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

29 April-2 May 2019
London, UK

Alistair Croll, Strata Conference Chair

Make data work for business.

The 2019 Strata Business Summit will give you a thorough understanding of how some of the world’s leading companies build successful data strategies. You’ll discover game-changing technologies and their business applications—and how to move your enterprise forward to bridge the gap. You'll also receive a hand-picked lineup of executive briefings on key issues such as: predictive analytics and machine learning, Cloud strategy, governance security and privacy, IoT, and artificial intelligence, and more.

Strata Business Summit provides the intel you need to build strategies and data-driven business models that deliver customer insight, drive efficiency and innovation in products and services, modernize architecture, reduce costs, and lower risk.

Featured Speakers

Platinum pass holders have access to to Strata Business Summit Mon–Thurs. Gold and Silver pass holders have access to Strata Business Summit on Tues–Thurs. Bronze pass holders have access to Strata Business Summit on Wed–Thurs.

Monday 29 April - Tuesday 30 April: 2-Day Training (Platinum & Training passes)
Tuesday 30 April: Tutorials (Gold & Silver passes)
Wednesday 1 May: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes)
9:00 | Location: Auditorium
Strata Data Conference Keynotes
10:45
Morning break
Thursday 2 May: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes)
9:00 | Location: Auditorium
Strata Data Conference Keynotes
10:45
Morning break
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9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Location: Capital Suite 16
Secondary topics:  AI and machine learning in the enterprise
Nijma Khan (Faculty ai), Alberto Favaro (Faculty)
Average rating: **...
(2.00, 6 ratings)
Nijma Khan and Alberto Favaro offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization. Read more.
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9:0017:00 Tuesday, 30 April 2019
Location: Capital Suite 12
Paco Nathan (derwen.ai), Ganes Kesari (Gramener Inc), 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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9:0017:00 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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13:3017:00 Tuesday, 30 April 2019
Location: Capital Suite 8
Secondary topics:  AI and machine learning in the enterprise
Peter Aiken (Data BluePrint | DAMA International | Virginia Commonwealth University)
Average rating: ***..
(3.71, 7 ratings)
Peter Aiken offers a more operational perspective on the use of data strategy, which is especially useful for organizations just getting started with data 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: *****
(5.00, 3 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
Secondary topics:  AI and Data technologies in the cloud, AI and machine learning in the enterprise, IoT and its applications
Mick Hollison (Cloudera)
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 10/11
Secondary topics:  AI and machine learning in the enterprise, Ethics
Laila Paszti (GTC Law Group PC & Affiliates)
As companies commercialize novel applications of AI in areas such as finance, hiring, and public policy, there's concern that these automated decision-making systems may unconsciously duplicate social biases, with unintended societal consequences. Laila Paszti shares practical advice for companies to counteract such prejudices through a legal- and ethics-based approach to innovation. Read more.
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12:0512:45 Wednesday, 1 May 2019
Location: Capital Suite 12
Secondary topics:  Data Platforms, Retail and e-commerce
Dirk Petzoldt (Zalando SE)
Average rating: ****.
(4.33, 3 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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12:0512:45 Wednesday, 1 May 2019
Location: Capital Suite 13
Secondary topics:  AI and machine learning in the enterprise, Open Data, Data Generation and Data Networks
Pete Skomoroch (Workday/Skipflag/LinkedIn)
Average rating: ****.
(4.50, 2 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 10/11
Secondary topics:  AI and machine learning in the enterprise, Ethics
Duncan Ross (Times Higher Education), Francine Bennett (Mastodon C)
Average rating: *****
(5.00, 4 ratings)
Being good is hard. Being evil is fun and gets you paid more. Once more Duncan Ross and Francine Bennett explore how to do high-impact evil with data and analysis (and possibly AI). Make the maximum (negative) impact on your friends, your business, and the world—or use this talk to avoid ethical dilemmas, develop ways to deal responsibly with data, or even do good. But that would be perverse. Read more.
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14:0514:45 Wednesday, 1 May 2019
Location: Capital Suite 13
Secondary topics:  Data preparation, data governance, and data lineage, Security and Privacy
Mark Donsky (Okera), Nikki Rouda (Amazon Web Services)
Average rating: *****
(5.00, 2 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 10/11
Secondary topics:  Data Platforms, Transportation and Logistics, Visualization, Design, and UX
Average rating: ****.
(4.00, 2 ratings)
Shailesh Chauhan explains how Uber built its business intelligence platform, detailing why the company took a platform approach rather than adding features in a piecemeal fashion. Read more.
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14:5515:35 Wednesday, 1 May 2019
Location: Capital Suite 12
David Maman (Binah)
Average rating: ****.
(4.00, 1 rating)
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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14:5515:35 Wednesday, 1 May 2019
Location: Capital Suite 13
Secondary topics:  AI and machine learning in the enterprise, Data preparation, data governance, and data lineage
Paco Nathan (derwen.ai)
Average rating: ****.
(4.67, 3 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 10/11
Secondary topics:  Visualization, Design, and UX
Brian O'Neill (Designing for Analytics)
Average rating: ****.
(4.33, 3 ratings)
Brian O'Neill explains how design is fundamentally improving the bottom line of business and can help data teams uncover the real problems and needs of customers and business stakeholders. Join in to learn and practice a key aspect of good design: how to properly interview stakeholders and users. Read more.
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16:3517:15 Wednesday, 1 May 2019
Location: Capital Suite 12
Secondary topics:  Data preparation, data governance, and data lineage, Financial Services, Security and Privacy
Maurício Lins (everis consultancy UK), Lidia Crespo (Santander UK)
Average rating: ****.
(4.67, 3 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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16:3517:15 Wednesday, 1 May 2019
Location: Capital Suite 13
Ellen Friedman (MapR Technologies)
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 10/11
Secondary topics:  Visualization, Design, and UX
Mars Geldard (University of Tasmania), Paris Buttfield-Addison (Secret Lab Pty. Ltd.)
Average rating: *****
(5.00, 8 ratings)
Science fiction has been showcasing complex, AI-driven interfaces for decades. As TV, movies, and video games have become more capable of visualizing a possible future, the grandeur of these imagined science fictional interfaces has increased. Mars Geldard and Paris Buttfield-Addison investigate what we can learn from Hollywood UX. Is there a useful takeaway? Does sci-fi show the future of AI UX? Read more.
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17:2518:05 Wednesday, 1 May 2019
Location: Capital Suite 12
Secondary topics:  Ethics
Duncan Ross (Times Higher Education), giselle cory (DataKind UK)
Average rating: ****.
(4.00, 1 rating)
DataKind UK has been working in data for good since 2013, helping over 100 UK charities to do data science for the benefit of their users. Some of those projects have delivered above and beyond expectations; others haven't. Duncan Ross and Giselle Cory explain how to identify the right data for good projects and how this can act as a framework for avoiding the same problems across industry. Read more.
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17:2518:05 Wednesday, 1 May 2019
Location: Capital Suite 13
Secondary topics:  AI and machine learning in the enterprise, Financial Services, Graph technologies and analytics
Teresa Tung (Accenture Labs), Jean-Luc Chatelain (Accenture)
Average rating: ***..
(3.00, 2 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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17:2518:05 Wednesday, 1 May 2019
Location: Capital Suite 17
Jesse Anderson (Big Data Institute)
Average rating: *****
(5.00, 2 ratings)
In this talk, we will cover the most common reasons why data engineering teams fail and how to correct them. This will include ways to get your management to understand that data engineering is really complex and time consuming. It is not data warehousing with new names. Management needs to understand that you can’t compare a data engineering team to the web development team, for example. Read more.
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11:1511:55 Thursday, 2 May 2019
Location: Capital Suite 12
Secondary topics:  Health and Medicine
Fabio Ferraretto (Accenture), Claudia Regina Laselva (Albert Einstein Jewish Hospital)
Average rating: ****.
(4.00, 2 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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11:1511:55 Thursday, 2 May 2019
Location: Capital Suite 13
Secondary topics:  AI and machine learning in the enterprise, Transportation and Logistics
Brandy Freitas (Pitney Bowes)
Average rating: *****
(5.00, 1 rating)
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
Secondary topics:  AI and machine learning in the enterprise
Vidya Raman (Cloudera)
Average rating: *****
(5.00, 2 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
Secondary topics:  IoT and its applications, Security and Privacy
Alasdair Allan (Babilim Light Industries)
Average rating: *****
(5.00, 3 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 12
Secondary topics:  AI and machine learning in the enterprise
Rosaria Silipo (KNIME)
Average rating: ****.
(4.00, 1 rating)
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.
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14:0514:45 Thursday, 2 May 2019
Location: Capital Suite 13
Secondary topics:  AI and machine learning in the enterprise
Jack Norris (MapR Technologies)
Average rating: *****
(5.00, 2 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 12
Secondary topics:  AI and machine learning in the enterprise
Robert Cohen (Economic Strategy Institute)
Robert Cohen discusses the skills that employers are seeking from employees in digital jobs, linked to the new software hierarchy driving digital transformation. Robert describes this software hierarchy as one that ranges from DevOps, CI/CD, and microservices to Kubernetes and Istio. This hierarchy is used to define the jobs that are central to data-driven digital transformation. Read more.
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14:5515:35 Thursday, 2 May 2019
Location: Capital Suite 13
Secondary topics:  AI and Data technologies in the cloud
Nikki Rouda (Amazon Web Services)
Average rating: ****.
(4.25, 4 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
Secondary topics:  Data Integration and Data Pipelines, Streaming and realtime analytics
Dean Wampler (Lightbend)
Average rating: *****
(5.00, 1 rating)
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.