was too difficult or costly to process, enterprises can gain valuable new insights. For example, they can learn how social influence affects customer behavior by analyzing shoppers' transactions, social networking and geographical data. By iteratively exploring large quantities of data on an ongoing basis, enterprises can create personalized customer experiences, develop new products and services, detect early warning signals and analyze the effectiveness of multi-channel marketing campaigns. Big Data allows enterprises to compete on agility – sensing opportunities and threats and responding with speed and purpose. In this session, we will understand the real story of Big Data in terms of business outcomes and competitive advantage. We will go beyond the hype and the buzzwords of Big Data to understand the strategic implications of Big Data. The audience will leave with a clear picture of the story they need to know and the story they need to tell to business decision makers in their enterprises to accelerate the adoption of Big Data analytics.

Lessons Learned from A/B and Multivariate Experiments

Dan Siroker, Co- Founder & CEO, Optimizely
Most businesses know they should be A/B testing their website but few actually do it on a regular basis. One of their biggest challenges is knowing what to test. In this talk, Dan Siroker will share the best practices and lessons learned from working with over 10,000 users who have created more than 30,000 experiments using a dead-simple A/B testing tool called Optimizely. Siroker will share key insights from these experiments and leave you inspired to start testing today.

The Effect of Big Data on Big Company Analytics

David Stone, Director of Information Analytics & Innovation, Sears Holding Corporation
Big data is bigger than ever at big companies, growing from Terabytes to Petabytes in the last couple of years. What are the trends that are working? What is not working? Where is this new big analytics industry taking us? Sears is in the process of making a game changing investment in analytics. Leveraging the power of analytics as a service on top of a powerful Enterprise Data Warehouse, empowering a wider variety of analysts to get the data they need to drive the business based on facts.

Practical Applications of Visual Analytics

Dustin Smith, Community Manager, Tableau Software
Organizations now have the ability to store and process massive amounts of data like never before. And there are huge expectations for turning data into a fundamental driver for business transformation and competitive advantage.
Visual analytics is helping everyday employees gain insight into data in order to solve unexpected problems and challenges, it is changing the way people interact with data and the way business intelligence is defined in organizations. In this presentation, we will share real-world examples of how everyday people can and are using visual analytics to solve some of businesses most challenging issues.

From Data Science to Business Value - Analytics Applied

Simon Zhang, LinkedIn
There is a lot of interest around "data science", but how can you ensure alignment with the business? How can the new insights from data science be applied to business results? Often, business users want their analytics "one drink at a time", but how can you help business users help themselves? Come to this session to discover new ideas and best practices for building scalable, repeatable analytic applications which give the business the insights they need to drive results.

Big Brands Meet Big Data – The Newest Innovator’s Dilemma

Marc Parrish, Vice President, Retention & Loyalty Marketing, Barnes & Noble
Big Data is moving too fast for Big Brands. They don't have the ability to technically pivot, to move quickly enough to take advantage of the astounding amount of customer information that's available, and make it part of their everyday practices. This poses a great risk to the world’s great retailers. Well-managed companies often fail because the very same management practices that made them industry leaders also make it difficult to assimilate the disruptive technologies that in the end allow others to steal their market.
With Big Data, the gap between merely sustaining your operations, and adopting disruptive technologies, is the difference between progress, or perish..

Apache Hadoop's Role in Your Big Data Architecture

Jim Walker, Director of Product Marketing, Hortonworks
Apache Hadoop has evolved rapidly to become a leading platform for managing and processing big data. If your organization is examining how you can use Hadoop to store, transform, and refine large volumes of multi-structured data, please join us for this session where we will discuss, the emergence of “big data” and opportunities for deriving business value, the evolution of Apache Hadoop and future directions, essential components required in a Hadoop-powered platform, and solution architectures that integrate Hadoop with existing data discovery and data warehouse platforms.
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