Over the last two decades, the U.S. has been plagued with high rates of prescription drug overdose. About 142 American citizens die every day from drug overdoses, many of which are caused by prescription medications obtained legally. Additionally, the number of opioids prescribed as of 2015 is three times as high as the number of prescriptions given out in 1999. Due to these staggering statistics, doctors and data scientists are working towards using predictive analytics to reduce the number of prescription drug overdoses.
The travel industry is one of the most technologically advanced markets in the world, adopting big data and predictive analytics (PA) when the tech was still in its early days. The highly competitive nature of the industry has prompted airlines and hotels to develop apps that leverage this cutting-edge technology in order to satisfy and retain customers.
Predictive analytics and big data are two forms of cutting-edge technology that are commonly integrated into apps and software, but many individuals are confused when it comes to how these two technologies compare. In fact, many are unsure which data-handling technology is best for their development project, which can make hiring the right developer a challenge.
Predictive analytics (PA) is transforming virtually every industry, from sports and banking to healthcare. This technology is essential in advancing the healthcare industry, as it offers a full-fledged idea of what a patient’s medical history looks like and compares it to data from patients with similar histories.
Machine learning algorithms have gone a long way toward transforming our digital experience, whether we’re online, using a mobile app, interacting with a piece of desktop software or leveraging the tools within an enterprise app. And while few people really appreciate the machine learning algorithm—or even realize it exists—it’s a remarkable piece of technology that is unique in terms of its adaptation capabilities and overall efficiency. As more and more mobile apps are built each month, an increasing number of developers are leveraging the many advantages of machine learning algorithms. But what are these benefits and how do they affect your mobile app, its budget and its overall capabilities?
Predictive analytics (PA) have lots of different applications, from weather forecasting to contextual advertising, business projections and more. But there’s one area where this technology really shines, and that is the realm of ecommerce. By using predictive analytics for ecommerce, you’ll have the ability to anticipate a shopper’s wants and needs, increasing sales by a significant margin in many cases. So what are the best strategies for using predictive analytics for ecommerce? And how do you implement this technology with your existing virtual storefront?
In nearly all areas of life, we consider the facts and probabilities before making a decision. This is true whether you’re making important investment choices or simply wondering whether to park on-street in a metered space or inside a parking garage. So it seems rather illogical—and even downright reckless—to make critical business decisions without a probable and factual basis. Yet company leaders make such determinations every day, with some choices carrying a multi-million dollar impact.
Over the past decade, predictive analytics technology has been introduced into the mainstream. This application of predictive analytics has some useful features, including eBook recommendations based on your reading history and cutting-edge matchmaking systems that leverage societal trends and specific user information to identify potential soulmates. And while the end user may not realize a particular mobile app feature is powered by a predictive analytics engine, this technology’s impact can go a long way toward helping companies achieve their goals and guiding customers to products and solutions that actually interest them.
Predictive analytics (PA) are becoming more and more mainstream. According to an October 2017 projection from Stratistics MRC, the worldwide predictive analytics marketplace is expected to grow from $3.89 billion in 2016 to $14.95 billion in 2023. PA technology is already a noticeable part of everyday life, from product recommendations on Amazon to the search queries autofilled on Google and the advertisements you see while surfing the web.