Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Schedule: Executive Briefing and best practices sessions

9:00am12:30pm Tuesday, March 26, 2019
Location: 2003
Secondary topics:  AI and machine learning in the enterprise
Joshua Poduska (Domino Data Lab), Kimberly Shenk (NakedPoppy), Mac Steele (Domino Data Lab)
Average rating: ****.
(4.60, 15 ratings)
The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders must deliver measurable impact on an increasing share of an enterprise's KPIs. Joshua Poduska, Kimberly Shenk, and Mac Steele explain how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage. Read more.
9:00am5:00pm Tuesday, March 26, 2019
Location: 2022
Alex Kudriashova (Astro Digital), Jonathan Francis (Starbucks), JoLynn Lavin (General Mills), Robin Way (Corios), June Andrews (GE), Kyungtaak Noh (SK Telecom), Taposh DuttaRoy (Kaiser Permanente), Sabrina Dahlgren (Kaiser Permanente), Craig Rowley (Columbia Sportswear), Ambal Balakrishnan (IBM), Benjamin Glicksberg (UCSF), Patrick Lucey (STATS), Rhonda Textor (True Fit)
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.
1:30pm5:00pm Tuesday, March 26, 2019
Location: 2003
Secondary topics:  AI and machine learning in the enterprise, Ethics, Security and Privacy
Andrew Burt (Immuta), Steven Touw (Immuta), richard geering (Immuta), Joseph Regensburger (Immuta), Alfred Rossi (Immuta)
Average rating: *****
(5.00, 2 ratings)
As ML becomes increasingly important for businesses and data science teams alike, managing its risks is quickly becoming one of the biggest challenges to the technology’s widespread adoption. Join Andrew Bur, Steven Touw, Richard Geering, Joseph Regensburger, and Alfred Rossi for a hands-on overview of how to train, validate, and audit machine learning models (ML) in practice. Read more.
11:00am11:40am Wednesday, March 27, 2019
Location: 2020
Secondary topics:  AI and Data technologies in the cloud, AI and machine learning in the enterprise, Security and Privacy
Mike Olson (Cloudera)
Average rating: ***..
(3.80, 5 ratings)
It's easier than ever to collect data, but managing it securely in compliance with regulations and legal constraints is harder. Mike Olson discusses the risks and the issues that matter most and explains how an enterprise data cloud that embraces your data center and the public cloud in combination can address them, delivering real business results for your organization. Read more.
2:40pm3:20pm Wednesday, March 27, 2019
Location: 2018
Secondary topics:  Data preparation, data governance, and data lineage
John Haddad (Informatica)
Average rating: ****.
(4.60, 5 ratings)
Just like a powerful space telescope that scans the universe, a data catalog scans the data universe to help data scientists and analysts find data, collaborate, and curate data for analytic and data governance projects. John Haddad explains how a data catalog can help you find the data you need and trust for analytic and data governance projects. Read more.
4:20pm5:00pm Wednesday, March 27, 2019
Location: 2020
Secondary topics:  AI and machine learning in the enterprise, Data preparation, data governance, and data lineage
Paco Nathan (derwen.ai)
Average rating: ***..
(3.67, 6 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.
5:10pm5:50pm Wednesday, March 27, 2019
Location: 2020
Secondary topics:  AI and machine learning in the enterprise
Average rating: **...
(2.50, 4 ratings)
How do you decide if you should invest in upskilling business teams? The question is no longer "if" but "when" and "how." Barkha Gvalani shares a framework for developing and delivering analytics training to nontechnical users. Read more.
11:50am12:30pm Thursday, March 28, 2019
Location: 2020
Secondary topics:  Model lifecycle management
David Talby (Pacific AI)
Average rating: ****.
(4.90, 10 ratings)
Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries. Read more.
1:50pm2:30pm Thursday, March 28, 2019
Location: 2020
Secondary topics:  Open Data, Data Generation and Data Networks
Ken Johnston (Microsoft), Ankit Srivastava (Microsoft)
Average rating: ****.
(4.80, 5 ratings)
At the rate data sources are multiplying, business value can often be developed faster by joining data sources rather than mining a single source to the very end. Ken Johnston and Ankit Srivastava share four years of hands-on practical experience sourcing and integrating massive numbers of data sources to build the Microsoft Business Intelligence Graph (M360 BIG). Read more.
2:40pm3:20pm Thursday, March 28, 2019
Location: 2020
Secondary topics:  Security and Privacy
Mark Donsky (Okera), Nikki Rouda (Amazon Web Services)
Average rating: ****.
(4.33, 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.
3:50pm4:30pm Thursday, March 28, 2019
Location: 2020
Secondary topics:  Streaming, realtime analytics, and IoT
Dean Wampler (Lightbend)
Average rating: ****.
(4.33, 6 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.
4:40pm5:20pm Thursday, March 28, 2019
Location: 2020
Secondary topics:  AI and machine learning in the enterprise
Michael Li (The Data Incubator)
Average rating: ***..
(3.75, 4 ratings)
As their data and AI teams scale from one to thousands of employees and the maturity of their analytics capabilities evolve, companies find that the analytics journey is not always smooth. Drawing on experiences gleaned from dozens of clients, Michael Li discusses organizational growing pains and the best practices that successful executives have adopted to scale and grow their team. Read more.