Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

DDBD conference sessions

Tuesday, September 29

9:15am–9:40am Tuesday, 09/29/2015
Location: 1 E14/1 E15
Farrah Bostic (The Difference Engine)
Average rating: ***..
(3.42, 12 ratings)
Customers lie. Samples are biased. Respondents engage in and succumb to groupthink. But just because all customers lie doesn't mean these lies aren't useful. The lies that really get in the way of growing a business are the deeply held traditions and beliefs (biases!) within an organization that risk leaving big ships dead in the water, while new, lighter-weight organizations pass them by. Read more.
9:40am–10:05am Tuesday, 09/29/2015
Location: 1 E14/1 E15
Mark Madsen (Teradata)
Average rating: ***..
(3.86, 14 ratings)
The story of the correlation between beer and diaper sales is commonly used to explain product affinities in introductory data mining courses. Rarely does anyone ask about the origin of this story. Is it true? Why is it true? What does true mean anyway? Read more.
10:05am–10:30am Tuesday, 09/29/2015
Location: 1 E14/1 E15
krish venkataraman (Syncsort)
Average rating: **...
(2.93, 14 ratings)
While CFOs have traditionally been seen as a company’s treasurer, technological innovations such as big data are creating a new breed of CFOs, who operate at the intersection of financial services, technology, and big data. Read more.
11:00am–11:20am Tuesday, 09/29/2015
Location: 1 E14/1 E15
Amy O'Connor (Cloudera)
Average rating: ***..
(3.89, 9 ratings)
The approach to successfully using big data is different from that of traditional analytics - it requires people to adopt a new mindset and new ways of working. Amy O’Connor works with Cloudera customers globally in their journeys to ensuring success on their big data initiatives. In this session O’Connor will explore how companies are shifting to a data-driven culture. Read more.
11:20am–11:40am Tuesday, 09/29/2015
Location: 1 E14/1 E15
Jana Eggers (Nara Logics)
Average rating: ****.
(4.00, 11 ratings)
Within the next decade, 16 percent of current US jobs will be done by artificial intelligences. It’s time to start thinking about how we onboard these employees. While we’ll look at what it takes to get started with machine learning projects, our focus will be on the top 5 things you need to consider when your next employee is an AI. Read more.
11:40am–12:10pm Tuesday, 09/29/2015
Location: 1 E14/1 E15
Vincent Dell'Anno (Accenture), Fredrik Backner (Telia Company ), Bill Moschella (Evariant), Florin Trandafir (Nokia)
Average rating: ***..
(3.25, 8 ratings)
Organizations use data – big and small, external and enterprise – along with analytics to fuel transformational change and become value-led, insights-powered enterprises. This panel will explore the obstacles that companies encounter when they use data to transform their organizations, and lessons they’ve learned. Read more.
12:10pm–12:30pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Intermediate
Bill Franks (Teradata Corporation)
Average rating: ***..
(3.80, 5 ratings)
In this talk, Teradata chief analytics officer Bill Franks considers the changes we need to make in order to complete a second industrial revolution centered on the analysis of data. Read more.
1:30pm–1:45pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Non-technical
Jake Kendall (Bill & Melinda Gates Foundation)
Average rating: ***..
(3.25, 8 ratings)
The Financial Services for the Poor (FSP) team at the Foundation works to connect poor people to mobile payment platforms through which they can access digital financial services such as credit, savings, payment, and insurance. This session explores how our team has applied data science in our work, and will highlight some of the opportunities and challenges we see going forward. Read more.
1:45pm–2:05pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Non-technical
Tricia Wang (Constellate Data ), Matt LeMay (Constellate Data)
Average rating: ***..
(3.50, 8 ratings)
As we use data to better understand our customers, how do we make sure that we retain the human dimension of that data -- the stories, language, and irreducible complexity that too often get lost in a “data-driven” world? In this talk, we will share actionable strategies for integrating qualitative and quantitative data, and how these strategies delivered transformative results for O'Reilly. Read more.
2:05pm–2:20pm Tuesday, 09/29/2015
Location: 1 E14/1 E15
Rahel Jhirad (Hearst)
Average rating: **...
(2.62, 8 ratings)
Rahel Jhirad, Director, Data Science Hearst Read more.
2:20pm–2:40pm Tuesday, 09/29/2015
Location: 1 E14/1 E15
Tags: media
Cecile Barbaroux (Schibsted Classified Media)
Average rating: ***..
(3.45, 11 ratings)
In this session, Cecile Barbaroux, head of data and insight, will explain how Schibsted Classified Media is reinventing itself to embrace the opportunities afforded by big data. The global company, once a group of decentralized marketplaces worldwide, is now working to become lean and data-driven. Read more.
2:40pm–3:00pm Tuesday, 09/29/2015
Location: 1 E14/1 E15
Alexander White (Next Big Sound)
Average rating: ***..
(3.89, 9 ratings)
Now is a pertinent time to step back and once again explore the state of the online music industry through the lens of data. How rapidly are we adopting streaming? (Rapid may be an understatement.) Which platforms are we using more frequently to keep tabs on artists? (Pics or it didn’t happen!) What impact does withholding content from any given service have? (I’m looking at you, Taylor.) Read more.
3:30pm–3:50pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Non-technical
Matthew Granade (Domino Data Lab)
Average rating: ***..
(3.64, 11 ratings)
The power of data science lies not in one-off insights but in the ability to capture insights and build upon them systematically. The mechanism for doing so is to have your business process captured in logic, which can be persisted, tested, and improved. Pursuing this broader approach to data science requires different organizational and technological capabilities. I will offer a how-to guide. Read more.
3:50pm–4:10pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Intermediate
Kristi Marotta (Allstate)
Average rating: ***..
(3.43, 7 ratings)
Allstate data evangelists set out on a mission to create an 'encyclopedic' data resource at one of the world's biggest insurance companies, and a single centralized place to go where they could quickly and easily answer data questions. Hear about how their usage of Tableau on top of Cloudera Hadoop has helped them build a culture of analytics for professionals of all skill levels. Read more.
4:10pm–4:30pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Non-technical
Adam Devine (WorkFusion)
Average rating: ***..
(3.75, 8 ratings)
The combination of machine learning and human intelligence is revolutionizing data collection, cleansing, and control in enterprise business. Data operations are shedding confining outsourcing contracts, eliminating fragile and fragmented point solutions like OCR, scrapers, and BPM, and forgoing IT projects in favor of agile cloud services that combine the best of human and machine productivity. Read more.
4:30pm–4:50pm Tuesday, 09/29/2015
Location: 1 E14/1 E15 Level: Intermediate
Gary Short (Microsoft)
Average rating: ****.
(4.14, 7 ratings)
The future of humankind depends on farming greater yields using fewer resources. This talk looks at a European Space Agency-funded project to explore the intersection of farming, big data, and data science. Read more.