Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore

Schedule: Becoming a data-centric company sessions

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9:00am12:30pm Tuesday, December 5, 2017
Location: 310/311 Level: Non-technical
John Akred (Silicon Valley Data Science)
Big data, AI, and data science have great potential for accelerating business, but how do you reconcile business opportunity with the sea of possible technologies? Data should serve the strategic imperatives of a business—those aspirations that will define an organization’s future vision. John Akred explains how to create a modern data strategy that powers data-driven business. Read more.
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9:00am12:30pm Tuesday, December 5, 2017
Location: 323
Alistair Croll (Solve For Interesting), kyungtaak Noh (SK Telecom), Jisung Kim (SK Telecom), Mike Prorock (mesur.io), Hugo Sheng (Qlik), Alexandre Chade (Dotz), Jonathan Seidman (Cloudera), Ted Malaska (Blizzard Entertainment), Mike Koelemay (Sikorsky Aircraft, Lockheed Martin)
In a series of half-hour talks aimed at a business audience, you’ll hear from household brands and global companies as they explain 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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11:15am11:55am Wednesday, December 6, 2017
Location: 321/322 Level: Non-technical
John Akred (Silicon Valley Data Science), Mark Hunter (Sainsburys Bank)
Deploying machine learning in business requires far more than just selecting an algorithm. You need the right architecture, tools, and team organization to drive your agenda successfully. John Akred and Mark Hunter share practical advice on the technical and human sides of machine learning, based on experience preparing Sainsbury’s for its ML-enabled future. Read more.
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12:05pm12:45pm Wednesday, December 6, 2017
Location: 328/329 Level: Non-technical
Jesse Anderson (Big Data Institute)
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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1:45pm2:25pm Wednesday, December 6, 2017
Location: 323 Level: Non-technical
Jessica Chen Riolfi (TransferWise)
Data is essential to unlock growth opportunities, and successful companies use it in every decision. Jessica Chen Riolfi explains how to build an organization with decentralized, data-driven decision making that enables teams to focus on the products and features that matter and ultimately unlock exponential growth. Read more.
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2:35pm3:15pm Wednesday, December 6, 2017
Location: 328/329 Level: Non-technical
Ricky Barron (InfoStrategy)
To many organizations, big data analytics is still a solution looking for a problem. Ricky Barron shares practical methods for getting the best out of your big data analytics capability and explains why establishing an "insights group" can improve the bottom line, drive performance, optimize processes, and create new data-driven products and solutions. Read more.
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4:15pm4:55pm Wednesday, December 6, 2017
Location: 321/322 Level: Beginner
Benjamin Wright-Jones (Microsoft), Simon Lidberg (Microsoft)
As organizations turn to data-driven strategies, they are also increasingly exploring the creation of a data science or analytic center of excellence (COE). Benjamin Wright-Jones and Simon Lidberg outline the building blocks of a center of excellence and describe the value for organizations embarking on data-driven strategies. Read more.
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5:05pm5:45pm Wednesday, December 6, 2017
Location: 328/329 Level: Non-technical
Teresa Tung (Accenture Labs)
A data-driven enterprise maximizes the value of its data. But how do enterprises emerging from technology and organization silos get there? Teresa Tung explains how to create data-driven enterprise maturity models that span technology and business requirements and walks you through use cases that bring the model to life. Read more.
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11:15am11:55am Thursday, December 7, 2017
Location: 321/322 Level: Beginner
Grace Tang (Uber)
Being a data-driven company means that we have to move fast and fail often. But how do we learn to not only be proud of our failures but also turn these fails into wins? Grace Tang explains how to set up experiments so that negative results become epic wins, saving your team time, effort, and money instead of just being swept under the carpet. Read more.
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4:15pm4:55pm Thursday, December 7, 2017
Location: 321/322 Level: Intermediate
Sarang Anajwala (Autodesk)
Autodesk's centralized data platform enables data-driven decision making by democratizing analytics across the various teams based on their personas and proficiencies. Sarang Anajwala explores the various user personas of the big data platform, challenges in enabling them for efficient interactions with big data, and his experience navigating these challenges. Read more.
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4:15pm4:55pm Thursday, December 7, 2017
Location: 328/329 Level: Intermediate
Thomas Dinsmore (Cloudera), Johnson Poh (DBS)
Data science alone is easy. Data science with others, in the enterprise, on shared distributed systems, requires a bit more work. Thomas Dinsmore and Johnson Poh share common technology considerations and patterns for collaboration in large teams and best practices for moving machine learning into production at scale. Read more.
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5:05pm5:45pm Thursday, December 7, 2017
Location: 321/322 Level: Non-technical
Daniel Ng (Cloudera)
Daniel Ng explores the current state of data professional talent in the APAC region and discusses some solutions to expand the profession, including an open source ecosystem for data professional development and a collaboration between Microsoft, Red Hat, Talend, and Cloudera in Malaysia to help realize the target of 20,000 data professionals in 2020. Read more.