Data-driven techniques have improved decision-making processes for people in industries such as finance and real estate. Yet, despite promising solutions that data analytics and artificial intelligence/machine learning (ML) tools can bring to healthcare, the industry remains largely unconvinced. In this O’Reilly report, you’ll explore the potential of—and impediments to—widespread adoption of AI and ML in the medical field. You’ll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare. Through interviews with several professionals working at the intersection of medicine and data science, author Mike Barlow examines five areas where the application of AI/ML strategies can spur a beneficial revolution in healthcare: Identifying risks and interventions for healthcare management of entire populations Closing gaps in care by designing plans for individual patients Supporting customized self-care treatment plans and monitoring patient health in real time Optimizing healthcare processes through data analysis to improve care and reduce costs Helping doctors and patients choose proper medications, dosages, and promising surgical options

Consider this scenario: You walk into a building and a sensor identifies you through your mobile phone. You then receive a welcoming text telling you when lunch will be served, or perhaps a health warning based on allergy information you’ve stored in your profile. Maybe you’ll be flagged as a security threat. How is that possible? This O’Reilly report explores ambient computing—hands-free, 24/7 wireless connectivity to hardware, data, and IT systems. Enabling that scenario requires a lot of work behind the scenes to determine network connectivity, device security, and personal privacy. With an ambient-computing technology stack already in the works, resolving those issues is only a matter of time. Through interviews with front-line tech pioneers—including Ari Gesher (Kairos Aerospace) and Matthew Gast (Aerohive Networks)—author Mike Barlow explores how real-time analytics can enable real-time decision making. How will simple beacons broadcast information to your phone as you pass businesses on your morning walk? How can emotional speech analysis monitor the emotional state of employees, students, or people in crowds? Pick up this report and find out.

Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you’ll find out how far data science reaches. With this anthology, you’ll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.

Data may indeed be the "new oil"—a seemingly inexhaustible source of fuel for spectacular economic growth—but it's also a valuable resource for humanitarian groups looking to improve and protect the lives of less fortunate people. In this O'Reilly report, you'll learn how statisticians and data scientists are volunteering their time to help a variety of nonprofit organizations around the world. Mike Barlow cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. There's certainly no lack of demand for data science services among nonprofits today, because these organizations, too, realize the potential of data for changing people's fortunes.

Here's the net takeaway: Businesses want insights from data they can translate into meaningful actions and real results. Software vendors are beginning to deliver a new generation of advanced analytics packages that address business issues directly. In this O'Reilly report, Mike Barlow reveals how this new user-friendly software is helping businesses go beyond data analysis and straight to decision-making—without requiring data science expertise or truckloads of cash. How has advanced analytics progressed from lab project to commercial product so quickly? Through interviews with data analysts, you'll understand the role that machine learning plays in specialized analytics packages, and how this software alone can make decisions based on what's likely to happen next. When you have these capabilities, you’ve reached "the last mile of analytics."

Technology does not exist in a vacuum. In the same way that a plant needs water and nourishment to grow, technology needs people and process to thrive and succeed. Culture (i.e., people and process) is integral and critical to the success of any new technology deployment or implementation. Big data is not just a technology phenomenon. It has a cultural dimension. It's vitally important to remember that most people have not considered the immense difference between a world seen through the lens of a traditional relational database system and a world seen through the lens of a Hadoop Distributed File System.This paper broadly describes the cultural challenges that accompany efforts to create and sustain big data initiatives in an evolving world whose data management processes are rooted firmly in traditional data warehouse architectures.

Five or six years ago, analysts working with big datasets made queries and got the results back overnight. The data world was revolutionized a few years ago when Hadoop and other tools made it possible to getthe results from queries in minutes. But the revolution continues. Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.