Strata Hadoop World NY 2016 - Data-driven business Track
Strata Hadoop World NY 2016 has following interestinig talks in its Data-driven business sessions
Data 101 introduces you to core principles of data architecture, teaches you how to build and manage successful data teams, and inspires you to do more with your data through real-world applications. Setting the foundation for deeper dives on the following days of Strata + Hadoop World, Data 101 reinforces data fundamentals and helps you focus on how data can solve your business problems.
Data science that works: Best practices for designing data-driven improvements, making them real, and driving change in your enterprise by Jerry Overton
Join expert Jerry Overton as he explains how to make the business and technical aspects of your data strategy work together for best results.
Developing a modern enterprise data strategy by Colette Glaeser and Edd Wilder-James
How do you reconcile the business opportunity of big data and data science with the sea of possible technologies? Fundamentally, data should serve the strategic imperatives of a business—those key aspirations that define an organization’s future vision. Edd Wilder-James and Colette Glaeser explain how to create a modern data strategy that powers data-driven business.
Making on-demand grocery delivery profitable with data science by Jeremy Stanley
Fifteen years ago, Webvan spectacularly failed to bring grocery delivery online. Speculation has been high that the current wave of on-demand grocery delivery startups will meet similar fates. Jeremy Stanley explains why this time the story will be different—data science is the key.
Creating and evaluating a distance measure by Melissa Santos
Whether we're talking about spam emails, merging records, or investigating clusters, there are many times when having a measure of how alike things are makes them easier to work with (e.g., with unstructured data that isn't incorporated into your data models). Melissa Santos offers a practical approach to creating a distance metric and validating with business owners that it provides value.
Where's the puck headed? by Michael Dauber and Shivon Zilis and Sarah Guo and Matt Witheiler and Sam Pullara
In a panel discussion, top-tier VCs look over the horizon and consider the big trends in big data, explaining what they think the field will look like a few years (or more) down the road. Join us to hear about the trends that everyone is seeing and areas for investment that they find exciting.
The insight-driven business by Brian Hopkins
Uber, Netflix, LinkedIn, Tesla, Stitch Fix, Earnest—the list of digital disruptors using data to steal customers grows every month. But is it just that these firms are data driven? Is because they have smart data scientists and Hadoop? The secret to their success is that these firms go further in order to be insight driven. Brian Hopkins explains what they're doing and how to join them.
A data-first approach to drive real-time applications by Jack Norris
Leading companies that are getting the most out of their data are not focusing on queries and data lakes; they are actively integrating analytics into their operations. Jack Norris reviews three customer case studies in ad/media, financial services, and healthcare to show how a focus on real-time data streams can transform the development, deployment, and future agility of applications.
Architecting for change: LinkedIn's new data ecosystem by Shirshanka Das and Yael Garten
Shirshanka Das and Yael Garten describe how LinkedIn redesigned its data analytics ecosystem in the face of a significant product rewrite, covering the infrastructure changes, such as client-side activity tracking, a unified reporting platform, and data virtualization techniques to simplify migration, that enable LinkedIn to roll out future product innovations with minimal downstream impact.
Winning with data: How ThredUp, Twilio, and Warby Parker use data to build advantage by Daniel Mintz
Daniel Mintz dives into case studies from three companies—ThredUp, Twilio, and Warby Parker—that use data to generate sustainable competitive advantages in their industries.
What Crimean War gunboats teach us about the need for schema registries by Alexander Dean
In 1853, Britain’s workshops built 90 new gunboats for the Royal Navy in just 90 days—an astonishing feat of engineering made possible by industrial standardization. Snowplow's Alexander Dean argues that data-sophisticated corporations need a new standardization of their own, in the form of schema registries like Confluent Schema Registry or Snowplow’s own Iglu.
AI-fueled customer experience: How online retailers are moving toward real-time perception, reasoning, and learning by Rupert Steffner
Today’s online storefronts are good at procuring transactions but poor in managing customers. Rupert Steffner explains why online retailers must build a complementary intelligence to perceive and reason on customer signals to better manage opportunities and risks along the customer journey. Individually managed customer experience is retailers' next challenge, and fueling AI is the right answer.
Breeding data scientists: A four-year study by Danielle Dean and Amy OConnor
At Strata + Hadoop World 2012, Amy O'Connor and her daughter Danielle Dean shared how they learned and built data science skills at Nokia. This year, Amy and Danielle explore how the landscape in the world of data science has changed in the past four years and explain how to be successful deriving value from data today.
CANCELED: How to hire and test for data skills: A one-size-fits-all interview kit by Tanya Cashorali
Given the recent demand for data analytics and data science skills, adequately testing and qualifying candidates can be a daunting task. Interviewing hundreds of individuals of varying experience and skill levels requires a standardized approach. Tanya Cashorali explores strategies, best practices, and deceptively simple interviewing techniques for data analytics and data science candidates.
Corporate strategy: Artificial intelligence or bust by Stephen Pratt
Stephen Pratt, the CEO of Noodle.ai and former head of Watson for IBM GBS, presents a shareholder value perspective on why enterprise artificial intelligence (eAI) will be the single largest competitive differentiator in business over the next five years—and what you can do to end up on top.
Using the explosion of data in the utility industry to prevent explosions in utility infrastructure by Kim Montgomery
With the advent of smart grid technology, the quantity of data collected by electrical utilities has increased by 3–5 orders of magnitude. To make full use of this data, utilities must expand their analytical capabilities and develop new analytical techniques. Kim Montgomery discusses some ways that big data tools are advancing the practice of preventative maintenance in the utility industry.