Topics to watch at the Strata Data Conference in New York 2019
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
Data
Our take on the ideas, information, and tools that make data work.
How to become a truly data-driven organization
From basic BI to using AI to automate and augment human endeavors, data-driven systems are increasingly powerful and pervasive in the enterprise.
Iterative competition is a catalyst for better problem solving
Kaggle is more than a machine learning competition platform; it’s a facilitator for efficient problem solving and a community for sharing and learning.
Use technology to do what you do better
Innovation has the power to change industries, but new technology doesn’t always mean you need a new business model.
Enabling end-to-end machine learning pipelines in real-world applications
The O’Reilly Data Show Podcast: Nick Pentreath on overcoming challenges in productionizing machine learning models.
The quest for high-quality data
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge.
Becoming a machine learning company means investing in foundational technologies
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.
Real-time entity resolution made accessible
The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.
Rise of the (advertising) machines
Mike Tidmarsh looks at how data and AI are radically reshaping the world of marketing communications.
Combining creativity and analytics
David Boyle shares lessons on how analysts can harness data and creativity to build partnerships.
Building data science capacity in your organization
Shingai Manjengwa shares insights from teaching data science to 300,000 online learners.
The unstoppable rise of white box data
Chris Taggart explains the benefits of “white box data” and outlines the structural shifts that are moving the data world toward this model.
Privacy, identity, and autonomy in the age of big data and AI
Sandra Wachter argues that a right to reasonable inferences could protect against new forms of discrimination.
Sustaining machine learning in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Finding your North Star
Cait O’Riordan discusses the North Star metric the Financial Times uses across the organization to drive subscriber growth.
The enterprise data cloud
Mick Hollison describes why hybrid and multi-cloud is the future for organizations that want to capitalize on machine learning and AI.
Making the future
James Burke asks if we can use data and predictive analytics to take the guesswork out of prediction.
Making data science useful
Cassie Kozyrkov explains how organizations can extract more value from their data.