Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Executive Briefing and best practices sessions

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9:0017:00 Tuesday, 30 April 2019
Location: Capital Suite 13
Alistair Croll (Solve For Interesting), Nicolette Bullivant (Santander UK Technology), Charlotte Werger (Van Lanschot Kempen), Daniel First (QuantumBlack), Yiannis Kanellopoulos (Code4Thought), Romi Mahajan (Quantarium), Rashed Iqbal (Investment and Development Office), Martin Leijen (Rabobank / Digital Transformation Office), Tal Doron (GigaSpaces), Alistair Croll (Solve For Interesting), Chris Taggart (OpenCorporates), Jan Novotny (Deutsche Bank)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.
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11:1511:55 Wednesday, 1 May 2019
Location: Capital Suite 12
Jane McConnell (Teradata), Sun Maria Lehmann (Equinor)
Average rating: ****.
(4.00, 9 ratings)
To succeed in implementing enterprise data management in industrial and scientific organizations and realize business value, the worlds of business data, facilities data, and scientific data—which have long been managed separately—must be brought together. Sun Maria Lehmann and Jane McConnell explore the cultural and organizational differences and the data management requirements to succeed. Read more.
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11:1511:55 Wednesday, 1 May 2019
Location: Capital Suite 13
Mick Hollison (Cloudera)
Average rating: ***..
(3.33, 3 ratings)
Managing your data securely is difficult, as is choosing the right machine learning tools and managing models and applications in compliance with regulation and law. Mick Hollison covers the risks and the issues that matter most and explains how to address them with an enterprise data cloud and by embracing your data center and the public cloud in combination. Read more.
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12:0512:45 Wednesday, 1 May 2019
Location: Capital Suite 13
Pete Skomoroch (Workday)
Average rating: ****.
(4.70, 10 ratings)
In the next decade, companies that understand how to apply machine intelligence will scale and win their markets. Others will fail to ship successful AI products that matter to customers. Pete Skomoroch details how to combine product design, machine learning, and executive strategy to create a business where every product interaction benefits from your investment in machine intelligence. Read more.
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14:0514:45 Wednesday, 1 May 2019
Location: Capital Suite 13
Mark Donsky (Okera), Nikki Rouda (Amazon Web Services)
Average rating: ****.
(4.67, 3 ratings)
The implications of new privacy regulations for data management and analytics, such as the General Data Protection Regulation (GDPR) and the upcoming California Consumer Protection Act (CCPA), can seem complex. Mark Donsky and Nikki Rouda highlight aspects of the rules and outline the approaches that will assist with compliance. Read more.
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14:5515:35 Wednesday, 1 May 2019
Location: Capital Suite 13
Paco Nathan (derwen.ai)
Average rating: ****.
(4.14, 7 ratings)
Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa. Read more.
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16:3517:15 Wednesday, 1 May 2019
Location: Capital Suite 13
Ellen Friedman (MapR Technologies)
Average rating: ****.
(4.20, 5 ratings)
A surprising fact of modern technology is that not knowing some things can make you better at what you do. This isn’t just lack of distraction or being too delicate to face reality. It’s about separation of concerns, with a techno flavor. Ellen Friedman outlines five things that best practice with emerging technologies and new architectures can give us ways to not know—and why that’s important. Read more.
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17:2518:05 Wednesday, 1 May 2019
Location: Capital Suite 13
Teresa Tung (Accenture Labs), Jean-Luc Chatelain (Accenture)
Average rating: **...
(2.67, 3 ratings)
How do enterprises scale moving beyond one-off AI projects to making it reusable? Teresa Tung and Jean-Luc Chatelain explain how domain knowledge graphs—the technology behind today's internet search—can bring the same democratized experience to enterprise AI. They then explore other applications of knowledge graphs in oil and gas, financial services, and enterprise IT. Read more.
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11:1511:55 Thursday, 2 May 2019
Location: Capital Suite 13
Brandy Freitas (Pitney Bowes)
Average rating: ****.
(4.71, 7 ratings)
Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. Brandy Freitas walks you through developing context and vocabulary around data science topics to help build a culture of data within your organization. Read more.
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12:0512:45 Thursday, 2 May 2019
Location: Capital Suite 12
Vidya Raman (Cloudera)
Average rating: ****.
(4.62, 8 ratings)
Not surprisingly, there's no single approach to embracing data-driven innovations within any industry vertical. However, some enterprises are doing a better job than others when it comes to establishing a culture, process, and infrastructure that lends itself to data-driven innovations. Vidya Raman explores some key foundational ingredients that span multiple industries. Read more.
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12:0512:45 Thursday, 2 May 2019
Location: Capital Suite 13
Alasdair Allan (Babilim Light Industries)
Average rating: *****
(5.00, 4 ratings)
Alasdair Allan explains why the current age, where privacy is no longer "a social norm," may not long survive the coming of the internet of things, as new smart embedded hardware may cause the demise of large-scale data harvesting. Smart devices will process data at the edge, allowing us to extract insights from the data without storing potentially privacy- and GDPR-infringing data. Read more.
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14:0514:45 Thursday, 2 May 2019
Location: Capital Suite 13
Jack Norris (MapR Technologies)
Average rating: ****.
(4.75, 4 ratings)
Many companies delay addressing core improvements in increasing revenues, reducing costs and risk exposure by tying changes to a to-be-hired data scientist. Drawing on three customer examples, Jack Norris explains how to achieve excellent results faster by starting with domain experience and helping developers and analysts better leverage data with available and understandable analytics. Read more.
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14:5515:35 Thursday, 2 May 2019
Location: Capital Suite 13
Nikki Rouda (Amazon Web Services)
Average rating: ****.
(4.14, 7 ratings)
Nikki Rouda shares key trends in data lakes and analytics and explains how they shape the services offered by AWS. Specific topics include the rise of machine-generated data and semistructured and unstructured data as dominant sources of new data, the move toward serverless, SPI-centric computing, and the growing need for local access to data from users around the world. Read more.
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16:3517:15 Thursday, 2 May 2019
Location: Capital Suite 13
Dean Wampler (Lightbend)
Average rating: *****
(5.00, 4 ratings)
Your team is building machine learning capabilities. Dean Wampler demonstrates how to integrate these capabilities in streaming data pipelines so you can leverage the results quickly and update them as needed and covers challenges such as how to build long-running services that are very reliable and scalable and how to combine a spectrum of very different tools, from data science to operations. Read more.