In the last five years, the amount of data in the world has grown exponentially. Domo’s ‘Data Never Sleeps 5.0 and 6.0’ suggest that 90% of world’s data was created between 2016 and 2017, and the numbers have not slowed down since then. Smartphone applications are now dominating customers’ mindshare and Generation Z (those born from the mid-1990s to early 2000s) has not seen a world without computers and phones. As these younger consumers are added to the traveler mix, travel companies are now feeling the pressure to reimagine themselves in order to stay relevant amidst changing technology preferences. Travel company executives are asking: What are the key ingredients for reimagining personalization in travel?

Drug Hub: Sophisticated Machine Learning Algorithms to Address Pharmaceutical Needs

The process of discovering and designing new drugs is complex, expensive, and time-consuming. Developing a new drug is a multistage process that takes fifteen years on average. Different drugs may have their own unique requirements, but the overarching process consists of: discovery, preclinical testing, clinical trials, review, and approval.

A few months ago, Scientific American published a list of what they consider to be the "Top 10 Emerging Technologies of 2018". Two different applications of AI technology ranked highly on this list.

Now that 2019 is in full swing, we wanted to pause and reflect on those AI applications... Where were we in 2018, and where will we go in 2019?

Check back for our follow-up post tomorrow! You'll find out how Opera Solutions is answering those questions and how we're expanding the research on the emerging AI technologies.

An Unconventional Business Model

The business of healthcare is unlike any other business. The primary objective of a health system is to provide best in class services, rather than to maximize profits. Most people would agree that this is a good thing. It’s also a rare thing in the competitive world of business. The admirable commitment to “patients before profits” puts every health system’s cash flow at constant risk. A health system has more control when it comes to providing services, but relatively little control over collecting payments for these services. Inefficiencies in collection processes and missed charges result in significant lower revenues and directly impact the bottom line.

How Do You Solve Box Office Scheduling? Watch a movie.

For every customer you have, there are dozens more with similar attributes waiting to hear from you.

Michael Jahrer, VP of applied machine learning at Opera Solutions, proves his data science mettle by using deep learning to predict who will be a safe driver in the year ahead. 

Porto Seguro, the third-largest insurance company in Brazil, set out to improve its predictions of who would file an insurance claim in the next year. The company sponsored a competition through Kaggle, the premier platform for predictive modeling and analytics competitions, and our own Michael Jahrer, VP of applied machine learning, took home the win. 

Online shoppers are getting antsy. They’re spending less time thinking about purchases before buying — or leaving a site — and drastically shrinking marketers’ opportunity time. We explore the latest trends in online personalization — and how to keep up.

We spend a lot of time talking about personalization in marketing. That’s because it’s both the most profitable way for our customers to leverage artificial intelligence and the most challenging. A new paper by Seth Earley, published in IEEE’s November/December issue of IT Professional, lays out some of these challenges and addresses how best to overcome them.“AI-Driven Analytics at Scale: The Personalization Problem” touches on many of the issues our customers face every day, including maximizing data scientists’ time, developing use cases in a timely fashion, and even using a Signal Layer to help expedite the process. It also cites a new study that examines exactly how much time companies must apply their personalization insights in a real-time setting.

Managers and executives at all levels are now expected to be at least familiar with how machine learning models are built and deployed. However, if you don’t have a formal data science education, reading through industry publications is not very helpful: High-level use case descriptions and marketing materials too often present machine learning as somewhat of a dark magic powering their products; technical publications tend to be incomprehensible for a nonspecialist, and how-to guides simply list the steps without giving sufficient background as to why each step is needed, which limits understanding.