Marketing automation's time has finally come, but it's been a long, tortuous road.

I’m a big fan of marketing automation. My experience goes back a few years. I was just getting settled at a new company when our CFO received a renewal invoice from a marketing automation vendor. She didn’t know how we were using the software, and neither did I. 

The Hadoop Summit conference, hosted by Hortonworks and Yahoo, has become a must-see Big Data event. The Hadoop distributed computing architecture is now an integral part of what it means to be a data scientist, and a few days of concentrated effort each year is enough to get a vision for where the industry is headed. The Hadoop Summit serves this purpose well by providing thought-provoking technical sessions, keynote addresses, and a vendor exhibition that brings many of the major players in the Hadoop ecosystem together under one roof.

Edited by Yan Zhang

Many pundits speaking about the state of the nation’s healthcare infrastructure routinely point to fraud, waste, and abuse (FWA) as major reasons for many of the problems the public witnesses every day – increased healthcare costs and the resulting increase in insurance premiums. The net effect is an annual loss of billions of dollars, and these losses affect the public in very real ways. 

The size of the healthcare sector, the enormous amount of money involved, and the lack of surveillance and monitoring mechanisms across the healthcare ecosystem make it an attractive target for FWA. According to the Office of Management and Budget, in 2010, about 9%, or $47.9 billion was lost to fraud in Medicare alone. It is therefore imperative to develop effective FWA technologies and solutions for reducing the costs associated with our healthcare system.

Data science and its primary enabling methodology of machine learning represent the country’s best chance for detecting FWA to avoid extraordinary sources of loss. Data science possesses the facilities to make a significant difference healthcare industry budgets and their impact to the public. Opera Solutions is the industry leader in applying Big Data technologies to the most challenging and significant business problems. We are the company charged with developing the analytics to identify fraud for the Centers for Medicare & Medicaid Services (CMS) on the health insurance exchanges. Here’s a look into just how big this challenge is — and some of the approaches we’re taking to overcome one of the costliest burdens in America.

As a relatively new term, “data science” can mean different things to different people due in part to all the hype surrounding the field. Often used in the same breath, we also hear a lot about “big data” and how it is changing the way that companies interact with their customers. This begs the question — how are these two technologies related? Unfortunately, the hype can cloud our understanding for how these technologies are working to shape our increasingly data-driven society. Rest assured, there truly is something deep and profound representing a paradigm shift in our society surrounding data, but the hype isn’t helping to clarify data science’s exact role in Big Data. In this article, we strive to put to rest many of the misunderstandings surrounding data science.

Switching to a new medical coding system won’t be easy, but when combined with data science and machine learning, ICD-10 presents enormous potential benefits for both the financial and the clinical sides of healthcare.

Part of why the healthcare industry is such a notorious laggard in jumping on the Big Data bandwagon is that every attempted change faces a huge domino effect, rendering many good ideas useless until everyone — and everything — is ready. One big step in the right direction, however, is an important upgrade to the computerized codes used for electronic medical records (EMR), which will take hold in the next year or two. These codes, known as ICD or International Classification of Diseases, determine what ailments patients have and how much they and their insurers should pay for a treatment. The set of codes, currently called ICD-9, is scheduled for its 10th revision this fall (but there may be a year-long delay). The updated codes, called ICD-10 codes, improve on the previous standard by adding more descriptive capabilities that will help healthcare professionals better categorize and keep track of patient disorders and treatments. Through the use of machine learning and other data science techniques, this increased granularity is expected to open up patient treatment analytics along with the ability to better monitor public health threats.

You may not be paying much attention to data science, but it’s paying attention to you — and will deliver personalized search results in due time.

For those of us who prefer to stay on the shopping side of recommender engines, how online retailers seem to know which hotels we’ll book, what flights we’ll take, or what brand of pots and pans we’ll buy next is simply math, or for those of us with a little more knowledge, algorithms. For most everyone else, it pretty much falls under something more akin to science so advanced it might as well be magic. Of course, for the machine learning data scientists who create these recommender engines, all engines are different, complex — and definitely not magic.

Think you know English? Think again. See if you have what it takes to teach a computer how to understand humans.

Anyone who has tried to learn English as a second language is only too familiar with its many — many — challenges. In addition to idioms, sarcasm, and a wide array of meanings when combined with various prepositions (think: make up, make out, make it, and of course, makeup), there’s also pop culture, trends, products, and more to keep straight. Luckily for us, we’re human, and even those well established in their native languages will be able to speak and decipher English with enough practice and exposure. But what about machines? How do we even begin to program them in a way that they can read and understand sentiment? Answer: very carefully. The process requires machine learning data scientists to use Natural Language Process (NLP) techniques, a form of advanced analytics. They use these techniques to build models that can decipher sentiment and weed out the meaningful information among the noise. 

A new report, featured in The New York Times, highlights the benefits of Big Data and machine learning in the airline industry and beyond.

The travel industry has always been a vast data collector. Every airline reservation, every hotel booking, every car rental ends up in a conventional database of structured data. But today Big Data — the unstructured data that includes ratings on blog sites, likes on social media, conversations with call centers, customer clickstreams, and more — is becoming increasingly important in determining how travel companies keep customers coming back.

Opera Solutions’ Arnab Gupta explores the vast opportunities in Big Data on CNBC’s “Squawk Box.”

"The Big Data phenomenon is so vast, it can support 50 IBMs."

That was just one of the thought-provoking statements Opera Solutions’ CEO, Arnab Gupta, made during his appearance on CNBC’s “Squawk Box” as he spoke with hosts Andrew Ross Sorkin, Becky Quick, and Joe Kernen on April 24^th.