Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Schedule: Data-driven business management sessions

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9:00am5:00pm Tuesday, September 26, 2017
Location: 1E 09
Rose Winterton (Pitney Bowes), Audrey Spencer-Alvarado (Portland Trail Blazers), Amie Elcan (CenturyLink), Sean Power (Repable), Parisa Foster (Play The Future), Nick Selby (CJX, Inc. | Midlothian Police Department), Salema Rice (Allegis Group), Aneesh Karve (Quilt), Derek Ruths (CAI), Kristina Bergman (Integris Software), Natalia Adler (UNICEF HQ), Brandon O'Brien (Expedia, Inc)
In a series of 12 half-hour talks aimed at a business audience, you’ll hear data-themed case studies from household brands and global companies, explaining the challenges they wanted to tackle, the approaches they took, and the benefits—and drawbacks—of their solutions. If you want practical insights about applied data, look no further. Read more.
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9:00am5:00pm Tuesday, September 26, 2017
Location: 1E 07/08
Bradford Cross (DCVC), Robert Passarella (Alpha Features), Jason Morton (Ascendant), Leigh Drogen (Estimize), Bob Levy (Virtual Cove, Inc.), Abraham Thomas (Quandl), Alistair Croll (Solve For Interesting), Robert Passarella (Alpha Features), Vincent-Charles Hodder (Local Logic), Priya Koul (American Express), Tanvi Singh (Credit Suisse), José Ribau (CIBC), Michael Beal (Data Capital Management), Jike Chong (Tsinghua University | Acorns)
Finance is information. From analyzing risk and detecting fraud to predicting payments and improving customer experience, data technologies are transforming the financial industry. And we're diving deep into this change with a new day of data-meets-finance talks, tailored for Strata Data Conference events in the world's financial hubs. Read more.
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1:30pm5:00pm Tuesday, September 26, 2017
Location: 1E 11 Level: Intermediate
John Akred (Silicon Valley Data Science), Heather Nelson (Silicon Valley Data Science)
Average rating: ****.
(4.50, 2 ratings)
John Akred and Heather Nelson share methods and observations from three years of effectively deploying data science in enterprise organizations. You'll learn how to build, run, and get the most value from data science teams and how to work with and plan for the needs of the business. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1A 06/07 Level: Intermediate
Eric Colson (Stitch Fix)
Average rating: ****.
(4.67, 3 ratings)
While companies often use data science as a supportive function, the emergence of new business models has made it possible for some companies to differentiate via data science. Eric Colson explores what it means to differentiate by data science and explains why companies must now think very differently about the role and placement of data science in the organization. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1A 18 Level: Intermediate
Secondary topics:  Financial services
Atul Dalmia (American Express)
Big data decisioning is critical to driving real-time business decisions in our digital age. But how do you begin the transformation to big data? The key is enterprise adoption across a variety of end users. Atul Dalmia shares best practices learned from American Express's five-year journey, the biggest challenges you’ll face, and ideas on how to solve them. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1E 15/16 Level: Intermediate
Mike Driscoll (Metamarkets)
Average rating: ****.
(4.00, 3 ratings)
Most analytics tools in use today provide static visuals that don’t reveal the full, real-time picture. Mike Driscoll shows how to take an interactive approach to analytics. From design techniques to discovering new forms of data exploration, he demonstrates how to put the full power of big data into the hands of the people who need it to make key business decisions. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Location: 1E 10/11
Chris Neumann (500 Startups), Carla Holtze (Parrable), Bradford Cross (DCVC), Kyle Wild (Keen IO), Tasso Argyros (‎ActionIQ)
This panel brings together partners from some of the world’s leading startup accelerators and founders of up-and-coming enterprise data startups to discuss how we can help create the next generation of successful enterprise data companies. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Location: 1E 10/11 Level: Beginner
Secondary topics:  AI, Marketing
Elsie Kenyon (Nara Logics)
Average rating: **...
(2.67, 3 ratings)
Enterprises today pursue AI applications to replace logic-based expert systems in order to learn from customer and operational signals. But training data is often limited or nonexistent, and applying or extrapolating the wrong dataset can be costly to a company's business and reputation. Elsie Kenyon explains how to harness institutional human knowledge to augment data in deployed AI solutions. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1A 18 Level: Intermediate
Brendan Aldrich (Ivy Tech Community College ), Lige Hensley (Ivy Tech Community College )
As the largest community college in the US, Ivy Tech ingests over 100M rows of data a day. Brendan Aldrich and Lige Hensley explain how Ivy Tech is applying predictive technologies to establish a true data democracy—a self-service data analytics environment empowering thousands of users each day to improve operations, achieve strategic goals, and support student success. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1E 12/13
Edd Wilder-James (Google)
Average rating: ****.
(4.00, 2 ratings)
Edd Wilder-James outlines a road map for executives who are beginning to consider their strategies for implementing artificial intelligence in their critical processes. Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Location: 1E 10/11 Level: Non-technical
Secondary topics:  Marketing, Retail
Moderated by:
Hilary Milnes (Glossy)
Panelists:
Karen Moon (Trendalytics), Jared Schiffman (Perch Interactive), Eric Colson (Stitch Fix), Catherine Twist (Xcel Brands (Isaac Mizrahi, C. Wonder, Halston, Judith Ripka))
Average rating: *....
(1.00, 1 rating)
Karen Moon, Jared Schiffman, Eric Colson, and Catherine Twist explore how the retail industry is embracing data to include consumers in the design and development process, tackling the challenges associated with the wealth of sources and the unstructured nature of the data they handle and process and how the data is turned into insights that are digestible and actionable. Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1A 21/22 Level: Beginner
Evan Levy (SAS)
Average rating: *****
(5.00, 5 ratings)
While it's clear organizations need to have a comprehensive data strategy, few have actually developed a plan to improve the access, sharing, and usage of data. Evan Levy discusses the five essential components that make up a data strategy and explores the individual attributes of each. Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1E 10/11 Level: Beginner
David Boyle (MasterClass)
Too many brilliant analytical minds are wasted on interesting but ultimately less-impactful problems. They are stuck in the weeds of the data or the challenges of our day to day. Too few ask what it means to reach for the stars—the big, shiny, business-changing issues. David Boyle explains why you must start asking bigger questions and making a bigger difference. Read more.
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1:15pm1:55pm Thursday, September 28, 2017
Location: 1A 18 Level: Intermediate
Secondary topics:  Media
Michael Li (LinkedIn), Chi-Yi Kuan (LinkedIn)
Average rating: *****
(5.00, 1 rating)
Michael Li and Chi-Yi Kuan offer an overview of the EOI (enable-optimize-innovate) framework for big data analytics and explain how to leverage this framework to drive and grow business in key corporate functions, such as product, marketing, and sales. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1E 10/11 Level: Non-technical
Sander Kieft (Sanoma Media)
Average rating: *****
(5.00, 1 rating)
Sanoma has been running big data as a self-service platform for over five years, mainly as a service for business analysts to work directly on the source data. The road to getting business analysts to directly do their analyses on Hadoop was far from smooth. Sander Kieft explores Sanoma's journey and shares some lessons learned along the way. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1E 12/13
Carme Artigas (Synergic Partners)
Average rating: ****.
(4.80, 5 ratings)
Big data technology is mature, but its adoption by business is slow, due in part to challenges like a lack of resources and the need for a cultural change. Carme Artigas explains why an analytics center of excellence (ACoE), whether internal or outsourced, is an effective way to accelerate adoption and shares an approach to implementing an ACoE. Read more.
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2:55pm3:35pm Thursday, September 28, 2017
Location: 1E 10/11 Level: Non-technical
Jesse Anderson (Big Data Institute)
Average rating: *****
(5.00, 2 ratings)
Early project success is predicated on management making sure a data engineering team is ready and has all of the skills needed. Jesse Anderson outlines five of the most common nontechnology reasons why data engineering teams fail. Read more.
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4:35pm5:15pm Thursday, September 28, 2017
Location: 1A 15/16/17 Level: Intermediate
Secondary topics:  Cloud
Felipe Hoffa (Google)
Average rating: *****
(5.00, 1 rating)
With Google BigQuery anyone can easily analyze the more than five years of GitHub metadata and 42+ terabytes of open source code. Felipe Hoffa explains how to leverage this data to understand the community and code related to any language or project. Relevant for open source creators, users, and choosers, this is data that you can leverage to make better choices. Read more.
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4:35pm5:15pm Thursday, September 28, 2017
Location: 1A 18 Level: Intermediate
Secondary topics:  Architecture
Philip Russom (TDWI: The Data Warehousing Institute)
Philip Russom explains how a data lake can improve the role of Hadoop in data-driven business management. With the right end-user tools, a data lake can enable self-service data practices that wring business value from big data and modernize and extend programs for data warehousing, analytics, data integration, and other data-driven solutions. Read more.
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4:35pm5:15pm Thursday, September 28, 2017
Location: 1E 10/11 Level: Non-technical
Tanya Cashorali (TCB Analytics)
Average rating: *****
(5.00, 1 rating)
Given the recent demand for data analytics and data science skills, adequately testing and qualifying candidates can be a daunting task. Interviewing hundreds of individuals of varying experience and skill levels requires a standardized approach. Tanya Cashorali explores strategies, best practices, and deceptively simple interviewing techniques for data analytics and data science candidates. Read more.