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

Schedule: Case studies sessions

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9:00–17:00 Tuesday, 30 April 2019
Data Case Studies
Location: Capital Suite 12
Paco Nathan (derwen.ai), Ganes Kesari (Gramener), Alicia Williams (Google), Semih Kumluk (Turkcell), Simon Moritz (Ericsson), Samuel Cristóbal (Innaxis), Volker Schnecke (Novo Nordisk), Julia Butter (Scout24), Cecilia Marchi (Jakala), Caroline Goulard (Dataveyes), Marc Rind (ADP), Juan Bengochea (Royal Caribbean Cruise Lines), Aaronpal Dhanda (EasyJet )
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions. Read more.
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9:00–17:00 Tuesday, 30 April 2019
Findata Day
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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12:05–12:45 Wednesday, 1 May 2019
Insights from engineering Europe's largest marketing platform for fashion
Location: Capital Suite 12
Secondary topics:  Data Platforms, Retail and e-commerce
Dirk Petzoldt (Zalando SE)
Average rating: ****.
(4.18, 11 ratings)
Dirk Petzoldt shares a case study from Europe’s leading online fashion platform Zalando illustrating its journey to a scalable, personalized machine learning–based marketing platform. Read more.
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14:55–15:35 Wednesday, 1 May 2019
Signal processing, machine learning, and video tell the truth
Location: Capital Suite 12
David Maman (Binah)
Average rating: ***..
(3.67, 3 ratings)
David Maman demonstrates how the combination of a mere few minutes of video, signal processing, remote heart-rate monitoring, machine learning, and data science can identify a person’s emotions, health condition and performance. Financial institutions and potential employers can now analyze whether you have good or bad intentions. Read more.
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16:35–17:15 Wednesday, 1 May 2019
The vindication of big data: How Santander UK uses Hadoop to defend privacy
Location: Capital Suite 12
Secondary topics:  Data preparation, data governance, and data lineage, Financial Services, Security and Privacy
Maurício Lins (everis NTT DATA UK), Lidia Crespo (Santander UK)
Average rating: ****.
(4.50, 4 ratings)
Big data is usually regarded as a menace to data privacy. But with data privacy principles and a customer-first mindset, it can be a game changer. Maurício Lins and Lidia Crespo explain how Santander UK applied this model to comply with GDPR, using graph technology, Hadoop, Spark, and Kudu to drive data obscuring, data portability, and machine learning exploration. Read more.
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11:15–11:55 Thursday, 2 May 2019
Insightful health: Amplifying intelligence in healthcare patient flow execution
Location: Capital Suite 12
Secondary topics:  Health and Medicine
Fabio Ferraretto, Claudia Regina Laselva (Albert Einstein Jewish Hospital)
Average rating: ***..
(3.33, 3 ratings)
Fabio Ferraretto and Claudia Regina Laselva explain how Hospital Albert Einstein and Accenture evolved patient flow experience and efficiency with the use of applied AI, statistics, and combinatorial math, allowing the hospital to anticipate E2E visibility within patient flow operations, from admission of emergency and elective demands to assignment and medical releases. Read more.
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14:05–14:45 Thursday, 2 May 2019
Practicing data science: A collection of case studies
Location: Capital Suite 12
Secondary topics:  AI and machine learning in the enterprise
Rosaria Silipo (KNIME)
Average rating: ****.
(4.40, 10 ratings)
Rosaria Silipo shares a collection of past data science projects. While the structure is often similar—data collection, data transformation, model training, deployment—each required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions. Read more.