Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Schedule: Data-driven business management sessions

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13:3017:00 Tuesday, 23 May 2017
Location: Capital Suite 12
Level: Intermediate
Scott Kurth (Silicon Valley Data Science), John Akred (Silicon Valley Data Science)
Average rating: ****.
(4.20, 5 ratings)
Big data and data science have great potential for accelerating business, but how do you reconcile the business opportunity with the sea of possible technologies? Data should serve the strategic imperatives of a business—those aspirations that will define an organization’s future vision. Scott Kurth and John Akred explain how to create a modern data strategy that powers data-driven business. Read more.
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11:1511:55 Wednesday, 24 May 2017
Location: Capital Suite 15/16
Level: Intermediate
Jack Norris (MapR Technologies)
Average rating: ***..
(3.80, 5 ratings)
Leading companies are integrating operations and analytics to make real-time adjustments to improve revenues, reduce costs, and mitigate risks. There are many aspects to digital transformation, but the timely delivery of actionable data is both a key enabler and an obstacle. Jack Norris explores how companies from Altitude Digital to Uber are transforming their businesses. Read more.
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14:5515:35 Wednesday, 24 May 2017
Location: Capital Suite 15/16
Level: Non-technical
Duncan Ross (TES Global), Emma Prest (DataKind)
Average rating: ****.
(4.50, 2 ratings)
Since its creation, DataKind has helped charities do some fantastic things with data science through volunteers from the data science community (that's you!). But charities often don't know what to do next. Duncan Ross and Emma Prest share lessons learned from DataKind's projects and outline a data maturity model for doing good with data. Read more.
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16:3517:15 Wednesday, 24 May 2017
Location: Capital Suite 2/3
Level: Non-technical
Jesse Anderson (Big Data Institute)
Average rating: *****
(5.00, 6 ratings)
Early project success is predicated on management making sure a data engineering team is ready and has all of the skills needed. Jesse Anderson outlines five of the most common non-technology reasons why data engineering teams fail. Read more.
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17:2518:05 Wednesday, 24 May 2017
Location: Capital Suite 17
Level: Non-technical
Carme Artigas (Synergic Partners)
Average rating: ****.
(4.08, 13 ratings)
Big data technology is mature, but its adoption by business is slow, due in part to challenges like a lack of resources or the need for a cultural change. Carme Artigas explains why an analytics center of excellence (ACoE), whether internal or outsourced, is an effective way to accelerate the adoption and shares an approach to implementing an ACoE. Read more.
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11:1511:55 Thursday, 25 May 2017
Location: Capital Suite 15/16
Secondary topics:  AI
Level: Non-technical
Martin Goodson (Evolution AI), Andrew Crisp (Dun & Bradstreet)
Average rating: ****.
(4.00, 8 ratings)
Martin Goodson gives a tell-all account of an ultimately successful installation of a deep learning system in an enterprise environment. Andy Crisp then shares insights into the challenges of integrating artificial intelligence systems into real-world business processes. Read more.
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12:0512:45 Thursday, 25 May 2017
Location: Capital Suite 17
Level: Beginner
Paco Nathan (O'Reilly Media)
Average rating: ****.
(4.33, 3 ratings)
O'Reilly recently launched Oriole, a new learning medium for online tutorials that combines Jupyter notebooks, video timelines, and Docker containers run on a Mesos cluster, based the pedagogical theory of computable content. Paco Nathan explores the system architecture, shares project experiences, and considers the impact of notebooks for sharing and learning across a data-centric organization. Read more.
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14:0514:45 Thursday, 25 May 2017
Location: Hall S21/23 (B)
Level: Intermediate
Rumman Chowdhury (Accenture)
Average rating: ***..
(3.00, 1 rating)
Multilevel regression and poststratification (MRP) is a method of estimating granular results from higher-level analyses. While it is generally used to estimate survey responses at a more granular level, MRP has clear applications in industry-level data science. Rumman Chowdhury reviews the methodology behind MRP and provides a hands-on programming tutorial. Read more.
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14:0514:45 Thursday, 25 May 2017
Location: Capital Suite 17
Level: Non-technical
David Martinez Rego (DataSpartan)
Average rating: **...
(2.50, 4 ratings)
The growth of data science as a strategic discipline makes its correct management paramount to the survival of new and traditional businesses that want to compete in a foreseeable data-driven economy. David Martinez Rego shares a set of sound, solid principles that will help increase your effectiveness as a data science manager. Read more.
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16:3517:15 Thursday, 25 May 2017
Location: Capital Suite 17
Level: Intermediate
John Akred (Silicon Valley Data Science)
Average rating: ***..
(3.00, 2 ratings)
Valuing data can be a headache. The unique properties of data make it difficult to assess its overall value using traditional valuation approaches. John Akred discusses a number of alternative approaches to valuing data within an organization for specific purposes so that you can optimize decisions around its acquisition and management. Read more.