Big Data & Analytics for Pharma

Gain Greater Insight Through Big Data & Analytics

June 10 & 11, 2015Philadelphia, PA

Past Speakers Include

Companies

• Information Architect, Boehringer Ingelheim• Director, Global Analytics & Insights, Shire • Sr. Leader, Global Affairs, AstraZeneca• Analyst, S&OP, Abbott Laboratories • Leader, Bioinformatics, MSKCC• Director, Forecasting, Amgen

• Head of Analytics, Takeda Pharmaceuticals• Vice President, Marketing, Decisyon • Sr. Data Analyst, Hospira• Sr. Scientist, Informatics, Berg

• Director, Informatics, Pfizer• VP, Informatics, Covance• Director, Registry, Miraca Life Sciences• Director, R&D, Rockland Immunochemicals• Sr. Director, Data Strategy, Genetech• Analyst, S&OP, Abbott Laboratories • Sr. Statisician, Takeda Pharmaceuticals• Vice Chair, Research, UMESP• Director, Forecasting, AstraZeneca

Who Will You Meet There is no question that IE. provides the gold standard events in the industry and will connect you with decision makers within the analytics industry. You will be meeting senior level executives from major corporations and innovative small to medium size companies.

Job Title Of Attendees

President/Principal

SVP/VP

C-Level

Snr. Director/Director

Global Head/ Head

Snr. Manager/Manager

Academic (1%)

78%

1000+ Employees300-999 Employees50-299 EmployeesLess than 49 Employees

Company Size Of Attendees

8%

11%

25%56% 81%Attendees are

companies with at least 300

employees

3%

21%

12%

42%

13%

8%

Attendees are at Director level or above

Past Delegates Include• Senior VP, Forest Laboratories

• Senior Director, CSL Behring

• Vice President, Sanofi Pasteur

• Head of Business Analytics, AstraZeneca

• Director, Analytics, Pfizer

• Vice President, Business Intelligence, Abbvie

The Big Data & Analytics for Pharma Summit brings together thought-leaders from the industry for an event acclaimed for its interactive sessions and high-level speakers. As many organisations are now gathering more data on patients than ever before, they are able to achieve a 360 degree view on the market and view patterns in their data. This presents both a challenge and opportunity as organisations must gain actionable results in order to succeed. Bringing together the decision makers and

leaders from within the industry, this event will focus on the challenges specific to Pharmaceutical companies surrounding data analytics, insight and data visualisation, as well as the benefits of using analytics and analysis in a rapidly evolving and challenging market. Illustrated with industry case studies as well as informative panels and breakout sessions, this summit promises to be the must attend event for data scientists working within Pharma.

About the Summit

Previous Speakers Information

Jennifer Quigley is the Director of Registry and Repository at Miraca Life Sciences, an anatomic pathology laboratory providing highest quality laboratory services to over 22,000 patients a week as well as health IT solutions to client providers. Since accepting the position of Director of Registry and Repository in 2012, Jennifer and the Miraca research team have managed, developed and implemented registry projects that have led to improvements in demographic and outcomes data collected, making way for evolutionary change that will result in a dynamic registry program. Jennifer is currently working on development of the Miraca data program; a primary goal is to associate Miraca data and tissue with provider clinical data, and therefore improve the data record. Prior to joining Miraca Life Sciences in 2010 (formally, Caris Life Sciences), Jennifer held multiple positions at CBLPath, DIANON Systems and UroCor and has over 15 years of oncology and specialty diagnostics experience. A graduate of the University of Pittsburgh, Jennifer holds BS degrees in Biology and Geology and advanced training in planning, leadership and organizational habits.

Bridging the Healthcare Data Divide: Coordinating Data Collection to Enhance Longitudinal Patient RecordsBig data can be a powerful tool for health organizations and providers seeking to reduce healthcare costs and improve patient outcomes by making better research, treatment and business decisions. The amounts of data collected are endless; clinical data to claims data to laboratory data, and the data in between; registries and clinical trials, for example. The majority of information collected is observational and non-interventional; this presentation will focus on such data. Providers and organizations collecting patient information fit into numerous categories and typically cannot coordinate records in a meaningful way. As an example, consider a single oncology patient. An oncology patient’s healthcare data includes pre-disease diagnostic and clinical data, primary disease diagnostic and clinical data and non-primary disease diagnostic and clinical data, plus. Information is collected and maintained by multiple providers and in most instances, there exists very little sharing of structured data that would result in a complete, longitudinal record, A record that can be analyzed and transformed into actionable knowledge.

Jennifer QuigleyDirector, Registry & Repository Miraca Life Sciences

Get Connected! Explore Big Data & Analytics for Pharma Content

Follow our Scoop.it! Page for daily updated news, articles and content!

“Like” us on Facebook!

Use the hashtag #DataPharma and join the conversation on Twitter!

Previous Speakers Information

Dr. Ray Liu received his PhD degree from Columbia University. Currently he is the head of Analytical Innovation and Consultation group at Takeda Pharmaceutical Company, the largest pharmaceutical company in Japan and the oldest pharmaceutical company in the world. He manages statisticians across sites to provide statistical consultation and project support to various functional areas in R&D, including Discovery, DMPK, CMC, Translational Research, Phase I clinical trial, and Outcome Research.

The Joint Analysis of Genomic and Pharmacological Data: A Novel Framework In Development

Drug target discovery involves a variety of complex data sources. Genome wide expression data, metabolics data, and drug sensitivity profiles are examples of commonly used data. Traditional analysis methods consider each type of data one at a time, but pooling the data could reveal new information. Here we propose a new statistical method using tensor factorization and Bayes’ theorem for the joint modeling of various data sources. This model enables incorporation of prior knowledge on the associations between genes, drugs and pathways through the Bayesian sparse model set-up. The model has multiple usages, including the prediction of novel drug targets. Performance of this model is evaluated via various simulations.

Ray LiuHead of Analytical Innovation Takeda Pharmaceuticals

Dr. Anthony E. Lee is Global Capability Development Director for Commercial Effectiveness at Astrazeneca Pharmaceuticals, serving as internal consultant in Business Planning, Forecasting, and Analytics.  Prior to this role, he was the US Head of Forecasting and built the Center of Excellence (CoE) where he innovated a ‘systems forecasting’ methodology. The paradigm shifting methodology transformed AZ US decision making and business planning capability by improving the forecast accuracy measured in double digit % errors to a sustained average of 2% errors annually in last 4 years. Dr. Lee brings with him 30 years of experience applying his systems thinking/problem solving experience across various areas encompassing production scheduling, LEAN, demand planning, business planning, forecasting,   and business development.   Prior to joining AZ US, he worked at Alcoa and later started-up his own demand planning software company.

Anthony LeeDirector, ForecastingAstraZeneca

Big Data – Analytics = DOA Petabytes

A body needs a volume of blood to be alive.  Yet, when it dies, we know its volume of blood still remains the same.  So what other things changed?  The circulation of, and the extraction of valuable nutrients from, this same volume of blood stopped. Transfusing more blood into the corpse will not help bring the body back alive. Feeding more   data into a corporation that has slow or blocked information circulation and that lacks analytics capability to extract business-impacting insights will bring no additional value to its decision-makers – much like trying to transfuse more blood into a corpse.   It results in DOA Petabytes when the project is over. What must we consider when designing a fit-for-purpose analytics solution to circulate and extract the Petabytes of BigData?

Giancarlo CrocettiInformation ArchitectBoehringer Ingelheim

Giancarlo Crocetti is a Business Partner for the RD&M department at Boehringer Ingelheim and actively involved in several research areas including, but not limited to: Knowledge Search, Semantic representation and Inference, Big Data, Data & Text Mining, and Network Analysis. He is also an Adjunct Professor at Pace University where he teaches classes on Data Mining and Research Methods topics.

Big Data & Research: How Big Data Will Change The Way We Think & ResearchThe way we conduct research is rooted on the premise of “data unavailability” which has dictated the research methods used in the last century. Big Data is challenging all these assumptions and opening up new opportunities that will force a drastic change in centuries of established practices in research and completely change the way we study, infer, and understand reality.

Previous Speakers Information

Dr. Dongliang Ge is Director of Bioinformatics at Gilead Sciences, where he provides leadership to the bioinformatics group and provides strategic input to bioinformatics infrastructure and process. Prior to Gilead, he was appointed as Assistant Professor of Biostatistics and Bioinformatics at Duke University School of Medicine. He received his PhD of Biostatistics and Genetic Epidemiology from Chinese Academy of Medical Sciences in 2004. Dr. Ge was named by the Genome Technology magazine as one of the “rising stars” in 2009. His work in discovering the IL28B genetic variants associated with the hepatitis C patients’ response to clinical treatment, published in Nature in 2009, has received over 2000 times of citations to date.

Big Data Strategy for PGx Studies in Precision Medicine

Utilizing pharmacogenomic strategy to develop clinical biomarker and companion diagnostics has been increasingly important in the pharmaceutical industry. Using real-world examples, this talks covers several of the most important bioinformatic considerations in this strategy.

How do we efficiently manage the large amount of data at differnet levels of precison to ensure a seamless data flow?

How do we annotate and present these data to make it more comprehensible and deliverable? How do we design and execute the associated clinical trials more efficiently?

Dongliang GeDirector, Bioinformatics Gilead Sciences

Krish Ghosh PhD, MBAVice President, InformaticsCovance

A Closer Look at Big Data and Analytics to Improve Drug Development Performance

McKinsey & Co., recently said, “BIG DATA could reduce research and development costs for pharmaceutical companies by $40 - $70 billion, and an era of open information in health care is now under way.” Big data is one of the hottest buzz phrases among Pharmaceutical information technology today. Executives across the life science industry recognize that the implications of utilizing big data in a way that will benefit the business cannot be ignored. Exploring the use of big data through utilization of such capabilities as data analytics and data mining, gives companies the opportunity to discover new insights among existing information that can make businesses increasingly agile in ways that may have been unattainable until now.

Covance is the market leader in central laboratory and pre-clinical services, and one of the top six providers of Phase 1-4 clinical trial services. Covance has one of the most comprehensive investigator knowledge-base in the pharmaceutical industry. Using this vast amount of data we have developed Xcellerate, a market leading product. It is a unique, proprietary approach to forecasting, investigator and site selection, and clinical trial management, designed to optimize trial results in order to help clients improve quality, reduce waste, decrease timelines, increase ROI and help get therapies to market faster.

Krish Ghosh is Vice President of Informatics at Covance, and serves as a member of the Informatics Executive Management team reporting to the CDO of the company. Prior to this, Krish was the Vice President of Global Resource Management and reported to the CFO. Krish joined Covance in May of 2006. He is responsible for leading Global Informatics activities. He has developed innovative methods and solutions to help drive value in the drug development continuum, business performance/expansions, and supported the growth, capacity management, expansion efforts, and profitability of the company.

In addition, Krish has 13 years of Pharmaceutical industry experience and 4 years in academics. He was the Director of Project Planning and Information at Wyeth and held different positions in R&D at BMS.

Krish holds a Ph.D. in Statistics and an MBA in Finance from Temple University.

Krish Ghosh PhD, MBAVice President, InformaticsCovance

Big Data Des ign and Appl icat ion: Implications for Clinical and Commercial Decision Support

Comparative Effectivness Research (CER) is promising to fill the evidence gaps for decision making by healthcare stakeholeders, including providers, patients, payers, and policy makers. However, it seems risky to implement policies or guideline based on a general “drug A is better than drug B for disease X” kind of comparative effectiveness, when facing individual patients in real-world healthcare. The decision makers need to know to what patient subgroup, what disease stage, and what treatment pathway, a comparative effectiveness evidence is applicable.

The proposed talk will present a predictive modeling approach for integrating CER and Personalized HealthCare (PHC), as welll as an idea of developing clinical decision support systems utilizing personlized comparative effectivness evidence. Some prelimimary results will be presented from such an approach in the context of clinical drug development.

Usman Iqbal is a Senior Medical Affairs Leader – Neuroscience at AstraZeneca, and based in Cambridge, MA. Usman has 10+ years of diverse experience spanning clinical medicine, health policy & management, R&D/Med AFF, Health economics & outcomes research across academia and biopharmaceutical. His experience spans number of different Therapeutic areas including Oncology, as a former Senior Director & Head of Oncology Global Evidence & Value Development (GEVD), at Sanofi.  As part of both R&D and Med Affairs in different roles, Usman has been responsible for Portfolio Prioritizations, Integrated Medical Affairs Planning, and end-to-end Evidence generation   based on Stakeholder engagement (Patient, Provider, Payer), Market insights and Relative Medical Value Assessment Platforms. Prior to working in the industry that also includes Amgen and Boehringer Ingelheim, Usman was at the Boston University Health Outcomes Technology Group as a senior research fellow and served as a research consultant for Veterans Affairs Pharmacy Benefit Management (VA-PBM), the Centers for Medicare &, Medicaid Services’ Health Outcomes Survey Initiative (CMS-HOS), the Agency for Healthcare Research and Quality (AHRQ), and the National Committee for Quality Assurance (NCQA).   Usman has authored more than 50 publications and his research experience encompasses patient & physician evaluations in BIG Data and large integrated health care systems with a specific focus on comparative effectiveness, disease management, physician profiling, patients’ health related quality of life, and patient access solutions. Dr Iqbal received his MD from Allama Iqbal Medical College, Lahore, Pakistan and Master of Public Health and Master of Business Administration from Boston University.

Dr. Abarca Heidemann, Director of Research and Development at Rockland Immunochemicals, is focused on driving Rockland to be the market leader in antibody production and assay development. She is actively involved in efficiently expanding the Life-Science tools portfolio in research areas including cancer, epigenetics, neuroscience and stem cells. She grows and maintains critical partnerships with academic and BioPharma institutions. Working closely with the Rockland marketing and sales department, her scientific expertise is essential for the strategic development of Rockland’s products. Karin obtained her PhD in Biochemistry at the University of Cologne, Germany, followed by post-doctoral trainings at prestigious universities in Europe and USA.

Breaking Barriers: Sharing Data to Enhance Productivity and Revenue

Historically hording information and limiting access to data suggested strength and market power. Despite this belief, Rockland is opening doors by partnering with leading academic and BioPharma institutions to build a culture of data exchange. We have found that even the most simple, non-sophisticated information can change the outcome of a product. Open communication among partners is not only a practice that leads to greater opportunities and productivity, it is a Rockland value.

Karin Abarca Heidemann, PhDR&D DirectorRockland Immunochemicals

Previous Speakers Information

Previous Speakers Information

Schreiber is an innovative thinker and leader with a broad skill set encompassing biology, informatics, knowledge engineering, data mining and IT. Skilled in the application of informatics and knowledge engineering to the drug discovery process. Ten years working in industry informatics including seven years of managing informatics and cross-disciplinary teams. Excellent written and oral communication skills. Adept at liaising between scientists, management and IT professionals.

Thinking Differently About Data and Data Integration?

The Biomedical space we have been blessed and cursed with data Variety since before data was "Big". Data variety comes from a diversity of sources, from a diversity of research activities and most subtly from diverse definitions of common biomedical entities. This session will take you through the observations we have made at Norvatis about data integration including:

• Why building monolithic integrated warehouses opposes research

• Why relational databases resist integration

• Why biological entities cannot have perfect definitions and why schema's will kill you

• Semantic approaches that allow access to diverse data and agile integration to solve specific research questions

• Why data marts should be available on demand using the technology that best suits the most appropriate analysis

Mark SchreiberDirector, Knowledge EngineeringAmgen

Li received her bachelors degree in probability and statistics from Peking University, China   in 2005  and PhD in Biostatistics from University of Pennsylvania   in 2009. During her four years of PhD studies, she mainly focused on model building and variable selection on high-dimensional data analysis, with application to gene expression data. In collaboration with advisor Dr. Hongzhe Li, they published three papers regarding this research. After graduation, she began her biostatistician career at the Food and Drug Administration.

In 2011, she moved to Chicago area and started working  in pharmaceutical industry. Right now she is working as Senior statistician at Takeda Pharmaceuticals. She has been active in statistical communities, serving in the FDA/Industry workshop program committee for the past  Four years, co-chair for roundtable, short course committee and proposal submission committee.

Statistical Methods for Analysis of High-Dimensional Genomic Data with Graphical Structure

Graphs and networks are common ways of depicting biological information. In biology, many different biological processes are represented by graphs, such as regulatory networks, metabolic pathways and protein-protein interaction networks. This kind of a priori use of graphs is a useful supplement to the standard numerical data such as microarray gene expression data. In this presentation, we consider the problem of regression analysis and variable selection when the covariates are linked on a graph. We study a graph-constrained regularization procedure and its theoretical properties for regression analysis to take into account the neighborhood information of the variables measured on a graph. This procedure involves a smoothness penalty on the coefficients that is defined as a quadratic form of the Laplacian matrix associated with the graph. We establish estimation and model selection consistency results and provide estimation bounds for both fixed and diverging numbers of parameters in regression models. We also developed a second method using Markov Random Field to incorporate the graph information into analysis of high-dimensional data. Finally, we demonstrate by simulations and a real dataset that the proposed procedure can lead to better variable selection and prediction than existing methods that ignore the graph information associated with the covariates.

Caiyan LiSr. StatisticianTakeda

Previous Speakers Information

Dr. Farong Li, Director of Amgen Commercial Operations, currently heads up a cross functional Business Analysis and Information team. His core responsibilities include forecasting, marketing sciences, and information management for US Commercial Operations. At Amgen Farong led the efforts of building key marketing sciences and forecasting capabilities over last 14 years. Prior to Amgen, he held various marketing analytical positions in Novartis, Health Products Research, and J&J. Farong received his Ph.D. in Applied Economics from the University of Minnesota.

Panel Session: Unleashing The Power of Big Data in BioPharma Commercial Operations

BioPharma commercial function starts to work with large quantities of data in recent years, the challenge has been and remains interpreting it and using it effectively

Key Question: What’s your experience and challenges?

Commercial decision support historically has been relied on small data from primary market research and elaborate analytics. Judgment has been played a big role

Key Question: What issues do you see using this approach and can we do a better job using big data?

Recreating and monitoring customer journeys using big data has been proved to be a very effective analytical model for other industries to make more accurate predictions, better decisions, and precise interventions

Key Question: Can we leverage the learning from other industry and apply to BioPharma commercial operations?

Have you experimented with this approach yet?

Dr. Farong LiDirector, ForecastingAmgen

Donovan T. Cheng, PhDGroup Leader, Clinical BioinformaticsMemorial Sloan-Kettering Cancer Center

Dr. Donavan Cheng is an accomplished bioinformatics scientist with over 5 years of experience in both pharma/biotech and molecular diagnostics. In his current role at Memorial Sloan-Kettering Cancer Center (MSKCC), Dr. Cheng leads the Clinical Bioinformatics group within the Molecular Diagnostics Service, where he has been involved in the development and CLIA-approval of NGS diagnostic tests through NY State Department of Health. Before his tenure at MSKCC, Dr. Cheng worked at Hoffmann-La Roche Inc, where he led the data analysis efforts for a number of genomic and biomarker profiling studies.

Production-Scale NGS in a Cl inical Operation: Challenges and Upsides

Following its recent rapid decline in cost, next generation sequencing (NGS) has become an increasingly viable technology for clinical molecular diagnostics. Leading medical centers are developing NGS assays for clinical applications, to provide targeted therapy and identify genetic alterations associated with phenotype. The size and complexity of data generated by production-scale clinical sequencing presents unique challenges in terms of analysis, management and subsequent data mining. This talk focuses on our efforts at MSKCC to develop scalable and extendable analysis pipelines and data management systems, to support large scale, targeted re-sequencing of actionable cancer genes in the clinic.

The Current Speaker Lineup Is Being Recruited for 2015! Nominate someone today!

The Information

For larger groups or special requests contact Sean +1 (415) 692 5514 by calling or email sforeman@theiegroup.com. Team discounts are applicable at the point of registration only.

Group Discount Offers3 Silver Passes: $3000 ($1000 per attendee)5 Silver Passes: $4500 ($900 per attendee)3 Gold Passes: $3900 ($1300 per attendee)5 Gold Passes: $6000 ($1200 per attendee)3 Diamond Passes: $4500 ($1500 per attendee)5 Diamond Passes: $7000 ($1400 per attendee)

Big Data & Analytics for Pharma SummitDate: June 10 & 11, 2015Location: Philadelphia, PAVenue: TBD

F TI L

Ways to Register

+1 425 992 7918 +1 323 446 7673

Diamond Pass

$1995Access to all sessions, networking events, annual subscription to all

content on the Big Data & Analytics channels via ieOnDemand

$1795Early Bird Price(before April 3)

1 Day Pass

$850Full access to the sessions to your chosen day of the summit, 7 days access to presentations from the

summit via ieOnDemand

7 dayonline access to event materials

On-Demand Pass

$600Unlimited access to presentations from the summit via ieOnDemand,

including presentations, interviews & the ability to contact speakers

Unlimited access to

ieOnDemand

Gold Pass

$1795Access to all sessions, networking

events & unlimited access to presentations from the summit via

ieOnDemand

$1595Early Bird Price(before April 3)

Silver Pass

$1495Access to all sessions &

networking events7 days access to presentations from the

summit via ieOnDemand

$1295Early Bird Price(before April 3)

Pass Prices

Register Here Today!

NAME OF EACH ATTENDEE

TITLE OF EACH ATTENDEE DEPARTMENT

COMPANY INDUSTRY

ADDRESS CITY

STATE/PROVINCE ZIP/POSTAL CODE COUNTRY

EMAIL OF EACH ATTENDEE BUSINESS PHONE NUMBER

1. Delegate Information...

2. Pass Types...Early Bird Pass Options until April 3, 2015

Early Bird Silver: $1295 Attendees ____ Early Bird Gold: $1495 Attendees ____ Early Bird Diamond: $1795 Attendees ____

Regular Pass Options after April 3, 2015 Silver Pass: $1495 Attendees ____ Gold Pass: $1795 Attendees ____ Diamond Pass: $1995 Attendees ____

Group Discount Pass Options 3 Silver Passes $3000 ($1000 per attendee) 5 Silver Passes $4500 ($900 per attendee) 3 Gold Passes $3900 ($1300 per attendee) 5 Gold Passes $6000 ($1200 per attendee) 3 Diamond Passes $4500 ($1500 per attendee) 5 Diamond Passes $7000 ($1400 per attendee)

For larger groups or special requests contact Sean Foreman by calling +1 (415) 692 5514 or email sforeman@theiegroup.comGroup passes only available when all participants register together.

Pass Descriptions:Silver Pass: Access to all sessions & networking eventsGold Pass: Access to all sessions, networking events & unlimited access to ieOnDemandDiamond Pass: Access to all sessions, networking events, annual subscription to all content on the Big Data & Analytics channels via ieOnDemand

Check (Make checks payable to The Innovation Enterprise Ltd) Invoice me

Visa Mastercard American Express Diners Club Discover

CARD NUMBER EXPIRATION DATE SECURITY NO.

CARDHOLDERS NAME CARDHOLDER’S SIGNATURE

BILLING ADDRESS -(same as above) INDUSTRY

Prices are exclusive of VAT. Places are transferable without any charge to another Summit occurring within 12 months of the original purchase. Team discounts are applicable at the point of registration only. Any cancellations within a group registration will in turn incur an increase in registration fee for the remaining group participants. Cancellations before May 04, 2015 incur an administrative charge of 50%. If you cancel your registration after May 04, 2015 you will be charged the full fee. You must notify The Innovation Enterprise in writing of a cancellation, or you will be charged the full fee. The Innovation Enterprise reserve the right to make changes to the program without notice. NB: FULL PAYMENT MUST BE RECEIVED BEFORE THE EVENT.

Registration FormBig Data & Analytics for Pharma SummitJune 10 & 11, 2015 | Philadelphia | PAFor registration or more information on the program, please call Sean on +1 (415) 692 5514, or fax this registration form to +1 (323) 446 7673

3. Payment Options...

Schedule

Networking Drinks 17.00 - 19.00

June 11

Session One 08.30 - 10.00

Coffee Break 10.00 - 10.30

Session Two 10.30 - 12.00

Lunch 12.00 - 13.30

Session Three 13.30 - 15.00

Coffee Break 15.00 - 15.30

Session Four 15.30 - 17.00

Day Two

June 10Day One 08.30

10.00

10.30

12.00

13.30

15.00

15.30

17.00

19.00

08.30

10.00

10.30

12.00

13.30

15.00

15.30

17.00

Session Five 08.30 - 10.00

Coffee Break 10.00 - 10.30

Session Six 10.30 - 12.00

Lunch 12.00 - 13.30

Session Seven 13.30 - 15.00

Coffee Break 15.00 - 15.30

Session Eight 15.30 - 17.00

Partnership Opportunities: Giles Godwin-Brown | ggb@theiegroup.com | +1 415 692 5498Attendee Invitation: Sean Foreman | sforeman@theiegroup.com | +1 415 692 5514

NovemberBig Data & Analyticsfor PharmaNovember 5 & 6, Philadelphia

Big Data & Marketing Innovation SummitNovember 6 & 7, Miami

Big Data for Finance November 12 & 13, Boston

Data Science Innovation Summit November 12, Chicago

Data VisualizationSummit November 12 & 13, London

Chief Data Officer Summit November 12 & 13, London

Big Data & Analytics Innovation SummitNovember 27 & 28, Beijing

DecemberBig Data & Analytics in Banking Summit December 3 & 4, New York

Chief Data Officer Summit December 3 & 4, New York

OctoberBig Data & Analytics Innovation SummitOctober 15 & 16, Dubai

JanuaryBig Data Innovation SummitJanuary 22 & 23, Las Vegas

FebruaryData Science InnovationSummitFebruary 18, San Diego

Hadoop InnovationSummitFebruary 19 & 20, San Diego

The Digital Oilfield Innovation SummitFebruary 20 & 21, Buenos Aires

Big Data & Analytics Innovation Summit February 27 & 28, Singapore

JuneBig Data Innovation Summit June 4 & 5, Toronto

Big Data & Analytics for PharmaJune 11 & 12, Philadelphia

Open Data Innovation SummitJune 11 & 12, Boston Big Data & Analytics for Retail SummitJune 19 & 20, Chicago

MayBig Data Innovation SummitMay 14 & 15, London

Big Data & Analytics in Healthcare May 14 & 15, Philadelphia

Chief Data Officer SummitMay 21 & 22, San Francisco

SeptemberBig Data & Analytics Innovation SummitSeptember 17 & 18, Sydney

Data Visualization SummitSeptember 25 & 26, Boston

Big Data Innovation SummitSeptember 25 & 26, Boston

Big Data

Women

Finance

CXO Healthcare

Expected

Flagship

Government

High Tech Pharma

Oil & GasHadoop

MarchBig Data Innovation SummitMarch 27 & 28, Hong Kong

2014 Calendar

AprilBig Data Innovation SummitApril 9 & 10, Santa Clara

Big Data Infrastructure SummitApril 9 & 10, Santa Clara

Data Visualization SummitApril 9 & 10, Santa Clara

October Continued

Big Data & Analytics Innovation Summit October 16 & 17, London

JanuaryBusiness Analytics Innovation Summit January 22 & 23, Las Vegas

MarchHR & Workforce Analytics InnovationMarch 19 & 20, London

Sports Analytics Innovation SummitMarch 26 & 27, London

Predictive AnalyticsInnovation SummitMarch 27 & 28, Hong Kong

FebruaryData Science Innovation SummitFebruary 18, San Diego

Predictive Analytics Innovation Summit February 19 & 20, San Diego

Big Data & Analytics Innovation Summit February 27 & 28, Singapore

May Continued

Predictive Analytics Innovation Summit May 14 & 15, London

Digital & Web Analytics Innovation May 14 & 15, London

Big Data & Analytics in Healthcare May 14 & 15, Philadelphia

Business Intelligence Innovation Summit May 21 & 22, Chicago

HR & Workforce Analytics Innovation May 21 & 22, Chicago

Business Analytics Innovation Summit May 21 & 22, Chicago

Manufacturing Analytics Innovation SummitMay 21 & 22, Chicago

Big Data & Advanced Analytics in Government May 21 & 22, Washington, DC

Partnership Opportunities: Giles Godwin-Brown | ggb@theiegroup.com | +1 415 692 5498Attendee Invitation: Sean Foreman | sforeman@theiegroup.com | +1 415 692 5514

SeptemberSports Analytics Innovation September 10 & 11, Boston

Big Data & Analytics Innovation SummitSeptember 17 & 18, Sydney

Analytics

JuneBig Data & Analytics for PharmaJune 11 & 12, Philadelphia

Big Data & Analytics for Retail SummitJune 19 & 20, Chicago

Customer Analytics Innovation SummitJune 19 & 20, Chicago

MaySocial Media & Web Analytics Innovation SummitMay 1 & 2, San Francisco

Sentiment Analysis Summit May 1 & 2, San Francisco

Gaming Analytics Summit May 1 & 2, San Francisco

OctoberBig Data & Analytics Innovation Summit October 16 & 17, London

NovemberSports PerformanceInnovation Summit November 5 & 6, Manchester

Big Data & Analytics for PharmaNovember 5 & 6, Philadelphia

Social Data Innovation November 6 & 7, Miami

Business Intelligence Innovation SummitNovember 12 & 13, Chicago

Predictive Analytics Innovation Summit November 12 & 13, Chicago

Data Science Leadership Summit November 12, Chicago

Big Data & Analytics Innovation Summit November 27 & 28, Beijing

Retail HR

HealthcareExpected Attendees

BankingFlagship Summit

Sports Social Media

2014 Calendar

Previous Sponsors

For more information, contact us at ggodwinbrown@theiegroup.com F TI L Exhibitor Exhibitor

Workshop Sponsor

Silver Sponsor Bronze Sponsor