Nate Silver Keynote Speaker - Nate Silver

Nate Silver

Author, FiveThirtyEight.com

Founder, FiveThirtyEight.com, Author, “The Signal And The Noise.” Fast Company’s No. 1 “Most Creative People in Business.” Mr. Silver will speak on “Powerful Predictions Through Data Analytics.” Nate Silver has become today’s leading statistician through his innovative analyses of political polling. He first gained national attention during the 2008 presidential election, when he correctly predicted the result of the primaries and the presidential winner in 49 states. In 2012, he called 50 of the 50 states.


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Keynote Speaker - Ric Elias

Ric Elias

CEO, Red Ventures

Red Ventures CEO and passenger on US Airways flight 1549, the “Miracle on the Hudson,” discusses the convergence of luck and data in business and in life.


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Tim Guerry Keynote Speaker - Tim Guerry

Tim Guerry

Retail & Preferred Technology and GWIM T&O Data Quality & Control Executive, Bank of America

The concept of the data-driven enterprise has become a client expectation and a business imperative.  The future belongs to those who are able to unleash fresh insights that sit dormant inside rich data sources.  Case in point, Amazon now derives 35% of its sales from highly relevant suggestions.  Or consider Netflix, which made a successful $100 million bet on House of Cards based on detailed viewing, director, actor and subject matter data from 30+ million usersBut insights will only be as good as the data from which they are derived.  Accuracy and timeliness of data can be much more important than breadth.   

What’s encouraging is a maturing set of technologies that make utilizing rich data to create client and business value possible: data storage is no longer the obstacle it once was, the number of data sources is exploding, and new tools for analysis, data visualization, data management and robust modeling, are available and evolving.

Despite all of this, “getting the data” is still a major impediment for unleashing these powerful insights.  Many of our data supply lines are prisoners of the past—they are overly complex and siloed, batch oriented, dependent on disparate technologies, and require manual upkeep.  Stronger data management practices which could help, sometimes lead to a “right vs. fast” divergence that further complicates the eco-system. 

It’s time to rethink and redesign the entire data supply chain using the technology, tools and techniques now at our disposal.


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Ritika Gunnar Keynote Speaker - Ritika Gunnar

Ritika Gunnar

Vice President, Data and Analytics, IBM


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Keynote Speaker - Analytics Landscape

Vikram Joshi

CEO and Co-Founder, Xcalar

It has been over a decade since the birth of Hadoop. With over $20 billion dollars in venture capital poured into open source technologies for big data, the expectation is that we are well past the hype cycle and have a mature set of products that meet the growing needs of businesses. However, reality speaks otherwise. Demystifying modern data continues to be harder than ever. And it requires programmers, a far cry from the promise of data democratization and self-service analytics. Should we have hope? What does the future hold?


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Keynote Speaker - Analytics Landscape

Rebecca Ye

COO and Co-Founder, Xcalar

It has been over a decade since the birth of Hadoop. With over $20 billion dollars in venture capital poured into open source technologies for big data, the expectation is that we are well past the hype cycle and have a mature set of products that meet the growing needs of businesses. However, reality speaks otherwise. Demystifying modern data continues to be harder than ever. And it requires programmers, a far cry from the promise of data democratization and self-service analytics. Should we have hope? What does the future hold?


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Break Out Sessions VII

Nitin Agrawal

Managing Director, Banking and Securities, Deloitte Consulting

As financial institutions seek to grow revenues and improve customer experience through Omni channel interactions and transactions, they open themselves up to new, more sophisticated fraudulent and criminal activities. In addition, stricter regulations and  increasing scrutiny on anti-money laundering and suspicious activity reporting require robust and trusted fraud detection capabilities

Fraud detection systems in most banks have traditionally been reactive in nature, with suspicious transactions being investigated and analyzed after the fact, offering very little ‘real’ protection from fraud. However, with emerging trends in analytics techniques and data architecture, banks and financial institutions are seeing significant opportunities to modernize their fraud detection and management functions. Predictive modeling are replacing transactional rule based detection engines to score and detect potential fraudulent transactions. Advances in storage architecture is enabling use of full historical data sets instead of samples to greatly improve model accuracy. In addition, banks are looking to incorporate machine learning into fraud detection models to continuously adapt to fast changing environments and behaviors

Our presentation will provide an overview of next generation data architecture patterns that overcome the limitations of traditional fraud management systems and enable more proactive, accurate and nimble fraud management functions in banks.


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Break Out Sessions VIII

Nitin Agrawal

Managing Director, Banking and Securities, Deloitte Consulting

The payments industry is undergoing significant disruption. Billions of transactions are flowing through the payments ecosystem creating huge volumes of data - this is one industry that really knows where the individuals and businesses are spending!! The industry is eager to leverage this data not only for driving its own growth and efficiency but also provide insight and value to other industries.

Innovations in data sciences and technology have been a key enabler of this phenomenon. In this session we will explore the key applications of data sciences to the Payments industry and the advances in data sciences that have enabled this application.


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Panel 1: Big Data and Health Analytics: A Job Prospectus

Jennifer Anderson

Executive Director, North Carolina Healthcare Information & Communication (NCHICA)

As the various sectors in the healthcare industry, payers, providers, vendors, etc., continue in the journey to value-based care, they are struggling to identify and fill critical positions.  This panel will focus exclusively on emerging opportunities for data science professionals within the health domain.


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Break Out Sessions V

Chris Beorkrem

UNC Charlotte, School of Architecture

VALSE is a framework for providing access to stored long term, large scale human behavior which allows an expert to view avatars representing real-time motion and also visualizations of motion summaries, all controlled using familiar DVR-style interface. VALSE requires robust algorithms for human tracking and activity analysis from video and usable interfaces for domain experts to query and understand the data. Our system combines tracking and activity recognition methods into an interactive, real- time system for visual data analysis in the study of motion behavior. VALSE combines time line visualization with spatial visualizations linked and open to exploration by ethnographers or other domain experts. A critical part of the VALSE interface is the methods of identifying meaningful behavior as the intersection of time, people and objects; the system can then search for all incidents of this behavior over very long time periods. This approach leverages the ability of human ethnographers to recognize meaningful patterns of behavior with the computational ability to apply these insights for very extended periods of time.


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Panel 1: Big Data and Health Analytics: A Job Prospectus

Christopher Blanchette

Vice President, Evidence Strategy, Generation & Communication, Precision Health Economics

As the various sectors in the healthcare industry, payers, providers, vendors, etc., continue in the journey to value-based care, they are struggling to identify and fill critical positions.  This panel will focus exclusively on emerging opportunities for data science professionals within the health domain.


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Break Out Sessions IV

Robin Blom

Ball State University

Cardinal Metrics is a student-run media analytics agency housed in the campus newsroom of Ball State University. The organization does web and social media analytics consulting for internal and external clients. One of those clients is Visit Indy, the bureau that promotes the city of Indianapolis with its multi-million dollar budget to attract more business for hotels, restaurants, stores, and entertainment venues. The partnership between Cardinal Metrics and Visit Indy started as an immersive-learning project for a “Media Analytics in Practice” course. A dozen undergraduate students from journalism, public relations, and advertising worked in two teams to advise Visit Indy on its web and social media strategies based on analyses of all available data that the organization continuously gathers through all its channels and platforms. Visit Indy has already implemented several recommendations made by the students to attract more visitors to its website and to create larger name recognition. This presentation will demonstrate how partnerships between higher education and the industry can be highly beneficial for everyone involved—providing students with transformative learning experiences and companies with innovative avenues to increase engagement, exposure, revenue, and profit.


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Break Out Sessions VIII

Ron Bodkin

CTO Architecture and Services, Teradata

Near perfect language translation, better than human image labeling, Natural Language Understanding, dominating humans in strategy games, self-driving cars - What do all these achievements by machines have in common? Deep Learning – driven by significant improvements in Graphic Processing Units and complex computational models inspired by the human brain that excel at capturing structures hidden in massive datasets. These techniques have been pioneered at research universities and Internet behemoths but are now finding their way into the mainstream enterprise through open source tools and hardware offerings, benefiting from a steady decline in cost of building large, parallel models at scale, inspired by unmatched predictive accuracy in many application areas. In this session we will discuss how Deep Learning technology can be integrated into mainstream enterprises to unlock significant business value and transform industries. In this talk we look at how Deep Learning is affecting the enterprise with an emphasis on financial services use cases like fraud detection, mobile personalization based on individual behavior, face recognition for authentication and data center optimization. We dive deep into how a large bank’s existing fraud detection engine was enhanced with deep learning algorithms that analyse tens of thousands of latent features. While the bank’s existing system was effective at blocking fraud, largely based on handcrafted rules created by the business, on intuition and some light analysis, it had a high rate of false positives which created expenses and inconvenience. It had proved increasingly challenging and costly to update and maintain as fraudsters evolved their capabilities with increasing speed. We look at how the business assessed the opportunity to use AI, the use of an agile Analytics Ops approach, and the results of applying AI to detect fraud.


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Rebecca Boyles Break Out Sessions VI

Rebecca Boyles

Program Manager, Bioinformatics, RTI International

Precision health allows the tailoring of medical care to the characteristics of individual patients. It allows preventive, therapeutic and diagnostic interventions to be tailored for those who may be expected to benefit, sparing expense and side effects for those who will not.

How will we accomplish this “precision health?”  A significant share of the power and promise of precision health will come from the collecting and assembling data from disparate human and electronic sources (e.g., genetics, family history, environment, lifestyle), analyzing and understanding the data, and then communicating the data analysis in a way that is actionable (as appropriate) for improved health outcomes.

This panel addresses the challenges and opportunities for precision health from industry and research perspectives on clinical trials, drug development and safety, bioinformatics, and pharmaceutical data analytics.


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Break Out Sessions III

Nicole Bradley

Watson Platform Specialist, IBM Core Watson Platform

Augmenting Intelligence with Watson


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Panel 2: Careers in Big Data Analytics

Anu Brookins

Leader, Digital Process Continuous Improvement Center of Excellence, Cisco Systems, Inc.

The phrase data-driven decision-making has almost become cliché across industries.  What are the critical skills and credentials needed for talent trying to break into Data Science?  Industry leaders from across the spectrum will explore the perceived talent shortage, where are the jobs today and where will they be tomorrow:  Retail, Advertising, Technology, Insurance, Health, Fin Tech, etc.  


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Ned Carroll

Enterprise Data Management and Architecture Executive , Bank of America

Introduction of Nate Silver and Tracy Kerrins


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Break Out Sessions V

Victor Chen, PhD

Professor, UNC Charlotte, Belk College of Business

This 60-minute breakout session is to present the audience with some state-of-the-art knowledge and practice on business strategy analytics by both a practitioner, Mr. Mikael Hagstroem (Partner and COO, McKinsey Analytics), and an academic researcher, Dr. Victor Zitian Chen (Assistant Professor of International Management, Belk College of Business, UNC Charlotte). This session will focus on how business strategy can benefit from integrating scientific research and big data to strive for managerial performance. The session will start with Dr. Chen’s introduction of the background of the session and the keynote speaker. Drawing on his role as COO of McKinsey Analytics and formerly Executive VP of SAS, Mr. Hagstroem will give a keynote speech on how to create a data-driven culture within an organization. He will focus on which companies are renowned for being data-driven and what attributes make them truly distinctive. He will also discuss the risks and how you mitigate those risks. After that, Dr. Chen will give a presentation on how academic research based on science-based principles and rigor can enhance analytics in business decisions. He will showcase two of his working projects as examples: one on performance measurement system of corporations that enhance the benefits for all stakeholders; another on entry mode choice to enhance post-entry performance in cross-border M&As. The last 20 minutes will be devoted to a genuine interaction among the speakers and the audience, focusing on how business and academic researchers can collaborate to improve each other’s work, and identifying gaps in the current research in business analytics.


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Break Out Sessions III

Melissa Coates

Business Intelligence Architect, SentryOne

Join us for a discussion of strategies and architecture options for implementing a modern data warehousing environment. We will explore advantages of augmenting an existing data warehouse investment with a data lake, and ideas for organizing the data lake for optimal data retrieval. We will also look at situations when federated queries are appropriate for employing data virtualization. This is an intermediate session suitable for attendees who are familiar with data warehousing fundamentals.


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Break Out Sessions I

Kent Colling

Managing Director, BluEyeQ, LLC.

“How do we get the data?” Invariably this is the first question asked by plant operators. According to IMS Research "85% of the billions of sensors and data access points needed to monitor machines exist today, but aren't being accessed." In the Industrial Internet of Things (IIoT) market we tout the value of the cloud and big data analytics, but often the reality is true insight occurs at the machine itself. In this session we present practical IIoT deployment examples, challenges, tradeoffs made, and machine health foresight gained through distributed data analytics. Distributed Analytics (FOG computing) puts data processing and continuous learning, machine intelligence at the edge of the network, in effect, breathing self-diagnosing life into the machine.


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Break Out Sessions VIII

Richard Cooperstein

Director, Model Risk Management, Andrew Davidson & Co.

There was a time when developing, approving and using models was like art appreciation—especially for longer-term financial instruments without readily observable prices. Model builders would convene an exhibition for interested parties to critique results. If the graphs looked good the models got used; and they might be adjusted over time so results continued to look good. However, the Big Questions below suggest that the stakes are too high for an artsy process. In recognition, OCC Bulletin 2011-12 provides comprehensive guidance on model risk management, expanding on OCC 2000-16 that focused on model validation. Validating models is now viewed as one component of a more complete process to manage all phases of model risk that particularly include soundness, usage and governance.


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Panel 1: Big Data and Health Analytics: A Job Prospectus

Patrick Curry

Global Commercial Leader: Clinical Data and Analytics, IBM Watson Health: Explorys

As the various sectors in the healthcare industry, payers, providers, vendors, etc., continue in the journey to value-based care, they are struggling to identify and fill critical positions.  This panel will focus exclusively on emerging opportunities for data science professionals within the health domain.
 


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Break Out Sessions I

Anil Daniel

Software Manager, Cisco Systems, Inc.

We would like to present the Data Analytics platform architecture and use cases built for Cisco's eCommerce where 48 Billion USD worth of transactions get processed. This platform will enable prescriptive business insights and recommendations within the commerce transaction(deals and orders) workflow. This will also be used to make data driven decisions in the New Product Introductions process as Cisco works on new Offers in addition to key commerce KPIs. The platform uses Cassandra, Spark, Scoop, Solr, Kafka and Scala built using the DataStax Distribution. We would be excited to share how we are helping achieve these goals for Cisco.


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Break Out Sessions V

Peter Darragh

Mariner

"Microsoft’s “Internet of Things” platform is used by many companies but most of the case-studies understandably concentrate on publicly-owned global giants and ignore how the platform can be utilized by smaller organizations. Using the experiences of four privately held organizations the session confirms that Microsoft’s IoT suite is applicable to a variety of different industries, a variety of applications and a variety of pre-existing technology assets. By reviewing the experience of those organizations the session describes some common traits and provides general advice on how smaller organizations can succeed with Microsoft’s IoT platform."


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Panel 2: Careers in Big Data Analytics

Michael Dulin

Chief Medical Officer, Tresata

The phrase data-driven decision-making has almost become cliché across industries.  What are the critical skills and credentials needed for talent trying to break into Data Science?  Industry leaders from across the spectrum will explore the perceived talent shortage, where are the jobs today and where will they be tomorrow:  Retail, Advertising, Technology, Insurance, Health, Fin Tech, etc.  


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Break Out Sessions VII

Michael Dulin

Chief Medical Officer, Tresata

This session will discuss a novel framework for the implementation of population and public health initiatives driven by data and best evidence. In addition, future directions for the advancement of healthcare delivery will be covered including a review of patient centered approaches to care and the application of next generation big data/analytics software to create patient engaged healthcare delivery models.


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jake finkelstein Break Out Sessions V

Jake Finkelstein

Method Savvy

This workshop will dig into how data-driven marketing improves results and enhances the accountability of the marketing department to the C-suite. The first part of the session will explore how qualitative and quantitative data can be gathered and used to inform marketing strategy and tactics; using case study examples and resources to guide practical application of the methodology. The second half will be a hands on learning session where your real-world questions will be answered by Jake.


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Break Out Sessions IV

Leonard Fishman

data.world

Open data can help us rise to humanity's toughest challenges, but only if we maximize its network effect. To do that, we need both data producers and data users to work collaboratively to create metadata-rich datasets. 18M open datasets exist today, and growth is accelerating. Data producers are uniquely positioned to create foundational metadata that can make open data more accessible. This includes establishing clear provenance, providing a data dictionary, identifying relationships that resolve ambiguity and irregularities between associations within the data, and documenting granular attribute-level information. Data producers cannot anticipate every use of a dataset, especially as data frequently has uses in other sectors and industries. As data users – both humans and computers – normalize, extract meaning, and identify correlations between datasets within their industries, they are creating additional metadata. However, this work too often resides in silos. It is used for one project, then lost forever, only to be repeated from scratch by the next person to touch the data. If data producers published their data using common taxonomies and architectures and if data users had a platform on which they could discuss a dataset, publish their analysis and queries, and in general enhance metadata, data would pass through the complete metadata maturity model and data’s network effect could be unlocked.


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Break Out Sessions V

Mikael Hagstroem

Partner and COO, McKinsey Analytics

This 60-minute breakout session is to present the audience with some state-of-the-art knowledge and practice on business strategy analytics by both a practitioner, Mr. Mikael Hagstroem (Partner and COO, McKinsey Analytics), and an academic researcher, Dr. Victor Zitian Chen (Assistant Professor of International Management, Belk College of Business, UNC Charlotte). This session will focus on how business strategy can benefit from integrating scientific research and big data to strive for managerial performance. The session will start with Dr. Chen’s introduction of the background of the session and the keynote speaker. Drawing on his role as COO of McKinsey Analytics and formerly Executive VP of SAS, Mr. Hagstroem will give a keynote speech on how to create a data-driven culture within an organization. He will focus on which companies are renowned for being data-driven and what attributes make them truly distinctive. He will also discuss the risks and how you mitigate those risks. After that, Dr. Chen will give a presentation on how academic research based on science-based principles and rigor can enhance analytics in business decisions. He will showcase two of his working projects as examples: one on performance measurement system of corporations that enhance the benefits for all stakeholders; another on entry mode choice to enhance post-entry performance in cross-border M&As. The last 20 minutes will be devoted to a genuine interaction among the speakers and the audience, focusing on how business and academic researchers can collaborate to improve each other’s work, and identifying gaps in the current research in business analytics.


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doug hague Panel 2: Careers in Big Data Analytics

Doug Hague

Chief Analytics Officer, Bank of America Merchant Services

The phrase data-driven decision-making has almost become cliché across industries.  What are the critical skills and credentials needed for talent trying to break into Data Science?  Industry leaders from across the spectrum will explore the perceived talent shortage, where are the jobs today and where will they be tomorrow:  Retail, Advertising, Technology, Insurance, Health, Fin Tech, etc.  


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Sara Imoff Break Out Sessions VI

Sara Imhof

Senior Director, Precision Medicine, NC Biotechnology Center

Precision health allows the tailoring of medical care to the characteristics of individual patients. It allows preventive, therapeutic and diagnostic interventions to be tailored for those who may be expected to benefit, sparing expense and side effects for those who will not.

How will we accomplish this “precision health?”  A significant share of the power and promise of precision health will come from the collecting and assembling data from disparate human and electronic sources (e.g., genetics, family history, environment, lifestyle), analyzing and understanding the data, and then communicating the data analysis in a way that is actionable (as appropriate) for improved health outcomes.

This panel addresses the challenges and opportunities for precision health from industry and research perspectives on clinical trials, drug development and safety, bioinformatics, and pharmaceutical data analytics.


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Break Out Sessions VII

Frank Jacobus

Principal SILO AR+D, Associate Professor and 21st Century Chair of Building Technologies in the Fay Jones School of Architecture at the University of Arkansas

SILO AR+D principals Marc Manack (assistant professor of architecture, UNCC) and Frank Jacobus (associate professor and 21st century chair of building technologies, University of Arkansas) will discuss how their architecture, research and design practice uses quantitative data and demographic statistics to not only author designs, but to understand and interpret the cultural field of architecture. They will present three projects during the session. The first is a system of memorials that bring awareness to gun violence whose design and construction is automated from data generated in the wake of the shooting incidents. The session will conclude with the presentation of two research publications, Archi-graphic, and The Visual Biography of Color that use data visualization to reveal hidden phenomena that shape architecture, design, and aesthetic culture.


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Shikha Kashyap Break Out Sessions II

Shikha Kashyap

CTO, Syntelli Solutions, Inc.

Breakout Session Description: When building an enterprise data strategy, consider “why?” before "how". Data is the most powerful, yet underutilized and poorly managed organizational asset. What are the best practices for developing the data roadmap. Take baby steps and quick wins as part of putting together the data roadmap. It is really easy to get lost in the technology stack evaluation before data goals are established within the organization.


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Keynote Speaker - Nate Silver

Tracy Kerrins

Global Wholesale Banking and Credit Technology Executive , Bank of America

Tracy will be engaging Nate in conversation about a wide range of topics in the Fireside Chat portion of his keynote address.


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Break Out Sessions I

Harsha Konduri

Lead AI & Machine Learning Product Strategy - Southeast, Microsoft

The 'Cloud' is finally making Machine Learning and Artificial Intelligence practical. The session explores the fundamental tenants of why Cloud Computing changes the way enterprises should theorize, architect and develop Artificial Intelligence and Machine Learning solutions. The advent of GPUs for Deep Learning, the "Uber" & "Airbnb" model of renting compute cycles and sophisticated Cognitive APIs developed by 'hyper-scale' Cloud companies that have massive ($1B+) consumer internet properties give the cloud an unfair advantage for ML & AI. Finally, the session will arm students, technologists and business leaders with a new lens of looking at Advanced Analytics in the new 'Cloud' world order.


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Matthias Krebs Break Out Sessions III

Matthias Krebs

Novant Health

The presentation takes the audience on a 2 year journey in which insights & analytics transformed how decisions are being made at a large healthcare system. The new fact-based approach resulted in an award winning TV advertising campaign and a drastic ROI improvement. Both causing executives to see marketing no longer as a cost center but instead as a profit center.


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Break Out Sessions IV

Sarah Kruse

Professor, Computer Information Science, Minnesota State University

Big Data is often associated with big organizations, but what happens when a small organization is faced with requirements to capture, use, and share data?  There are many untold stories about the successes and challenges faced by community health centers since the 2004 mandate for healthcare organizations implement electronic health records.  This presentation tells one of those stories.  Participants will gain an understanding of the federal reporting requirements for FQHCs, learn about goals to better understand patient clinical outcomes, costs, trends, and utilization patterns, and hear about organizational successes and challenges to achieving data informed decision making related to population health and reducing health disparities.


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Break Out Sessions VIII

Shiva Kumar

Director, Data Science, Metlife Retail Group (Brighthouse Financial)

Many companies are aspiring to stay ahead with use of Advanced Analytics. While the popularity and buzz around analytics has helped with initial adoption companies are struggling to find the right model to grow and accelerate the use of Analytics, and prove the value within their business. The presentation provides an overview of the journey at Brighthouse Financial (previously Metlife Retail) with Advanced Analytics group since its inception a couple of years back. The Data Science group is growing in size and influence and has identified key factors for success which they intend to share through some examples and stories.


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Break Out Sessions V

Donna Lanclos

Associate Professor, Anthropological Research, UNC Charlotte

VALSE is a framework for providing access to stored long term, large scale human behavior which allows an expert to view avatars representing real-time motion and also visualizations of motion summaries, all controlled using familiar DVR-style interface. VALSE requires robust algorithms for human tracking and activity analysis from video and usable interfaces for domain experts to query and understand the data. Our system combines tracking and activity recognition methods into an interactive, real- time system for visual data analysis in the study of motion behavior. VALSE combines time line visualization with spatial visualizations linked and open to exploration by ethnographers or other domain experts. A critical part of the VALSE interface is the methods of identifying meaningful behavior as the intersection of time, people and objects; the system can then search for all incidents of this behavior over very long time periods. This approach leverages the ability of human ethnographers to recognize meaningful patterns of behavior with the computational ability to apply these insights for very extended periods of time.


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Break Out Sessions I

Joel Lieser

L2Bedford, Inc.

* Immutability Changes Everything * Collect and store as much of the data generated by your organization as possible * Ubiquitous availability of this data to a multitude of applications and processes * Manage Compute and Storage Independently * Stateless, ephemeral, and horizontally scalable distributed compute * Decoupled, infinite storage * Choose Proven Open Source Solutions; Loosely-Couple Them * Low-risk investments that can be easily swapped * Multi-Tenant Platform * Tolerate and segregate disparate applications having diverse resource/compute demands There are a number of ways to successfully deploy a distributed analytic platform, and this session highlights one of those approaches. It minimizes costs while providing infinite and seamless scalability to organizations of any size.


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Panel 2: Careers in Big Data Analytics

Patrick Madsen

Director, University Career Center, UNC Charlotte

The phrase data-driven decision-making has almost become cliché across industries.  What are the critical skills and credentials needed for talent trying to break into Data Science?  Industry leaders from across the spectrum will explore the perceived talent shortage, where are the jobs today and where will they be tomorrow:  Retail, Advertising, Technology, Insurance, Health, Fin Tech, etc.


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Break Out Sessions VII

Marc Manack

Principal of SILO AR+D, Assistant Professor of Building Design in the School of Architecture at the UNC Charlotte

SILO AR+D principals Marc Manack (assistant professor of architecture, UNCC) and Frank Jacobus (associate professor and 21st century chair of building technologies, University of Arkansas) will discuss how their architecture, research and design practice uses quantitative data and demographic statistics to not only author designs, but to understand and interpret the cultural field of architecture. They will present three projects during the session. The first is a system of memorials that bring awareness to gun violence whose design and construction is automated from data generated in the wake of the shooting incidents. The session will conclude with the presentation of two research publications, Archi-graphic, and The Visual Biography of Color that use data visualization to reveal hidden phenomena that shape architecture, design, and aesthetic culture.


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Break Out Sessions VIII

Lindsay Marshall

Data Scientist II, Metlife Retail Group (Brighthouse Financial)

Many companies are aspiring to stay ahead with use of Advanced Analytics. While the popularity and buzz around analytics has helped with initial adoption companies are struggling to find the right model to grow and accelerate the use of Analytics, and prove the value within their business. The presentation provides an overview of the journey at Brighthouse Financial (previously Metlife Retail) with Advanced Analytics group since its inception a couple of years back. The Data Science group is growing in size and influence and has identified key factors for success which they intend to share through some examples and stories.


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Break Out Sessions VIII

Dan McGurrin

Director of Executive Education, Poole College of Management, NC State University

This session will provide real life use cases on how Cisco in collaboration with global universities, including North Carolina State University, had implemented an educational model to accelerate the Digitization of Cisco's business processes. Training is in place for employees from Senior Executives to individual contributors, and at each level of the organization, the transformation is yielding tangible outcomes. North Carolina State University, as one of Cisco's key education partners, will also participate in this session to provide their perspective on curriculum development, program delivery, and the path from academic training to business outcomes.


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Shannon McKeen Panel 2: Careers in Big Data Analytics

Shannon McKeen

Consultant, National Consortium for Data Science

The phrase data-driven decision-making has almost become cliché across industries.  What are the critical skills and credentials needed for talent trying to break into Data Science?  Industry leaders from across the spectrum will explore the perceived talent shortage, where are the jobs today and where will they be tomorrow:  Retail, Advertising, Technology, Insurance, Health, Fin Tech, etc.  


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Break Out Sessions VI

Shannon McKeen

National Consortium for Data Science


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Break Out Sessions VI

Alan Menius

Chief Data Scientist, HAP Innovations

Precision health allows the tailoring of medical care to the characteristics of individual patients. It allows preventive, therapeutic and diagnostic interventions to be tailored for those who may be expected to benefit, sparing expense and side effects for those who will not.

How will we accomplish this “precision health?”  A significant share of the power and promise of precision health will come from the collecting and assembling data from disparate human and electronic sources (e.g., genetics, family history, environment, lifestyle), analyzing and understanding the data, and then communicating the data analysis in a way that is actionable (as appropriate) for improved health outcomes.

This panel addresses the challenges and opportunities for precision health from industry and research perspectives on clinical trials, drug development and safety, bioinformatics, and pharmaceutical data analytics.


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Break Out Sessions I

Atif Mohammad

UNISYS

The threats, which are real and our Banking and Payment Services are constantly facing as one of diverse high priority attack targets in terms of Computer Network Attacks. Such tactics utilized by Cyber-Bandits have arguably become the greatest ubiquitous medium of the threat to known CIA (confidentiality, integrity and availability) of transaction dealing with financial, retail, corporate and investment banking. Banking is the core for such breaches and the strategic threat to the payment systems and services is at the heart of these criminal activities of an interdependent financial system. This discussion will shed light, how to mitigate such threats and take preventive actions.


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Welcome

Bob Morgan

President and CEO, Charlotte Chamber

Bob will open the conference and welcome everyone to Charlotte.


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Break Out Sessions II

Reza Mousavi, PhD

UNC Charlotte, Belk College of Business

Algorithmic decision making is spreading into our businesses, governments, and schools. While commentators are demarcating their public relevance, algorithms are now making decisions in areas where human judgment and discretion traditionally played a big role. In the city of Chicago, predictive algorithms are being used in a wide range contexts including identifying individuals who would likely commit a violent crime to identifying restaurants that would likely violate health inspection rules to predicting rodent population. Given the widespread use of algorithms in a wide array of contexts, there is a need to ensure that these algorithms are first acting as intended. This refers to a review from a technical perspective, such as meeting performance criteria. The second is ensuring that models comport with our established norms. For example, ensuring that a model does not operate in biased or discriminatory manner. In this session, we provide an overview of our framework for reviewing algorithms and models. Our focus is the machine learning models developed by data scientists. The framework is illustrated using a case study of an algorithm designed by the City of Chicago for food inspection. The result is a review process that considers both the technical aspects of the models as well as how a model comports with our established norms. This can be used by practitioners and policymakers in developing and vetting algorithms.


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Vinay Mummigatti

BPM & RPA Center of Excellence, Bank of America

Vinay will introduce keynote speaker Tim Guerry.


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Break Out Sessions II

Andrew Orso

Red Ventures

As the value of data science continues to be realized and data scientists become harder to find, the need to empower employees to leverage cutting-edge analytical approaches on large amounts of data is critical for continued growth. This talk will explore a business-facing data-science tool that has been developed by the Red Ventures data-science team and how it addresses this challenge. This tool allows non-data-scientists to build and deploy real-time predictive and forecasting models as well as set up anomaly detection on data feeds, with little to no support from data scientists. Specific focus will be given to building organizational support, integration with current systems, and gaining adoption. Red Ventures use cases in the customer acquisition funnel, including customer churn, agent staffing, and sales call coaching, will be used for illustration.


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Deepak Patel

Big Data Architect, Commerce Analytics/Insight, Cisco Systems, Inc.

We would like to present the Data Analytics platform architecture and use cases built for Cisco's eCommerce where 48 Billion USD worth of transactions get processed.

This platform will enable prescriptive business insights and recommendation within the commerce transaction(deals and orders) workflow.  This will also be used to make data driven decisions in the New Product Introductions process as Cisco works on new Offers in addition to key commerce KPIs.

The platform uses Cassandra, Spark, Scoop, Solr, Kafka and Scala built using the DataStax Distribution.  We would be excited to share how we are helping achieve these goals for Cisco.


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Break Out Sessions IV

Kevin Pedde

Red Ventures

Paid Search is a popular digital marketing channel that allows companies to reach audiences based on what they are searching for. When done correctly, paid search can significantly impact strategic initiatives such as customer acquisition and lead generation. At a high level, the task is simple – decide which keywords to bid on and how much at each time to maximize value. However, the complexity of performance by day-of-week, time-of-day, device, demographics, and geography combined with rapidly changing auction dynamics for hundreds of thousands of potential keywords makes this task daunting. This talk will focus on Red Venture’s NCTA award winning technology (http://nctechnology.tumblr.com/post/153222113412/2016-nc-tech-awards-recap) to address this problem, Sematic. Sematic builds tens of thousands of predictive models and places more than half a million distinct bids every day. Specific focus will be given to the underlying data science infrastructure required to support the tool and how analysts interact with it. Specific results from Red Ventures customer acquisition funnel will be used for illustration.


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Marcia Price

Marcia Price

Data Science and Business Analytics Masters of Science, Student, UNC Charlotte

Marcia will be the emcee for Thursday's sessions.


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Break Out Sessions VIII

Tushar Puranik

Managing Director, Banking and Payments, Deloitte Consulting

The payments industry is undergoing significant disruption. Billions of transactions are flowing through the payments ecosystem creating huge volumes of data - this is one industry that really knows where the individuals and businesses are spending!! The industry is eager to leverage this data not only for driving its own growth and efficiency but also provide insight and value to other industries. Innovations in data sciences and technology have been a key enabler of this phenomenon. In this session we will explore the key applications of data sciences to the Payments industry and the advances in data sciences that have enabled this application.


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Break Out Sessions VII

Shashank Rao

Manager, Analytics & Information Management, Deloitte Consulting

As financial institutions seek to grow revenues and improve customer experience through Omni channel interactions and transactions, they open themselves up to new, more sophisticated fraudulent and criminal activities. In addition, stricter regulations and  increasing scrutiny on anti-money laundering and suspicious activity reporting require robust and trusted fraud detection capabilities

Fraud detection systems in most banks have traditionally been reactive in nature, with suspicious transactions being investigated and analyzed after the fact, offering very little ‘real’ protection from fraud. However, with emerging trends in analytics techniques and data architecture, banks and financial institutions are seeing significant opportunities to modernize their fraud detection and management functions. Predictive modeling are replacing transactional rule based detection engines to score and detect potential fraudulent transactions. Advances in storage architecture is enabling use of full historical data sets instead of samples to greatly improve model accuracy. In addition, banks are looking to incorporate machine learning into fraud detection models to continuously adapt to fast changing environments and behaviors

Our presentation will provide an overview of next generation data architecture patterns that overcome the limitations of traditional fraud management systems and enable more proactive, accurate and nimble fraud management functions in banks.


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aish sabbisetty Break Out Sessions VIII

Aish Sabbisetty

Data Scientist II, Metlife Retail Group (Brighthouse Financial)

Many companies are aspiring to stay ahead with use of Advanced Analytics. While the popularity and buzz around analytics has helped with initial adoption companies are struggling to find the right model to grow and accelerate the use of Analytics, and prove the value within their business. The presentation provides an overview of the journey at Brighthouse Financial (previously Metlife Retail) with Advanced Analytics group since its inception a couple of years back. The Data Science group is growing in size and influence and has identified key factors for success which they intend to share through some examples and stories.


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Break Out Sessions V

Eric Sauda

Architect, Professor and Researcher, UNC Charlotte, School of Architecture

VALSE is a framework for providing access to stored long term, large scale human behavior which allows an expert to view avatars representing real-time motion and also visualizations of motion summaries, all controlled using familiar DVR-style interface. VALSE requires robust algorithms for human tracking and activity analysis from video and usable interfaces for domain experts to query and understand the data. Our system combines tracking and activity recognition methods into an interactive, real- time system for visual data analysis in the study of motion behavior. VALSE combines time line visualization with spatial visualizations linked and open to exploration by ethnographers or other domain experts. A critical part of the VALSE interface is the methods of identifying meaningful behavior as the intersection of time, people and objects; the system can then search for all incidents of this behavior over very long time periods. This approach leverages the ability of human ethnographers to recognize meaningful patterns of behavior with the computational ability to apply these insights for very extended periods of time.


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Break Out Sessions VII

Dylan Savage

Concert Pianist, Speaker, Author, Associate Professor of Piano, UNC Charlotte

Dylan Savage, concert pianist and UNC-Charlotte music professor, will give a talk and performance demonstration (using a digital piano) on the parallels between interpreting music and interpreting Big Data. Like single pieces of data, single notes have no meaning unless they are combined into a larger whole. Dr. Savage will demonstrate facets of the interpretive process in music for their possible use in analytics. Some questions he will explore are: What is interpretation? What are some of the many layers in an interpretive process? Are there boundaries in interpretation? How important is skill and perspective in interpretation? Can interpretation be over-manipulated? How much does artistry contribute to interpretation and the end product? What are some of the many layers in an interpretive process? The session will be conducted in an 'edutainment' style -- informative and entertaining.


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Break Out Sessions III

Michael Scully

Chief Product Officer, Passport

Customer centricity is often misunderstood. Changes in customer behavior and enabling technology have given rise to increasing focus on customer centric marketing in recent years, yet transformation of marketing techniques presents a number of challenges. In this talk, Michael Scully discusses the ""what, why, and how"" of customer centricity and how predictive analytics for customer lifetime value (CLV) can improve quantification of return on marketing investment.


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Break Out Sessions VI

Andrew Selbst

Attorney and Academic, Yale, Georgetown University Law Center

Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society. Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm’s use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court.
 
Addressing the sources of this unintentional discrimination will be difficult technically, difficult legally, and difficult politically. In the absence of a demonstrable intent to discriminate, the best legal hope for data mining’s victims would seem to lie in disparate impact doctrine. That hope would largely be in vain. After an overview of American anti-discrimination law, offered through the lens of Title VII’s prohibition of discrimination in employment, it will become possible to understand why the standard approach to discrimination law will be difficult to apply here. These challenges also throw into stark relief the tension between the two major theories underlying anti-discrimination law: anti-classification and anti-subordination. Finding a solution to big data’s disparate impact will require more than best efforts to stamp out prejudice and bias. Rather, it will require new regulatory strategies, some of which may require that we once again reexamine the meanings of "fairness" and "bias."


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Break Out Sessions IV

Nirav Shah

Post-doc Fellow, Oral Medicine, Carolinas Healthcare

Identification of candidate biomarkers is crucial for the development of novel therapies against diseases. Access to the freely available meta-data for various diseases generated in past 15 years using micro-array technology is difficult. In this study, we present a simple web interface for the easy retrieval of gene expression profiles of queried genes from 8 curated databases associated with multiple Rheumatic diseases and various tissue sites. This web-tool allows researchers to retrieve meta-analysis results showing various parameters such as gene names, fold changes and p-values. In our talk, we will highlight major features, specific challenges related to the development of this interface and future directions for further improvement.


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Break Out Sessions VI

Winfred Shaw

Head, Precision Medicine Center of Excellence, QuintilesIMS

Precision health allows the tailoring of medical care to the characteristics of individual patients. It allows preventive, therapeutic and diagnostic interventions to be tailored for those who may be expected to benefit, sparing expense and side effects for those who will not.

How will we accomplish this “precision health?”  A significant share of the power and promise of precision health will come from the collecting and assembling data from disparate human and electronic sources (e.g., genetics, family history, environment, lifestyle), analyzing and understanding the data, and then communicating the data analysis in a way that is actionable (as appropriate) for improved health outcomes.

This panel addresses the challenges and opportunities for precision health from industry and research perspectives on clinical trials, drug development and safety, bioinformatics, and pharmaceutical data analytics.


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Mohamed Shehab

Associate Professor, College of Computing and Informatics, UNC Charlotte

Identification of candidate biomarkers is crucial for the development of novel therapies against diseases. Access to the freely available meta-data for various diseases generated in past 15 years using micro-array technology is difficult. In this study, we present a simple web interface for the easy retrieval of gene expression profiles of queried genes from 8 curated databases associated with multiple Rheumatic diseases and various tissue sites. This web-tool allows researchers to retrieve meta-analysis results showing various parameters such as gene names, fold changes and p-values. In our talk, we will highlight major features, specific challenges related to the development of this interface and future directions for further improvement.


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Break Out Sessions II

Tom Shepherd

Director, Customer Architecture Team, Box


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Break Out Sessions VII

Mark Shilling

Principal, Banking and Securities National Lead, Deloitte Consulting

As financial institutions seek to grow revenues and improve customer experience through Omni channel interactions and transactions, they open themselves up to new, more sophisticated fraudulent and criminal activities. In addition, stricter regulations and  increasing scrutiny on anti-money laundering and suspicious activity reporting require robust and trusted fraud detection capabilities

Fraud detection systems in most banks have traditionally been reactive in nature, with suspicious transactions being investigated and analyzed after the fact, offering very little ‘real’ protection from fraud. However, with emerging trends in analytics techniques and data architecture, banks and financial institutions are seeing significant opportunities to modernize their fraud detection and management functions. Predictive modeling are replacing transactional rule based detection engines to score and detect potential fraudulent transactions. Advances in storage architecture is enabling use of full historical data sets instead of samples to greatly improve model accuracy. In addition, banks are looking to incorporate machine learning into fraud detection models to continuously adapt to fast changing environments and behaviors

Our presentation will provide an overview of next generation data architecture patterns that overcome the limitations of traditional fraud management systems and enable more proactive, accurate and nimble fraud management functions in banks.


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Amaresh Tripathy Panel 1: Big Data and Health Analytics: A Job Prospectus

Amaresh Tripathy

Principal, Advisory Group, PwC

As the various sectors in the healthcare industry, payers, providers, vendors, etc., continue in the journey to value-based care, they are struggling to identify and fill critical positions.  This panel will focus exclusively on emerging opportunities for data science professionals within the health domain.


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Break Out Sessions VI

Amaresh Tripathy

Principal, Advisory Group, PwC

The biggest barrier and risk for analytics and data science in any organization is the inertia to act on the 'insights' of the model. Senior leaders in organizations who have made the technology and people investments are increasingly frustrated by lack of top or bottom line impact - especially when analytics is not the 'silver bullet'. Most of the times the blame is put on the lack of 'data-driven culture' in the organization. The discussion will break down the analytical culture into discrete behaviours and tactics , along with some interesting real world stories of organizations who were able to overcome the inertia and make analytics real. This is based on my experience with more than 50 organizations where I have been involved in driving change through analytics.


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Break Out Sessions VIII

Pamela Webber

Manager, Education Services, Cisco Systems, Inc.

This session will provide real life use cases on how Cisco in collaboration with global universities, including North Carolina State University, had implemented an educational model to accelerate the Digitization of Cisco's business processes. Training is in place for employees from Senior Executives to individual contributors, and at each level of the organization, the transformation is yielding tangible outcomes. North Carolina State University, as one of Cisco's key education partners, will also participate in this session to provide their perspective on curriculum development, program delivery, and the path from academic training to business outcomes.


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