Now in its sixth edition, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve examined throughout 2016. This collection of blog posts, authored by leading thinkers and experts in the field, reflects a unique set of themes we’ve identified as gaining significant attention and traction. Our list of topics for 2016 includes: Careers in data Tools and architecture for big data Intelligent real-time applications Cloud infrastructure Machine learning: models and training Deep learning and artificial intelligence

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data.

Data-driven technologies are now being adopted, developed, funded, and deployed throughout the health care market at an unprecedented scale. But, as this O'Reilly report reveals, health care innovation contains more hurdles and requires more finesse than many tech startups expect. By paying attention to the lessons from the report's findings, innovation teams can better anticipate what they'll face, and plan accordingly. Simply put, teams looking to apply collective intelligence and "big data" platforms to health and health care problems often don't appreciate the messy details of using and making sense of data in the heavily regulated hospital IT environment. Download this report today and learn how it helps prepare startups in six areas: Complexity: An enormous domain with noisy data not designed for machine consumption Computing: Lack of standard, interoperable schema for documenting human health in a digital format Context: Lack of critical contextual metadata for interpreting health data Culture: Startup difficulties in hospital ecosystems: why innovation can be a two-edged sword Contracts: Navigating the IRB, HIPAA, and EULA frameworks Commerce: The problem of how digital health startups get paid This report represents the initial findings of a study funded by a grant from the Robert Wood Johnson Foundation. Subsequent reports will explore the results of three deep-dive projects the team pursued during the study.

In the four years that O'Reilly Media, Inc. has produced its annual Big Data Now report, the data field has grown from infancy into young adulthood. Data is now a leader in some fields and a driver of innovation in others, and companies that use data and analytics to drive decision-making are outperforming their peers. And while access to big data tools and techniques once required significant expertise, today many tools have improved and communities have formed to share best practices. Companies have also started to emphasize the importance of processes, culture, and people. The topics in represent the major forces currently shaping the data world: Big Data Now: 2014 Edition Cognitive augmentation: predictive APIs, graph analytics, and Network Science dashboards Intelligence matters: defining AI, modeling intelligence, deep learning, and "summoning the demon" Cheap sensors, fast networks, and distributed computing: stream processing, hardware data flows, and computing at the edge Data (science) pipelines: broadening the coverage of analytic pipelines with specialized tools Evolving marketplace of big data components: SSDs, Hadoop 2, Spark; and why datacenters need operating systems Design and social science: human-centered design, wearables and real-time communications, and wearable etiquette Building a data culture: moving from prediction to real-time adaptation; and why you need to become a data skeptic Perils of big data: data redlining, intrusive data analysis, and the state of big data ethics